平坦衰落信道下基于变分贝叶斯的多天线信号联合符号检测算法

该文针对平坦衰落信道下存在信道参数差异的多天线接收信号联合参数估计和符号检测问题,提出一种基于变分贝叶斯的联合处理算法。算法直接利用多个接收数据流进行信息符号的估计,抑制传统信号合成与解调解耦处理带来的性能损失。将问题建模为已知多组观测数据条件下发送符号、信道传输时延、信道增益和噪声功率的联合最大后验估计问题。基于变分贝叶斯理论对该最大后验进行近似求解,在相对熵最小化的准则下,推导得到了各个待估参数解析形式的近似后验分布——变分分布。所提算法无需计算各参数精确的点估计值,而是采用信道参数和信息符号变分分布迭代处理的方式进行联合求解。仿真结果表明,所提算法通过多信号、多参数的联合处理能够获得优于经典解耦处理和部分联合处理技术的系统误码率性能,且在接收天线数目较多和观测数据长度较短时性能优势体现更加明显。     

Total variation blind deconvolution

We present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed by Acar and Vogel (1994). The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images as well as some blurring functions, e.g., motion blur and out-of-focus blur. An alternating minimization (AM) implicit iterative scheme is devised to recover the image and simultaneously identify the point spread function (PSF). Numerical results indicate that the iterative scheme is quite robust, converges very fast (especially for discontinuous blur), and both the image and the PSF can be recovered under the presence of high noise level. Finally, we remark that PSFs without sharp edges, e.g., Gaussian blur, can also be identified through the TV approach.     

Two Methods for Optimal MECG Elimination and FECG Detection from Skin Electrode Signals

Two signal processing techniques for the suppression of the maternal ECG and simultaneously optimal detection of fetal ECG with respect to noise are presented. Both techniques are based on the singular value decomposition of a measurement matrix. Criteria are given in order to evaluate, a priori, electrode locations and sampling schemes for both methods. A fundamental difference with other methods is that the number of linearly independent FECG signals is not constrained to one. One of the presented techniques is a typical offline method. It is well suited for a large number of electrodes and large number of samples, which results in a better signal to noise ratio. The second technique is a typical on-line method. It gives fetal ECG signals within about 1 s, and is adaptive to changes of the transfer (e.g., due to fetal movement). It can be applied with a small number of electrodes (e.g., eight). It is shown that if three of these signals are from thoracic electrodes, the MECG suppression is guaranteed.     

Bit error probability reduction in direct detection opticalreceivers using RZ coding

We analyze the bit error probability reduction for direct detection ON-OFF keying optical receivers using return-to-zero (RZ) coding instead of the nonreturn-to-zero (NRZ) format. For the same average optical power, RZ is shown to outperform NRZ, even when employing the same receiver bandwidth. Results are given for receivers whose noise variance is i) dominated by a signal-independent term (e.g., simple pin diode receivers), ii) dominated by a signal-dependent term (e.g., optically preamplified receivers), and iii) made up of two equally important contributions [e,g,, avalanche photodiode (APD) receivers]. Based on semianalytic simulations including intersymbol interference, we show that the achievable RZ sensitivity gain is typically less for dominating signal-independent noise than for dominating signal-dependent noise, where it amounts to about 3 dB. We also quantitatively discuss the influence of the optical pulse shape on the achievable RZ coding gain, and show that finite extinction ratios can significantly reduce that gain, especially when the RZ signals are produced by direct-modulation methods     

Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation

A new method for QRST cancellation is presented for the analysis of atrial fibrillation in the surface electrocardiogram (ECG). The method is based on a spatiotemporal signal model which accounts for dynamic changes in QRS morphology caused, e.g., by variations in the electrical axis of the heart. Using simulated atrial fibrillation signals added to normal ECGs, the results show that the spatiotemporal method performs considerably better than does straightforward average beat subtraction (ABS). In comparison to the ABS method, the average QRST-related error was reduced to 58 percent. The results obtained from ECGs with atrial fibrillation agreed very well with those from simulated fibrillation signals.     

