Principles of space-time array processing for ultrawide-bandimpulse radar and radio communications

The emerging ultrawide-band (UWB) impulse technology has found numerous applications in the commercial as well as the military sector. The rapid technological advances have made it possible to implement (cost-effective, short-range) impulse radar and impulse-radio communication and localization systems. Array beamforming and space-time processing techniques promise further advancement in the operational capabilities of impulse radar and impulse-radio communications to achieve long-range coverage, high capacity and interference-free quality of reception. We introduce a realistic signal model for UWB impulse waveforms and develop the principles of space-time array processing based on the signal model. A space-time resolution function (STRF), a space-frequency distribution function (SFDF) and a monopulse-tracking signal are derived for impulse waveforms received by a self-steering array beamforming system. The directivity peak-power pattern and energy pattern of the beamformer are also derived. Computer plots of the STRF, SFDF and the beam patterns are obtained. The directivity beam patterns of impulse waveforms are sidelobe-free and, therefore, there is no need for sidelobe suppression via amplitude weighting of the array elements. Also, the resolution angle for the beam patterns is derived as a decreasing function of array size and frequency bandwidth. Electronic beamsteering based on slope processing of monopulse waveforms is described     

A point process model for auditory neurons considering both their intrinsic dynamics and the spectrotemporal properties of an extrinsic signal

We propose a point process model of spiking activity from auditory neurons. The model takes account of the neuron's intrinsic dynamics as well as the spectrotemporal properties of an input stimulus. A discrete Volterra expansion is used to derive the form of the conditional intensity function. The Volterra expansion models the neuron's baseline spike rate, its intrinsic dynamics-spiking history-and the stimulus effect which in this case is the analog of the spectrotemporal receptive field (STRF). We performed the model fitting efficiently in a generalized linear model framework using ridge regression to address properly this ill-posed maximum likelihood estimation problem. The model provides an excellent fit to spiking activity from 55 auditory nerve neurons. The STRF-like representation estimated jointly with the neuron's intrinsic dynamics may offer more accurate characterizations of neural activity in the auditory system than current ones based solely on the STRF.     

Motion analysis and segmentation through spatio-temporal slices processing

This paper presents new approaches in characterizing and segmenting the content of video. These approaches are developed based upon the pattern analysis of spatio-temporal slices. While traditional approaches to motion sequence analysis tend to formulate computational methodologies on two or three adjacent frames, spatio-temporal slices provide rich visual patterns along a larger temporal scale. We first describe a motion computation method based on a structure tensor formulation. This method encodes visual patterns of spatio-temporal slices in a tensor histogram, on one hand, characterizing the temporal changes of motion over time, on the other hand, describing the motion trajectories of different moving objects. By analyzing the tensor histogram of an image sequence, we can temporally segment the sequence into several motion coherent subunits, in addition, spatially segment the sequence into various motion layers. The temporal segmentation of image sequences expeditiously facilitates the motion annotation and content representation of a video, while the spatial decomposition of image sequences leads to a prominent way of reconstructing background panoramic images and computing foreground objects.     

A new spatio-temporal equalization method based on estimatedchannel response

This paper proposes a new spatio-temporal equalization method, which simultaneously utilizes an adaptive antenna array and a decision feedback equalizer (DFE). For effective spatio-temporal equalization with less computational cost, how to split equalization functionality into spatial processing, and temporal processing is quite important. One of the answers which we have given is “incoming signals with larger time delays should be cancelled at the spatial equalization part.” The weights of both adaptive antenna array elements and taps of DFE are calculated only using the estimated channel impulse response, therefore, it requires no information on direction of arrival (DoA). We show the performance of the proposed system in multipath fading channels often encountered in indoor wireless environments and discuss the attainable bit error rate (BER), antenna patterns, and the computational complexity in comparison with other equalization methods such as spatial equalization and temporal equalization     

Detection and visualization method of dynamic state transition for biological spatio-temporal imaging data

In the statistical analysis of functional brain imaging data, regression analysis and cross correlation analysis between time series data on each grid point have been widely used. The results can be graphically represented as an activation map on an anatomical image, but only activation signal, whose temporal pattern resembles the predefined reference function, can be detected. In the present study, we propose a fusion method comprising innovation approach in time series analysis and statistical test. Autoregressive (AR) models were fitted to time series data of each pixel for the range sufficiently before or after the state transition. Then, the remaining time series data were filtered using these AR parameters to obtain its innovation (filter output). The proposed method could extract brain neural activation as a phase transition of dynamics in the system without employing external information such as the reference function. The activation could be detected as temporal transitions of statistical test values. We evaluated this method by applying to optical imaging data obtained from the mammalian brain and the cardiac sino-atrial node (SAN), and demonstrated that our method can precisely detect spatio-temporal activation profiles in the brain or SAN.     

