车载环境下基于样本熵的语音端点检测方法

在语音处理中一个关键性问题是如何准确找到语音的起止位置,目前提出许多的语音端点检测算法不能得到理想的检测结果.由于样本熵是近似熵的改进算法,提出车载环境下基于样本熵的语音端点检测方法.并采用模糊C均值聚类算法和贝叶斯信息判决算法进行样本熵特征门限估计,以及使用双门限法进行语音端点检测.在TIMIT连续语音库上的实验表明,车载噪声环境下,样本熵法和近似熵法的检测正确率均远高于谱熵法和能量谱熵法,而样本熵法相对于近似熵法具有更好的检测效果,特别是当信噪比小于等于OdB时,样本熵法的检测性能优于近似熵法近10%.因此,样本熵法在车栽智能语音领域具有很好的应用前景,能够为车载导航提供准确的语音端点检测技术.     

光谱响应差异对高精度交叉定标的影响——以FY-3A/MERSI与EOS/MODIS为研究实例

分析了MERSI与MODIS间光谱响应差异对测量表观反射比的影响。正演模拟结果显示,在极区冰雪情况下,MERSI与MODIS测量得到的表观反射比之间的相对差异是关于MODIS所测表观发射比的二次函数。MERSI的前4个通道与对应的MODIS通道之间的相对差异在0~0.8%,0~2%,0.5%~2.5%,-1.8%~-0.8%之间。将正演模拟得到的对光谱响应差异的修正关系应用到具体的SNO测量后,MERSI前3个通道的表观反射比与修正后MODIS的表观反射比之间的相对差异减小到了3%以内。MERSI的1、2通道在红外波段也有响应,这部分响应在极区冰雪条件下对表观反射率的影响在0.5%左右,基本可以忽略。     

On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images

In this work, neural network-based models involved in hyperspectral image spectra separation are considered. Focus is on how to select the most highly informative samples for effectively training the neural architecture. This issue is addressed here by several new algorithms for intelligent selection of training samples: (1) a border-training algorithm (BTA) which selects training samples located in the vicinity of the hyperplanes that can optimally separate the classes; (2) a mixed-signature algorithm (MSA) which selects the most spectrally mixed pixels in the hyperspectral data as training samples; and (3) a morphological-erosion algorithm (MEA) which incorporates spatial information (via mathematical morphology concepts) to select spectrally mixed training samples located in spatially homogeneous regions. These algorithms, along with other standard techniques based on orthogonal projections and a simple Maximin-distance algorithm, are used to train a multi-layer perceptron (MLP), selected in this work as a representative neural architecture for spectral mixture analysis. Experimental results are provided using both a database of nonlinear mixed spectra with absolute ground truth and a set of real hyperspectral images, collected at different altitudes by the digital airborne imaging spectrometer (DAIS 7915) and reflective optics system imaging spectrometer (ROSIS) operating simultaneously at multiple spatial resolutions.     

Parametric polynomial spectral factorization using the sum of roots and its application to a control design problem

This paper presents an algebraic approach to polynomial spectral factorization, an important mathematical tool in signal processing and control. The approach exploits an intriguing relationship between the theory of Gröbner bases and polynomial spectral factorization which can be observed through the sum of roots, and allows us to perform polynomial spectral factorization in the presence of real parameters. It is discussed that parametric polynomial spectral factorization enables us to express quantities such as the optimal cost in terms of parameters and the sum of roots. Furthermore an optimization method over parameters is suggested that makes use of the results from parametric polynomial spectral factorization and also employs two quantifier elimination techniques. This proposed approach is demonstrated in a numerical example of a particular control problem.     

A novel associative memory approach to speech enhancement in a vehicular environment

Numerous attempts have been undertaken to apply the spectral subtraction method to cancel noise perturbations but these efforts have yet to produce an algorithm that is able to adapt well to the environmental changes in the perturbations. In addition, the variants of the spectral subtraction method so far proposed in the literature would require a non-voice activity detector (NVAD), for a single microphone system, to store the perturbation. This is used as an estimate for the reference signal. Inaccuracy in the perturbation estimates causes the cleaned speech to be corrupted by musical artifacts, which is unacceptable. Post processing of signals corrupted by the musical artifacts is very costly. This paper provides an alternative approach that employs associative memory for speech enhancement. Extensive comparison is made using the soft computing approaches for noise cancellation based on associative memories. A set of stereo microphones captures the corrupted speech in a vehicle and is used to point to the closest associative memory location. The Wiener filter approach is used to cancel the noise. The paper discusses novel examples of the associative memories using the cerebellum model for noise modeling. Experimental results show the potential of these novel soft computing architectures in generating and adapting the required Weiner filters to cancel perturbation even at signal to noise ratio (SNR) of less than −13 dB.     