A subspace-based direction finding algorithm using fractional lowerorder statistics

We propose several classes of fractional lower order moment (FLOM)-based matrices that can be used with MUSIC to estimate the DOAs of independent circular signals embedded in additive SαS (symmetric α stable) noise (e.g., sea clutter). We run simulations with different choices of the FLOM parameter p for our FLOM-based matrices and conclude that when the noise is SαS with unknown α≠2, FLOM-multiple signal classification (MUSIC) with p close to unity yields good performance. The performance of FLOM-MUSIC and robust covariation-based (ROC)-MUSIC are similar. Three scenarios that contain circular signals (phase modulation (PM), circularly symmetrical Gaussian, and quaternary phase-shift keying (QPSK)) and one scenario that contains noncircular signals (binary phase-shift keying (BPSK)), all embedded in the same SαS noise, are tested. These simulation results reveal that the scenario containing BPSK signals leads to poor performance, indicating that FLOM-MUSIC is presently limited to circular signals     

Robust statistical label fusion through COnsensus Level, Labeler Accuracy, and Truth Estimation (COLLATE)

Segmentation and delineation of structures of interest in medical images is paramount to quantifying and characterizing structural, morphological, and functional correlations with clinically relevant conditions. The established gold standard for performing segmentation has been manual voxel-by-voxel labeling by a neuroanatomist expert. This process can be extremely time consuming, resource intensive and fraught with high inter-observer variability. Hence, studies involving characterizations of novel structures or appearances have been limited in scope (numbers of subjects), scale (extent of regions assessed), and statistical power. Statistical methods to fuse data sets from several different sources (e.g., multiple human observers) have been proposed to simultaneously estimate both rater performance and the ground truth labels. However, with empirical datasets, statistical fusion has been observed to result in visually inconsistent findings. So, despite the ease and elegance of a statistical approach, single observers and/or direct voting are often used in practice. Hence, rater performance is not systematically quantified and exploited during label estimation. To date, statistical fusion methods have relied on characterizations of rater performance that do not intrinsically include spatially varying models of rater performance. Herein, we present a novel, robust statistical label fusion algorithm to estimate and account for spatially varying performance. This algorithm, COnsensus Level, Labeler Accuracy and Truth Estimation (COLLATE), is based on the simple idea that some regions of an image are difficult to label (e.g., confusion regions: boundaries or low contrast areas) while other regions are intrinsically obvious (e.g., consensus regions: centers of large regions or high contrast edges). Unlike its predecessors, COLLATE estimates the consensus level of each voxel and estimates differing models of observer behavior in each region. We show that COLLATE provides significant improvement in label accuracy and rater assessment over previous fusion methods in both simulated and empirical datasets.     

Noise Effects in Time-Domain Systems Involving Three-Axial Field Probes for the Measurement of Nonstationary Radiated Emissions

This paper deals with three-axial systems for time-domain measurement of radiated emissions. A statistical analysis is developed in order to assess the influence of noise on the system measurement uncertainty in the case of nonstationary emissions, for which averaging techniques cannot be exploited. For a vector field with spatial components consisting of multiple sinusoids in additive white or colored noise, the modifications in the statistics of the measured signals due to application of the discrete Fourier transform to the digitized time series are investigated. The analysis is carried out in the frequency domain, and is used for probabilistic detection and maximum likelihood estimation of the amplitude of spectral lines close to the noise floor (i.e., in the typical conditions imposed by short-time measurement windows due to signal nonstationarity). Results are extended to sum, sum of squares, and logarithm of the amplitude of the spectral lines. As an application example, statistical estimates are derived for the safety factor used to assess compliance with the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines, in case of nonionizing radiation exposure to multiple frequency magnetic fields. The theoretical work is validated both versus test signals with known statistical properties and against measurement data obtained with a time-domain system for magnetic field measurement in the bandwidth 100 kHz–1.5 GHz.      