Échantillonnage des images de télévision: analyse dans le domaine spatio-temporel et dans le domaine de Fourier

The spatio-temporal sampling of television pictures is considered. The authors first introduce the notation (spatio-temporal domain, video domain, Fourier domains), the notion of sampling pattern and the spatio-temporal sampling theorem. The Fourier transforms of the selected sampling patterns are then calculated. In the spatio-temporal domain and in the Fourier domain, they conduct the objective analysis of the sampling degradations for various picture configurations (spatio-temporal sine-wave patterns and edges). They then go into the problem of optimizing the sampling and conclude by mentioning spatio-temporal filtering and colour sampling aspects.     

海量车牌识别数据集上基于时空划分的旅行时间计算方法

城市路段旅行时间计算是智能交通领域的一个研究热点.车牌识别数据作为近年来新兴的一种针对城市道路行驶车辆的实时监测数据,具有持续生成且数据量大、时间空间相关等特性.为了利用车牌识别数据集进行高效、准确的旅行时间计算,给出了基于车牌识别数据集的旅行时间计算定义,在此基础上提出一种基于时空划分的流水线式并行计算模型,并给出了该模型基于实时MapReduce的实现.通过一组基于海量真实车牌识别数据集的实验表明,本文方法在亿级车牌识别数据集上的旅行时间计算性能方面相对于直接基于Hadoop的实现可以提高3倍以上,同时具有适合细粒度划分及受路网规模影响小的特点.     

On Analog Networks and Mixed-Domain Spatio-Temporal Frequency Response

In this paper, we extend the linear cellular neural network (CNN) paradigm by introducing temporal derivative diffusion connections between neighboring cells. Our proposal results in an analog network topology for implementing general continuous-time discrete-space mixed-domain 3-D rational transfer functions for linear filtering. The network connections correspond one-to-one to the transfer function coefficients. The mixed-domain frequency response is treated as a temporal frequency-dependent spatial function and we show how nonseparable properties of the spatio-temporal magnitude response can be derived from the combination of: 1) sinusoidal functions of spatial frequencies and 2) polynomials of the continuous-time frequency in the 3-D frequency response expression. A generic VLSI-compatible implementation of the network based on continuous-time integrators is also proposed. Based on our proposed CNN extension, the analysis of a spatio-temporal filtering example originated from analytical modeling of receptive fields of the visual cortex is presented and a spatio-temporal cone filter is designed and presented with numerical simulation results.      

A fuzzy rule-based approach to spatio-temporal hand gesturerecognition

Gesture based applications widely range from replacing the traditional mouse as a position device to virtual reality and communication with the deaf. The article presents a fuzzy rule based approach to spatio-temporal hand gesture recognition. This approach employs a powerful method based on hyperrectangutar composite neural networks (HRCNNs) for selecting templates. Templates for each hand shape are represented in the form of crisp IF-THEN rules that are extracted from the values of synaptic weights of the corresponding trained HRCNNs. Each crisp IF-THEN rule is then fuzzified by employing a special membership function in order to represent the degree to which a pattern is similar to the corresponding antecedent part. When an unknown gesture is to be classified, each sample of the unknown gesture is tested by each fuzzy rule. The accumulated similarity associated with all samples of the input is computed for each hand gesture in the vocabulary, and the unknown gesture is classified as the gesture yielding the highest accumulative similarity. Based on the method we can implement a small-sized dynamic hand gesture recognition system. Two databases which consisted of 90 spatio-temporal hand gestures are utilized for verifying its performance. An encouraging experimental result confirms the effectiveness of the proposed method     

Identifying regional cardiac abnormalities from myocardial strains using nontracking-based strain estimation and spatio-temporal tensor analysis

Myocardial strain is a critical indicator of many cardiac diseases and dysfunctions. The goal of this paper is to extract and use the myocardial strain pattern from tagged magnetic resonance imaging (MRI) to identify and localize regional abnormal cardiac function in human subjects. In order to extract the myocardial strains from the tagged images, we developed a novel nontracking-based strain estimation method for tagged MRI. This method is based on the direct extraction of tag deformation, and therefore avoids some limitations of conventional displacement or tracking-based strain estimators. Based on the extracted spatio-temporal strain patterns, we have also developed a novel tensor-based classification framework that better conserves the spatio-temporal structure of the myocardial strain pattern than conventional vector-based classification algorithms. In addition, the tensor-based projection function keeps more of the information of the original feature space, so that abnormal tensors in the subspace can be back-projected to reveal the regional cardiac abnormality in a more physically meaningful way. We have tested our novel methods on 41 human image sequences, and achieved a classification rate of 87.80%. The regional abnormalities recovered from our algorithm agree well with the patient's pathology and clinical image interpretation, and provide a promising avenue for regional cardiac function analysis.     

