Shack-Hartmann sensor improvement using optical binning

We present a design improvement for a recently proposed type of Shack-Hartmann wavefront sensor that uses a cylindrical (lenticular) lenslet array. The improved sensor design uses optical binning and requires significantly fewer detector pixels than the corresponding conventional or cylindrical Shack-Hartmann sensor, and so detector readout noise causes less signal degradation. Additionally, detector readout time is significantly reduced, which reduces the latency for closed loop systems and data processing requirements. We provide simple analytical noise considerations and Monte Carlo simulations, we show that the optically binned Shack-Hartmann sensor can offer better performance than the conventional counterpart in most practical situations, and our design is particularly suited for use with astronomical adaptive optics systems.     

一种视频强杂波两级空域滤波抑制方法

为便于对复杂视频强杂波背景下的微弱运动目标进行检测,提出了一种两级空域滤波强杂波抑制方法。采用局域均值去除(LMR)作为一级滤波,将原始图像变换为较大局域内均值和方差缓变的二维广义平稳过程,再通过基于二维AR模型参数估计的二级滤波器自适应滤除残余杂波。实验结果表明,对于一般噪声条件下的视频目标图像,利用该方法处理后得到的残留噪声呈现出很好的高斯性和独立性,并且输出目标信号杂波噪声比(SCNR)的平均增益达到1．33dB,有利于进一步的检测。     

合成孔径雷达图像的恒虚警率目标检测

目的　对合成孔径雷达 (SAR)图像实施恒虚警率 (CFAR)目标检测 .方法　利用 K-Gamma和Weibull分布的杂波模型分别对海面和陆地的人造目标实施检测 ;同时 ,对不同杂波背景下 CFAR检测方法进行比较 .结果与结论　通过检测结果的比较 ,证实了 K-Gamma分布和 Weibull分布分别适合于海面和陆地杂波背景下的目标检测 ;在非均匀杂波背景中 ,有序统计量 (OS)较单元平均 (CA)方法具有优越性     

Self-oriented glucose-modified infrared sensor for the detection of compounds bearing carboxylic acid groups

To explore the interactions of self-oriented glucose toward organic species, we prepared a glucose-modified infrared (IR) chemical sensor and examined its performance in the detection of compounds bearing carboxylic acid functional groups. Using a two-layer modification method, we chemically bonded glucose to the surface of an IR sensing element. The immobilized glucose moieties exhibited the ability to assemble into a chemical form similar to that of the cyclodextrins (CDs) such that they could trap targeted molecules. This modified sensing phase displayed an analysis capability similar to that of CDs, but without the problems encountered attempting to maintain the activity of the latter. From a systematic examination of three classes of structural analogs of acids, we found that the behavior of the glucose-modified sensing phase was similar to that of a CD-modified sensing phase. In terms of quantitative detection, we obtained an average regression coefficient of approximately 0.996 for the analytical signals of the examined compounds at concentrations less than 20 mg/L. The linearity of the standard curves was related to the detection time: the shorter the detection time, the greater the linearity. Defined as three times the noise level, we obtained detection limits of a few hundred parts per billion (microg/L) for the determination of high-polarity compounds using our glucose-modified sensing phase.     

基于声呐历程累积图像的弱回波目标检测方法

针对浅海随机噪声与混响背景下蛙人等弱回波强度、慢速小目标的检测问题,提出一种基于声呐历程累积图像的目标检测方法。首先根据声呐图像时域、空域相关性,采用背景空时归一化处理技术,抑制声呐背景中的静态混响、突发性噪声等强回波干扰。声呐历程累积图像集成了多帧声呐图像的信息,目标回波亮点由于运动连续性形成亮线特征,利用该特征,采用Radon恒虚警率(Radon Constant False Alarm Rate,Radon-CFAR)检测声呐历程累积图像中的目标短时运动轨迹,能够检测到低信噪比的目标。分析了空时归一化处理和检测算法的性能,并通过海试数据验证了该算法的有效性,可以检测到低信噪比的蛙人目标回波。     

基于声呐历程累积图像的弱回波目标检测方法

针对浅海随机噪声与混响背景下蛙人等弱回波强度、慢速小目标的检测问题,提出一种基于声呐历程累积图像的目标检测方法.首先根据声呐图像时域、空域相关性,采用背景空时归一化处理技术,抑制声呐背景中的静态混响、突发性噪声等强回波干扰.声呐历程累积图像集成了多帧声呐图像的信息,目标回波亮点由于运动连续性形成亮线特征,利用该特征,采...     