Development of a 3D HUD using a tunable bandpass filter for wavelength multiplexing

A 3D stereoscopic head‐up display using a tunable bandpass filter to perform left and right image spectral separation is presented. Using a single filter reduces the size and the cost of the head‐up display optical engine and enables each spectral band to be accurately tuned. Experiments performed on the first prototype demonstrate the ability to continuously tune the bandpass frequency on 30‐nm range while keeping a 20‐nm bandwidth. Such a system avoids the use of a bulky and costly rotating wheel and enables the use of holographic optical elements known to be wavelength selective.     

基于Wollaston棱镜阵列的目标识别系统

针对传统目标识别方法识别率低、受背景环境影响大等问题,设计了一种基于Wollaston棱镜阵列的偏振光谱成像系统.为了有效地探测野外目标的微弱信号,采用了高光通量的光学天线、偏振模块及干涉模块的设计.为了保证实时采集及高稳定性的设计要求,采用了全静态光学器件完成偏振分光及空间光程调制.系统由偏振成像模块与光谱成像模块构成,可实现同一视场内4个偏振方向图像及光谱图像的同时采集,并经图像融合算法重建被测区域的偏振光谱图像.实验在不同测试距离上分别以钢板和某型火炮作为被测目标,分别采用本系统偏振模块与传统非偏振系统进行测试.实验结果显示,偏振目标识别信噪比高、受距离影响小.基于此实验,再将偏振识别图像与光谱识别图像相融合,从而构成偏振光谱图像.与传统目标识别图像相比较,本系统合成图像中目标清晰可见,受背景环境影响小、可探测距离远,具有很高的野外适应性及稳定性.     

成像光谱技术的古画隐藏信息提取

目的 古画隐藏信息是包含在画作中的一种微弱信息,对于文物鉴定与修复具有非常重要的意义,如何快速无损准确提取古画中微弱的隐藏信息是目前研究和应用的一大技术难题,本文利用高光谱成像技术快速无损提取古画隐藏信息。方法 对古画进行高光谱扫描成像,得到短波红外波段（1 000~2 500 nm）的高光谱立方体数据,联合多种光谱匹配技术分析古画中人物头冠颜料种类;利用主成分分析技术,提取古画头冠周围涂抹痕迹。结果 通过多种光谱匹配算法对颜料进行分析,确定了颜料种类,并对匹配算法进行了对比,结果表明在颜料混合的情况下,光谱匹配算法仍能够有效的识别出混合颜料的端元种类,光谱信息散度与光谱角的匹配算法SID_SA匹配精度可以达到0.096,相比其他匹配算法,效果最好;利用主成分分析技术分层剥离主要背景信息后,头冠周围位于画作底层的涂抹信息得到了加强,可以有效地区分背景颜料信息与涂抹信息,提取隐藏信息。结论 高光谱分析技术对古画隐藏信息的提取有很好地识别效果,能够准确提取绘画颜料种类,发现绘画过程中的涂抹等痕迹,特别适用于混合颜料的种类识别与画作底层信息提取,发掘画作隐藏信息,为文物保护修复与鉴定提供支持。     

General Spectral Camera Lens Simulation

We present a camera lens simulation model capable of producing advanced photographic phenomena in a general spectral Monte Carlo image rendering system. Our approach incorporates insights from geometrical diffraction theory, from optical engineering and from glass science. We show how to efficiently simulate all five monochromatic aberrations, spherical and coma aberration, astigmatism, field curvature and distortion. We also consider chromatic aberration, lateral colour and aperture diffraction. The inclusion of Fresnel reflection generates correct lens flares and we present an optimized sampling method for path generation.     

Estimation of the Bivariate Stable Spectral Representation by theProjection Method

A method of estimating the spectral representation of a generalized bivariatestable distribution is presented, based on a series of maximum likelihood (ML)estimates of the stable parameters of univariate projections of the data. Thecorresponding stable spectral density is obtained by solving a quadraticprogram. The proposed method avoids the often arduous task of computing themultivariate stable density, relying instead on the standard univariate stabledensity. The paper applies this projection procedure, under the simplifyingassumption of symmetry, to simulated data as well as to foreign exchangereturn data, with favorable results. Kanter projection coefficients governingconditional expectations are computed from the estimated spectral density. For the simulated data these compare well to their known true values.