An integrated neighborhood correlation and hierarchical clustering approach of functional MRI

Clustering analysis is a promising data-driven method for the analysis of functional magnetic resonance imaging (fMRI) time series, however, the huge computation load makes it difficult for practical use. In this paper, neighborhood correlation (NC) and hierarchical clustering (HC) methods are integrated as a new approach where fMRI data are processed first by NC to get a preliminary image of brain activations, and then by HC to remove some noises. In HC, to better use spatial and temporal information in fMRI data, a new spatio-temporal measure is introduced. A simulation study and an application to visual fMRI data show that the brain activations can be effectively detected and that different response patterns can be discriminated. These results suggest that the proposed new integrated approach could be useful in detecting weak fMRI signals.     

On the use of spatio-temporal multiresolution analysis in method ofmoments solutions of transient electromagnetic scattering

A novel method of moments approach to the solution of time-domain integral-equation formulation of electromagnetic scattering problems is presented. The method is based on a spatio-temporal multiresolution analysis. This analysis facilitates a basis from which a small number of expansion functions is selected via an iterative procedure and utilized to model the unknown current distribution. In contrast to marching-on-in-time sequential procedures, the proposed method models the unknown current simultaneously at all the time steps within the time frame of interest. This new method is applied to a one-dimensional (1-D) problem of electromagnetic plane wave interaction with a dielectric slab. A comparison of the computed results with results based on the analytic solution demonstrates that the method is capable of attaining accurate results while achieving substantial reduction in computation time and resources     

Fast spatio-temporal image reconstruction for dynamic PET

In tomographic imaging, dynamic images are typically obtained by reconstructing the frames of a time sequence independently, one by one. A disadvantage of this frame-by-frame reconstruction approach is that it fails to account. For temporal correlations in the signal. Ideally, one should treat the entire image sequence as a single spatio-temporal signal. However, the resulting reconstruction task becomes computationally intensive. Fortunately, as the authors show in this paper, the spatio-temporal reconstruction problem call be greatly simplified by first applying a temporal Karhunen-Loeve (KL) transformation to the imaging equation. The authors show that if the regularization operator is chosen to be separable into space and time components, penalized weighted least squares reconstruction of the entire image sequence is approximately equivalent to frame-by-frame reconstruction in the space-KL domain. By this approach, spatio-temporal reconstruction can be achieved at reasonable computational cost. One can achieve further computational savings by discarding high-order KL components to avoid reconstructing them. Performance of the method is demonstrated through statistical evaluations of the bias-variance tradeoff obtained by computer simulation reconstruction     

Background light ray modeling for change detection

This paper is an extension of the work that was originally reported in Shimada et al. (2013). This paper proposes a change detection method based on spatio-temporal light ray consistency. The proposed method introduces light field sensing, which is used to generate an arbitrary in-focus plane. Change detection is performed in a surveillance scene, where the background region can be filtered out by an out-focusing process. This approach resolves a longstanding issue in background modeling-based object detection, which often suffers from false positives in the background regions. To realize this new change detection method, a new feature representation, called the local ray pattern (LRP), is introduced. The LRP evaluates the spatial consistency of the light rays, and this plays an important role in distinguishing whether the light rays come from the in-focus plane or elsewhere. A combination of the LRP and Gaussian mixture model (GMM)-based background modeling realizes change detection in the in-focus plane. Experimental results demonstrate the proposed method’s effectiveness and its applicability to video surveillance.     

基于LCMV线性约束的自适应方向图控制

该文提出一种基于线性最小方差约束(LCMV)的自适应方向图控制方法,在约束条件中增加了对静态方向图的拟合条件,可以在自适应抗干扰的同时形成期望副瓣形状,分析了小快拍条件下自适应方向图副瓣起伏机理,并把对角加载与本方法结合,极大改善了副瓣收敛速度,并在小快拍时就能有较好的性能。随后的计算机仿真证明了本文方法的有效性。     