Multiple-input multiple-output over-thehorizon radar: experimental results

Results from an experiment that applied one class of multiple-input multiple-output (MIMO) waveform techniques to over-the-horizon radar (OTHR) are reported. The experiment objective was to demonstrate that adaptive transmitter beamforming could be used in an appropriately design radar to reject spatially discrete Doppler-spread clutter. In the particular MIMO radar architecture that the authors call non-causal transmit beamforming, conventional or adaptive transmitter beamforming occurs following waveform transmission, propagation, scatter from targets and clutter sources, return propagation and finally signal reception. In the case reported herein spatially discrete clutter was successfully rejected to the noise floor of the radar return with rejection in excess of 35 dB achieved using common adaptive algorithms and straightforward training data selection. As part of the rejection algorithm the transmitted waveform direction-of-departure (DOD) from the transmitter array to the target was estimated and used as the preserved steer direction in the adaptive beamformer. The DOD estimates agree well with the geometrically determined true values. The demonstration of non-causal transmit beamforming suggests that it will be possible to create multiple simultaneous adaptive range-dependent transmitter beams with an appropriately designed OTHR. This has several applications including for the mitigation of Doppler-spread clutter.     

低信噪比图象中运动目标的实时检测

在低信噪比条件下,要从图象中以视频速率检测出运动目标存在不少困难,提出一种用帧间邻域高位比较法检测当前帧的运动目标的方法。这种方法包括选择处理窗口对当前象素及其邻域象素编码,存储和比较。     

形态学和小波域杂波抑制的微弱目标检测

本文提出了一种基于形态学和小波域杂波抑制的微弱目标检测方法,该方法将图像序列进行形态擘tophat滤波,然后小波变换,再分别对各小波子带作平滑滤波,按各子带对滤波前后小波系数作差分运算,最后经过小波逆变换得到具有微弱目标的残差图像序列.用残差图像tophat结果估计目标潜在区域,在目标潜在域的约束下,对残差图像序列进行时空域数据融合,实现微弱运动目标的检测.仿真实验表明,该方法杂波抑制后残差图像具有很好的白高斯特性,且目标邻域信杂比(scNR)的平均增益比图像空域平滑滤波和图像频域低通滤波等典型运算的SCNR平均增益有明显改善,目标检测算法在5帧图像集成时能稳定检测出微弱运动目标轨迹.     

去除脉冲噪声的自适应开关中值滤波

为消除图像中的脉冲噪声,提出了自适应开关中值(ASM)滤波算法。该算法采用一种新的噪声检测方法将图像中的像素分为信号点和噪声点两类。对检测出的噪声点统计其个数并由此估算图像中的噪声密度,根据估计的噪声密度自适应确定滤波窗口尺寸,采用改进的中值滤波对检测出的噪声点进行处理;而信号点则保留其灰度值不予处理。对ASM滤波进行仿真实验,结果表明,它能在有效去除噪声的同时很好地保护图像细节,较传统中值滤波及其它改进中值滤波算法有更优的滤波性能。     

基于小波变换的海面背景红外小目标检测方法

采用基于正交小波分解的多分辨率分析实现频带选择,抑制噪声和背景杂波;检测图像的水平和垂直方向边缘,确定海天线和目标潜在区;利用不同方向边缘进行融合,除去大量背景干扰而获得目标候选点;检测候选目标点的灰度值,确定阈值排除虚警点并分割目标。实验结果表明,该方法能检测复杂海面背景中的舰船红外小目标。     

The foreground detection algorithm combined the temporal–spatial information and adaptive visual background extraction