Density evolution for asymmetric memoryless channels

Density evolution (DE) is one of the most powerful analytical tools for low-density parity-check (LDPC) codes and graph codes with message passing decoding algorithms. With channel symmetry as one of its fundamental assumptions, density evolution has been widely and successfully applied to different channels, including binary erasure channels (BECs), binary symmetric channels (BSCs), binary additive white Gaussian noise (BiAWGN) channels, etc. This paper generalizes density evolution for asymmetric memoryless channels, which in turn broadens the applications to general memoryless channels, e.g., z-channels, composite white Gaussian noise channels, etc. The central theorem underpinning this generalization is the convergence to perfect projection for any fixed-size supporting tree. A new iterative formula of the same complexity is then presented and the necessary theorems for the performance concentration theorems are developed. Several properties of the new density evolution method are explored, including stability results for general asymmetric memoryless channels. Simulations, code optimizations, and possible new applications suggested by this new density evolution method are also provided. This result is also used to prove the typicality of linear LDPC codes among the coset code ensemble when the minimum check node degree is sufficiently large. It is shown that the convergence to perfect projection is essential to the belief propagation (BP) algorithm even when only symmetric channels are considered. Hence, the proof of the convergence to perfect projection serves also as a completion of the theory of classical density evolution for symmetric memoryless channels.     

A class of Monte-Carlo-based statistical algorithms for efficient detection of repolarization alternans

Cardiac repolarization alternans is an electrophysiologic condition identified by a beat-to-beat fluctuation in action potential waveform. It has been mechanistically linked to instances of T-wave alternans, a clinically defined ECG alternation in T-wave morphology, and associated with the onset of cardiac reentry and sudden cardiac death. Many alternans detection algorithms have been proposed in the past, but the majority have been designed specifically for use with T-wave alternans. Action potential duration (APD) signals obtained from experiments (especially those derived from optical mapping) possess unique characteristics, which requires the development and use of a more appropriate alternans detection method. In this paper, we present a new class of algorithms, based on the Monte Carlo method, for the detection and quantitative measurement of alternans. Specifically, we derive a set of algorithms (one an analytical and more efficient version of the other) and compare its performance with the standard spectral method and the generalized likelihood ratio test algorithm using synthetic APD sequences and optical mapping data obtained from an alternans control experiment. We demonstrate the benefits of the new algorithm in the presence of Gaussian and Laplacian noise and frame-shift errors. The proposed algorithms are well suited for experimental applications, and furthermore, have low complexity and are implementable using fixed-point arithmetic, enabling potential use with implantable cardiac devices.     

Robust statistical fusion of image labels

Image labeling and parcellation (i.e., assigning structure to a collection of voxels) are critical tasks for the assessment of volumetric and morphometric features in medical imaging data. The process of image labeling is inherently error prone as images are corrupted by noise and artifacts. Even expert interpretations are subject to subjectivity and the precision of the individual raters. Hence, all labels must be considered imperfect with some degree of inherent variability. One may seek multiple independent assessments to both reduce this variability and quantify the degree of uncertainty. Existing techniques have exploited maximum a posteriori statistics to combine data from multiple raters and simultaneously estimate rater reliabilities. Although quite successful, wide-scale application has been hampered by unstable estimation with practical datasets, for example, with label sets with small or thin objects to be labeled or with partial or limited datasets. As well, these approaches have required each rater to generate a complete dataset, which is often impossible given both human foibles and the typical turnover rate of raters in a research or clinical environment. Herein, we propose a robust approach to improve estimation performance with small anatomical structures, allow for missing data, account for repeated label sets, and utilize training/catch trial data. With this approach, numerous raters can label small, overlapping portions of a large dataset, and rater heterogeneity can be robustly controlled while simultaneously estimating a single, reliable label set and characterizing uncertainty. The proposed approach enables many individuals to collaborate in the construction of large datasets for labeling tasks (e.g., human parallel processing) and reduces the otherwise detrimental impact of rater unavailability.     

