Signal modeling classes for linearized OFDM SSPA behavioral analysis

This paper presents a comparison of four signal representation philosophies, denoted direct time domain, (DTD), mixed frequency and time domain (MFTD), statistical (Stat) and a newly proposed hybrid MFTD-statistical (MFTD-S) for the behavioral analysis of an orthogonal frequency division multiplexing (OFDM) signal through a solid state power amplifier (SSPA) with five degrees of linearization applied. The potential of the Stat and MFTD-S approaches for behavioral analysis of multicarrier, multiband and ultra wideband systems is illustrated. Also and for the first time, the impairment amelioration obtained through the introduction of different degrees of linearization in OFDM based systems is quantified, together with an upper bound to potential linearization improvement: linearization benefit is poor unless the narrow area of benefit can be matched to the raised-sine shape of the SSPA energy efficiency curve.     

Signal detection using third-order moments

The noise suppression capability of higher-order moments and spectra has made them attractive when the goal is to extract or reconstruct a signal that is contaminated by multivariate Gaussian noise or certain types of non-Gaussian noise. Two new detectors, one centralized and one distributed, which are based on the third-order moment of the data are proposed. The asymptotic performance of the centralized detector and the asymptotic distribution of the components of the distributed detector are analyzed. Further, the performance of these detectors is simulated and compared to that of the matched filter for three different types of interference: Gaussian noise, Gaussian noise corrupted by a sinusoid with random phase, and Arctic under-ice noise.     

Three-dimensional building detection and modeling using astatistical approach

In this paper, we address the problem of building reconstruction in high-resolution stereoscopic aerial imagery. We present a hierarchical strategy to detect and model buildings in urban sites, based on a global focusing process, followed by a local modeling. During the first step, we extract the building regions by exploiting to the full extent the depth information obtained with a new adaptive correlation stereo matching. In the modeling step, we propose a statistical approach, which is competitive to the sequential methods using segmentation and modeling. This parametric method is based on a multiplane model of the data, interpreted as a mixture model. From a Bayesian point of view the so-called augmentation of the model with indicator variables allows using stochastic algorithms to achieve both model parameter estimation and plane segmentation. We then report a Monte Carlo study of the performance of the stochastic algorithm on synthetic data, before displaying results on real data.     

用PSoC混合信号阵列实现频率检测

人们知道很多用模拟电路及模拟元件实现频率检测的方法。但是,这些附加的元件不仅占用额外的电路板空间,增加成本,而且还可能带来老化和漂移问题。相反,数字信号处理器(DSP)提供了一种在数字域内实现频率检测的方法。它没有纯模拟电路的问题。不过,DSP的成本有时对特定产品而言太高。这时,用小型低成本微控制器实现可靠的频率检测的方法就很有优势。现在有几种实现频率检测的方法。其中之一广为人知,也许已经用在了你的设计方案中。本文中叙述的其他方法则具有一些有趣的新思路,它们利用了市场上已有的新型微控制器的混合信号能力。用计…     

Motion detection in moving camera videos using background modeling and FlowNet

Real-time moving object detection is challenging for moving cameras due to the moving background. Many studies use homography matrix to compensate for global motion by warping the background model to the current frame. Then, the pixel difference between the current frame and the background model is used for background subtraction. Moving pixels are extracted by applying adaptive threshold and some post-processing techniques. On the other hand, deep learning-based dense optical flow can be efficient enough to extract the moving pixels, but it increases computational cost. This study proposes a method to enhance a classical background modeling method with deep learning-based dense optical flow. The main contribution of this paper is to propose a fusing algorithm for dense optical flow and background modeling approach. The background modeling methods are error-prone, especially with continuous camera movement, while the optical flow method alone may not always be efficient. Our hybrid method fuses both techniques to improve the detection accuracy. We propose a software architecture to run background modeling and dense optical flow methods in parallel processes. The proposed implementation approach significantly increases the method’s working speed, while the proposed fusion and combining strategy improve detection results. The experimental results show that the proposed method can run at high speed and has satisfying performance against the methods in the literature.     

Efficient object detection using convolutional neural network-based hierarchical feature modeling

A hierarchical data-driven object detection framework is addressed considering a deep feature hierarchy of object appearances. The performance of many object detectors is degraded due to ambiguities in inter-class appearances and variations in intra-class appearances, but deep features extracted from visual objects show a strong hierarchical clustering property. Deep features were partitioned into unsupervised super-categories at the inter-class level, and augmented categories at the object level, to discover deep feature-driven information. A hierarchical feature model is built using a latent topic model algorithm, assembling a one-versus-all support vector machine at each node to constitute a hierarchical classification ensemble. Extensive experiments show that the proposed method is superior to state-of-the-art techniques using the PASCAL VOC 2007 and VOC 2012 datasets.     