Visual background extraction algorithm, which utilises a global threshold to complete the foreground segmentation, cannot adapt to illumination change well. It will easily choose the wrong pixels to initialise the background model, resulting in the emergence of the ghost in the beginning of detection. In order to address these problems, this article proposes an improved algorithm based on pixel’s temporal–spatial information to initialise the background model. First of all, the pixels in video image sequences and their neighbourhood pixels are used to complete background model initialisation in the first five frames. Second, the segmentation threshold is adaptively obtained by the complexity of background that uses the spatial neighbourhood pixels. Finally, the background model of the neighbourhood pixels is updated by a dynamic update rate which is gained by calculating the Euclidean distance between pixels. Experimental results and comparative study illustrate that the improved method can not only increase the accuracy of target detection by reducing the impact of illumination change effectively but also eliminate the ghost quickly.     

Adaptive detectors with diagonal loading for airborne multi-input multi-output radar

The problem of adaptive target detection for airborne multi-input multi-output (MIMO) radars with space-time receivers in the presence of Gaussian interference (including clutter and noise) is studied. Previous work has assumed the interference covariance matrix to be known. The case with unknown covariance matrix is investigated here. By exploiting the low rank property of clutter subspace, generalised likelihood ratio test detector and adaptive matched filter detector with diagonal loading are suggested to improve the detection performance of MIMO radars in limited secondary data case. The closed-form detection probabilities and false alarm probabilities of the two proposed detectors are derived and numerically evaluated. Theoretical analysis and numerical results show the advantages of the proposed detectors.     

Performance of the adaptive generalised matched subspace constant false alarm rate detector in non-Gaussian noise: an experimental analysis

The authors deal with the performance analysis of an adaptive version of the generalised matched subspace detector (GMSD) in compound-Gaussian clutter with unknown covariance matrix. The original GMSD was proposed to detect subspace signals in compound-Gaussian noise with known covariance matrix and ensures the constant false alarm rate (CFAR) property. In real situations, this assumption is unrealistic, which means that the covariance matrix must be estimated from training data. The authors use a robust estimate of the covariance matrix called the fixed-point estimate, recently proposed in the literature. The performance of the obtained adaptive detector, in terms of CFAR behaviour and probability of detection, is evaluated in the presence of real sea clutter data, collected by the McMaster IPIX radar.     

Novel portable electrical detection system for DNA SENSOR application

A novel, portable, electrical detection system was constructed for DNA sensor application to detect DNA hybridisation. The read-out circuit consists of an analogue circuit and a digital circuit. The analogue circuit with an IC MAX038 generates a sine wave with a constant frequency (10?kHz), which serves as the input for the DNA sensor. The DNA sensor consists of active and reference sensors. DNA hybridisation between the DNA probe and the target sequences causes changes in the conductance of the conductive membrane (DNA/MWCNTs) on the sensor surface, which lead to changes in the amplitude of the sine wave from the sensor output compared with that of the reference signal output (input sine wave). We used a digital circuit with a microprocessor (PIC33FJ256MC710) to determine the change in the amplitude of the sine wave signal of the sensor. From these digital data, the difference in the amplitudes of the active and reference sensors was calculated and displayed on the liquid crystal display. Measurement results show that the portable electrical detection system can detect DNA target concentrations as low as 0.16?µM. The detection of the amplified polymerase chain reaction sample and the reproducibility of the DNA sensor results were also determined using the designed readout circuit. The proposed electrical detection system is suitable for DNA sensor application.     

基于不同空间结构Hadamard融合的图像边缘提取算法

目的针对图像边缘提取算法中噪声对边缘的影响,易导致边缘定位精度不高,出现虚假边缘与漏检等不足,设计一种不同空间结构Hadamard融合的图像边缘提取方案。方法首先,通过计算像素与相邻点之间的方差来分析像素的结构,得到边缘点的最大概率分布矩阵(MPDM),利用MPDM来表示候选边缘集。其次,通过分析邻域点之间的亮度,计算像素与其4个相邻像素之间的最大和最小差值,得到相应的差异矩阵,并引入Logistic回归分析对2种矩阵归一化处理,得到一个权重矩阵(WM)。然后,通过Hadamard乘积模型将MPDM与WM进行融合,从而设计边缘分割阈值函数。最后,通过比较WM和分割阈值,去掉非边缘点,检测出真实图像边缘。结果实验表明,与当前边缘提取方法对比,文中方法能够有效抑制噪声,得到的边缘清晰、完整,边缘细化度与平滑度良好,在客观评价FOM与ROC中具有更大的优势。结论所提算法具有良好的边缘提取精度,在图像处理与包装条码领域具有良好的应用价值。     