结合时空注意力机制和自适应图卷积网络的骨架行为识别

针对骨架行为识别对时空特征提取不充分以及难以捕捉全局上下文信息的问题,研究了一种将时空注意力机制和自适应图卷积网络相结合的人体骨架行为识别方案。首先,构建基于非局部操作的时空注意力模块,辅助模型关注骨架序列中最具判别性的帧和区域;其次,利用高斯嵌入函数和轻量级卷积神经网络的特征学习能力,并考虑人体先验知识在不同时期的影响,构建自适应图卷积网络;最后,将自适应图卷积网络作为基本框架,并嵌入时空注意力模块,与关节信息、骨骼信息以及各自的运动信息构建双流融合模型。该算法在NTU RGB+D数据集的两种评价标准下分别达到了90.2%和96.2%的准确率,在大规模的数据集Kinetics上体现出模型的通用性,验证了该算法在提取时空特征和捕捉全局上下文信息上的优越性。      

基于过完备字典稀疏表示的多通道脑电信号压缩感知联合重构

该文基于多通道脑电信号时空特性构建非正交变换过完备字典,准确稀疏表示蕴含时空相关性信息的多通道脑电信号,提高基于时空稀疏贝叶斯学习模型的多通道脑电信号压缩感知联合重构算法性能。实验选用eegmmidb脑电数据库的多通道脑电信号验证所提算法有效性。结果表明,基于过完备字典稀疏表示的多通道脑电信号,能够为多通道脑电信号压缩感知重构算法提供更多的时空相关性信息,比传统多通道脑电信号压缩感知重构算法所得的信噪比值提高近12 dB,重构时间减少0.75 s,显著提高多通道脑电信号联合重构性能。     

脉冲/连续高功率激光光斑分布阵列探测器

通过测量发射到远场的激光功率密度时空分布给出所需要的到靶总功率、光束质量、桶中功率、功率时间曲线等关键指标参数,是目前准确评价激光系统性能的重要技术手段。介绍了一种基于光电探测器阵列实现近红外脉冲激光功率密度时空分布的测量方法,可以实现900~1700 nm波长、动态范围大于2000倍的激光光斑参数测量。该阵列探测器具有测量面积大、单元一致性好、测量精度高等特点,并可同时实现脉冲和连续激光参数测试要求。给出了阵列探测器的总功率测量结果,测量值与激光器输出功率偏差在5%以内,且激光光斑分布测量结果准确可靠。该阵列探测器已在多套激光系统的参数测试中得到成功应用,可以作为响应波段内的脉冲/连续激光光斑参数测试一种有效技术方案。     

非制冷IRFPA像元结构与时空随机噪声的优化理论

非制冷红外焦平面探测器(Uncooled IRFPA)像元结构从单层结构向双层结构的发展降低了探测器噪声,提升了性能。介绍了像元的MEMS结构及主要物理参数,指出双层结构与单层结构的主要差异在于像元有效面积和桥臂热导的不同。三维噪声模型是对IRFPA噪声进行分析的有效手段,其中时空随机噪声是非制冷IRFPA最主要的噪声成份。分析了非制冷IRFPA时空随机噪声的产生机理,建立了时空随机噪声模型,得到时空随机噪声与像元有效面积和桥臂热导的关系。将某款单层像元结构探测器改进为双层像元结构并进行噪声测试,实测数据证明了非制冷IRFPA时空随机噪声模型的有效性。     

Multiple dipole modeling and localization from spatio-temporal MEGdata

An array of biomagnetometers may be used to measure the spatio-temporal neuromagnetic field or magnetoencephalogram (MEG) produced by neural activity in the brain. A popular model for the neural activity produced in response to a given sensory stimulus is a set of current dipoles, where each dipole represents the primary current associated with the combined activation of a large number of neurons located in a small volume of the brain. An important problem in the interpretation of MEG data from evoked response experiments is the localization of these neural current dipoles. We present here a linear algebraic framework for three common spatio-temporal dipole models: i) unconstrained dipoles, ii) dipoles with a fixed location, and iii) dipoles with a fixed orientation and location. In all cases, we assume that the location, orientation, and magnitude of the dipoles are unknown. With a common model, we show how the parameter estimation problem may be decomposed into the estimation of the time invariant parameters using nonlinear least-squares minimization, followed by linear estimation of the associated time varying parameters. A subspace formulation is presented and used to derive a suboptimal least-squares subspace scanning method. The resulting algorithm is a special case of the well-known MUltiple SIgnal Classification (MUSIC) method, in which the solution (multiple dipole locations) is found by scanning potential locations using a simple one dipole model. Principal components analysis (PCA) dipole fitting has also been used to individually fit single dipoles in a multiple dipole problem. Analysis is presented here to show why PCA dipole fitting will fail in general, whereas the subspace method presented here will generally succeed. Numerically efficient means of calculating the cost functions are presented, and problems of model order selection and missing moments are discussed. Results from a simulation and a somatosensory experiment are presented.