Activation detection in functional MRI using subspace modeling and maximum likelihood estimation

A statistical method for detecting activated pixels in functional MRI (fMIRI) data is presented. In this method, the fMRI time series measured at each pixel is modeled as the sum of a response signal which arises due to the experimentally controlled activation-baseline pattern, a nuisance component representing effects of no interest, and Gaussian white noise. For periodic activation-baseline patterns, the response signal is modeled by a truncated Fourier series with a known fundamental frequency but unknown Fourier coefficients. The nuisance subspace is assumed to be unknown. A maximum likelihood estimate is derived for the component of the nuisance subspace which is orthogonal to the response signal subspace. An estimate for the order of the nuisance subspace is obtained from an information theoretic criterion. A statistical test is derived and shown to be the uniformly most powerful (UMP) test invariant to a group of transformations which are natural to the hypothesis testing problem. The maximal invariant statistic used in this test has an F distribution. The theoretical F distribution under the null hypothesis strongly concurred with the experimental frequency distribution obtained by performing null experiments in which the subjects did not perform any activation task. Application of the theory to motor activation and visual stimulation fMRI studies is presented.     

Signal modeling techniques in speech recognition

A tutorial on signal processing in state-of-the-art speech recognition systems is presented, reviewing those techniques most commonly used. The four basic operations of signal modeling, i.e. spectral shaping, spectral analysis, parametric transformation, and statistical modeling, are discussed. Three important trends that have developed in the last five years in speech recognition are examined. First, heterogeneous parameter sets that mix absolute spectral information with dynamic, or time-derivative, spectral information, have become common. Second, similarity transform techniques, often used to normalize and decorrelate parameters in some computationally inexpensive way, have become popular. Third, the signal parameter estimation problem has merged with the speech recognition process so that more sophisticated statistical models of the signal's spectrum can be estimated in a closed-loop manner. The signal processing components of these algorithms are reviewed     

Moving cast shadow detection using online sub-scene shadow modeling and object inner-edges analysis

In this paper, we propose an adaptive and accurate moving cast shadow detection method employing online sub-scene shadow modeling and object inner-edges analysis for applications of static-camera video surveillance. To describe shadow appearance more accurately, the proposed method builds adaptive online shadow models for sub-scenes with different conditions of irradiance and reflectance. The online shadow models are learned by utilizing Gaussian functions to fit the significant peaks of accumulating histograms, which are calculated from Hue, Saturation and Intensity (HSI) difference of moving objects between background and foreground. Additionally, object inner-edges analysis is adopted to reject camouflages, which are misclassified foreground regions that are highly similar to shadows. Finally, the main shadow regions are expanded to recycle the misclassified shadow pixels based on local color constancy. The proposed algorithm can adaptively handle the shadow appearance changes and camouflages without prior information about illuminations and scenarios. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.     

基于HLA雷达信号建模仿真

数学仿真试验是实现对雷达电子战系统的性能鉴定与作战效能评估的重要技术手段.以雷达电子战作战效能评估需求为背景,提出基于高层体系结构(HLA)建立雷达信号仿真系统,完成雷达相干视频仿真,仿真雷达在电子对抗条件下目标探测、确认、跟踪及识别的全过程,同时作为HLA一个联邦可以集成到更高级别的仿真系统中.基于HLA雷达信号仿真关键技术包括FOM/SOM的开发、HLA接口、雷达信号模型的建立、用户界面等方面的内容.重点阐述HLA联邦成员设计与雷达信号建模两方面内容.     

Segmentation into three classes using gradients

Consider a three-dimensional "scene" in which a density f(x, y, z) is assigned to every point (x, y, z). In a discretized version of the scene the density D(i, j, k) assigned to the (i, j, k) th volume element (voxel) is the average value of f(x, y, z) over the voxel. Suppose that the points in the original scene can be meaningfully segmented into classes 1, 2, and 3 separated by two threshold values l and u. Partial volume artifact is the phenomenon that a voxel (i, j, k) which is at the interface of class 1 and class 3 (and thus contains only points with low and high densities) usually has a density D(i, j, k) between l and u, and so cannot be distinguished by density alone from a voxel which contains only points in class 2. We describe how a two-dimensional (gradient, density) feature space can be used for the segmentation of such discrete scenes into three classes in a meaningful way. We illustrate the method using examples from medical imaging.     

Oil spill detection: imaging system modeling and advanced image processing using optimized SDC algorithm

Oil spill on the sea surface might happen without any previous caution and is seen relatively often. Efficient and effective oil spill monitoring and detection can reduce response time, minimize remediation costs and limit dangerous impacts to the environment. An innovative satellite-based oil pollution detection framework is demonstrated in this paper, including satellite imaging system modeling—communication link configuration, noise model for image transmission and preprocessing. Finally, an optimized sequential detection of change-based image object detection algorithm is proposed to detect oil spill on the ocean surface from the enhanced remote sensing data. Moderate resolution imaging spectroradiometer images of the Gulf of Mexico accident from NASA between May and June 2010 are adopted for testing in our framework. The results of this research show that the proposed algorithms can effectively distinguish the spill covering vast areas of the marine environment even with severe additive noise and have good separation properties against complex signatures, such as the vicinity to the irregular coast or foggy and cloudy weather conditions.     