Two-Parameter Discontinuity-Induced Bifurcation Curves in a ZAD-Strategy-Controlled DC–DC Buck Converter

The dynamics of a zero-average dynamic strategy controlled dc–dc Buck converter, modelled by a set of differential equations with discontinuous right-hand side is studied. Period-doubling and corner-collision bifurcations are found to occur close to each other under small parameter variations. Closer examination of the parameter space leads to the discovery of a novel bifurcation. This type of bifurcation has not been reported so far in the literature and it corresponds to a corner-collision bifurcation of a nonhyperbolic cycle. The bifurcation boundaries are computed analytically in this paper and the system dynamics are unfolded close to the novel bifurcation point.This paper was completed during a research period of all the authors at the CRM in Barcelona in March 2007.      

基于WHI及ICP的点云配准算法

针对两步点云配准中精度差、计算效率低、易受噪声干扰的问题,提出一种基于WHI特征描述符结合改进的ICP点云配准算法。首先,对大数据量的点云通过ISS算法提取特征点集作为配准点云;然后,计算特征点云的WHI特征描述符,利用随机采样一致性算法完成粗配准;最后,基于安德森加速迭代ICP算法对粗配准点云进行精确配准。通过多组点云数据集对所提算法进行验证,实验表明,该算法配准精度高、速度快,在含有噪声数据集的优势更明显。在不同的点云模型下,所提算法的配准效率提高2倍以上,在噪声环境下具有一定的鲁棒性。     

A mathematical model for source separation of MMG signals recorded with a coupled microphone-accelerometer sensor pair

Recent advances in sensor technology for muscle activity monitoring have resulted in the development of a coupled microphone-accelerometer sensor pair for physiological acousti signal recording. This sensor can be used to eliminate interfering sources in practical settings where the contamination of an acoustic signal by ambient noise confounds detection but cannot be easily removed [e.g., mechanomyography (MMG), swallowing sounds, respiration, and heart sounds]. This paper presents a mathematical model for the coupled microphone-accelerometer vibration sensor pair, specifically applied to muscle activity monitoring (i.e., MMG) and noise discrimination in externally powered prostheses for below-elbow amputees. While the model provides a simple and reliable source separation technique for MMG signals, it can also be easily adapted to other aplications where the recording of low-frequency (< 1 kHz) physiological vibration signals is required.     

Extrapolation of signals using the method of alternating projections in fractional Fourier domains

The need for extrapolation of signals in time domain or frequency domain often arises in many applications in the area of signal and image processing. One of the approaches used for the extrapolation of the signals is the method of alternating projections (MAP) in conventional Fourier domains (CFD). Here we propose an extension of this approach using the fractional Fourier transform (FRFT) called here as the method of alternating projections in the FRFT domains (MAPFD). It is shown through the simulation results that the mean square error (MSE) between the true signal and the extrapolated signal obtained from the given signal is a function of the angle parameter of the FRFT, and the MAPFD gives lower MSE than the MAP in the CFD for the class of signals bandlimited in the FRFT domains, e.g., chirp signals. Moreover, the performance of the extrapolation using the MAPFD is shown to be shift-variant along the time axis.     

基于光谱加权低秩矩阵分解的高光谱影像去噪方法

高光谱遥感影像在获取和传输过程中会受到各种类型噪声的污染,不仅降低影像质量,也限制了其后续应用的精度。高光谱影像噪声类型复杂多样,且噪声在不同波段上的强度也并不相同。通过引入光谱域上的权重矩阵,文中提出了一种基于光谱加权低秩矩阵分解的高光谱遥感影像混合噪声去除方法,利用光谱权重矩阵均衡不同波段的噪声强度差异性。为进一步将噪声与纯净影像分离,利用加权核范数最小化来约束纯净高光谱影像的局部低秩结构,并利用交替方向乘子法对所提出的模型进行优化求解。通过对模拟与真实高光谱遥感数据的实验,验证了所提方法的有效性与优越性。