Detecting three-dimensional location and shape of noisy distorted three-dimensional objects with ladar trained optimum nonlinear filters

We propose a filtering technique that uses laser radar (ladar) data to detect a target's three-dimensional (3D) coordinates and shape within an input scene. A two-dimensional ladar range image is converted into 3D space, and then the 3D optimum nonlinear filtering technique is used to detect the 3D coordinates of targets (including the target's distance from the sensor). The 3D optimum nonlinear filter is designed to detect distorted targets (i.e., out-of-plane and in-plane rotations and scale changes) and to be noise robust. The nonlinear filter is derived to minimize the mean of the output energy in response to the input scene in the presence of disjoint background noise and additive noise and to maintain a fixed output peak for the members of the true-class target training set. The system is tested with real ladar imagery in the presence of background clutter. The background clutter used in the system evaluation includes false objects that are similar to the true targets. The correlation output of ladar images shows a dominant peak at the target's 3D coordinates.     

A novel learning-based switching median filter for suppression of impulse noise in highly corrupted colour images

A novel switching median filter integrated with a learning-based noise detection method is proposed for suppression of impulse noise in highly corrupted colour images. Noise detection employs a new machine learning algorithm, called margin setting (MS), to detect noise pixels. MS detection is achieved by classifying noise and clean pixels with a decision surface. MS detection yields very high detection accuracy, i.e. a zero miss detection rate and a fairly low over detection rate for a wide range of noise levels. After noise detection, a new filter scheme called the noise-free two-stage (NFTS) filter is triggered. NFTS corrects the noise pixels using the median of the noise-free pixels in two stages. The results of experiments have demonstrated that the MS based NFTS (MSN) filter is superior to the support vector machine and neural network for denoising highly corrupted images, in terms of noise suppression and detail preservation.     

一种低信噪比环境下的语音端点检测算法

端点检测技术是语音信号处理的关键技术之一，为提高低信噪比环境下端点检测的准确率和稳健性，提出了一种非平稳噪声抑制和调制域谱减结合功率归一化倒谱距离的端点检测算法。该算法首先通过抑制非平稳噪声再采用调制域谱减消除残余噪声来提升信噪比，减少语音失真。然后再提取每帧信号的功率归一化倒谱系数，计算每帧信号与背景噪声的功率归一化倒谱距离。最后将该倒谱距离作为检测参数，采用双门限判决方法进行端点检测。实验结果表明，该端点检测算法对语音帧和噪声帧具有较好的区分性。此外，在低信噪比环境下，所提出的算法对于不同类型的噪声都具有较好的稳健性。     

Designing a holographic modal wavefront sensor for the detection of static ocular aberrations

The extent to which holographic modal wavefront sensing can be applied to the detection of ocular aberrations was investigated. First, the idea of extending the dynamic range of the sensor by increasing the mask bias and the collection area of the pinhole detectors used in the sensor is reviewed. Errors in the detection of single-mode aberrations owing to reduced coherence from retinal scattering, photon, readout, and quantization noise are evaluated. A sensitivity-to-noise metric is introduced to evaluate sensor designs and is found to be maximized by using a pinhole detector radius of 8.6(flambda/NDelta) for every wave of mask bias (where f=transform lens focal length, lambda=wavelength, and N and Delta are the number and size of the hologram pixels, respectively). The problem of detecting ocular aberrations composed of multiple modes required a generalization of the sensitivity measure to include all incident aberration modes. A "detect and correct" ocular aberration detection scheme was implemented that reduced the effects of cross talk and showed a maximum sensitivity-to-noise ratio of 40, which varied inversely with the size of the ocular aberration being detected.