Signal modeling with filtered discrete fractional noise processes

Filtered versions of fractionally differenced Gaussian noise (fdGn) processes are studied. Fractionally differenced Gaussian noise is a discrete-time equivalent of fractional Brownian motion. Filtered versions of such processes are ideally suited for modeling signals with different short-term and long-term correlation structure. Two iterative algorithms for estimating the parameters of filtered fdGn processes are described. The first technique is based on the expectation-maximization algorithm. It converges to a stationary point of the log-likelihood function corresponding to the parameters of the model. The second technique is a computationally efficient approximate approach. It is found to converge experimentally, but no proof of its convergence is given. The usefulness of filtered fdGn models and the performance of the proposed iterative algorithms are illustrated by fitting filtered fdGn models to speech waveforms and other data corresponding to natural phenomena     

Signal detection games with power constraints

Formulates and solves maximin and minimax detection problems for signals with power constraints. These problems arise whenever it is necessary to distinguish between a genuine signal and a spurious one designed by an adversary with the principal goal of deceiving the detector. The spurious (or deceptive) signal is subject to certain constraints, such as limited power, which preclude it from replicating the genuine signal exactly. The detection problem is formulated as a zero-sum game involving two players: the detector designer and the signal designer. The payoff is the probability of error of the detector, which the detector designer tries to minimize and the deceptive signal designer to maximize. For this detection game, saddle point solutions-whenever possible-or otherwise maximin and minimax solutions are derived under three distinct constraints on the deceptive signal power; these distinct constraints involve lower bounds on (i) the signal amplitude, (ii) the time-averaged power, and (iii) the expected power. The cases of independent and identically distributed and correlated signals are considered     

Signal detection with a laser amplifier

The use of signal-to-noise ratio as a figure of merit for a signal detection system involving a threshold detector may lead to erroneous conclusions when the signal strength, in absolute terms, is small. Recourse to approximate or exact distributions must be made and the notion of statistical separation must be employed. A new figure of merit utilizing the idea of statistical separation is defined. The method is applied to the question of the efficiency of a laser amplifier in a pulse detection system.     

Signal detection by complex spatial filtering

In the past, spatial filtering in coherent optical systems has been limited by the inability to realize practically a general complex filter. This paper describes a technique for realizing such a filter, and gives an application of spatial filtering to the problem of detecting isolated signals in a variety of noise backgrounds. The experimental results obtained to date indicate that this technique provides an excellent two-dimensional filtering capability that will play a key role in such problems as shape recognition and signal detection.     

基于动作类的虚拟拆装过程运动建模仿真

针对装备拆装过程存在的拆装对象动作繁多,缺乏对各种零部件拆装动作的统一,不便于仿真实现的问题,提出维修拆装动作类对拆装运动进行描述,结合UML拆装执行基本拆装动作次数的不同,结合面向对象的思想提出了拆装动作类的概念,对拆装过程中具有相同动作次数的零部件做统一描述。利用UML建立拆装动作类的类图,结合状态机和拆装动作类建立拆装过程的运动模型。拆装仿真实例验证了动作类和拆装运动模型的可行性和实用性。     

Signal detection theory and reconstruction algorithms-performancefor images in noise

In many noisy image processing situations, decision making is the ultimate objective. Here, the authors show using signal detection theory how direct optimal processing of the projection data yields a considerable gain in the decision making performance over that obtained by first using image reconstruction. The problem is formulated in the framework of a two hypotheses detection problem. Optimal processors based on the likelihood ratio approach have been presented for two cases. The first considers direct processing of the projection data. The second applies optimal decision theory to the reconstructed data. Results based on computer simulation are presented in the form of receiver operating curves for different signal-to-noise ratios. Early results indicate that large performance gains can be achieved by direct optimal processing of the projection data compared with optimal processing of reconstructed data. Results for the latter case can be interpreted as providing an upper bound on all postreconstruction decision rules. The authors hope to extend this approach to a number of different aspects of the image decision making problem     

变压器局部放电信号检测与类型识别

为实现变压器局部放电信号检测和类型识别,设计基于超高频(UHF)法的变压器局部放电检测系统,针对4种典型的变压器放电模型进行了局部放电实验,获得相应的局部放电包络信号数据,并通过以太网通信将数据上传至电脑。利用提升双树复小波变换对包络信号数据进行消噪,从消噪后的信号不难看出,同一放电模型的局部放电包络信号形状大致相同,不同放电模型存在差别。提取6种包络信号的特征参数,结合外部加载电压,采用BP神经网络对变压器局部放电类型进行识别,当训练误差δ=0.02时,变压器放电类型识别平均正确率在98%以上。     

Signal detection under mismatch (Corresp.)

A binary detection problem of the Neymann-Pearson type, in which the probability density functions used are inaccurate versions of the true ones, are considered. The performance of the above suboptimal detection scheme as the number of observations increases is investigated. A necessary and sufficient condition is given for the exponential convergence to zero of the two error probabilities as the number of observations increases. The condition is in terms of an inequality between differences of asymptotic per sample informational divergence expressions.