调零天线中的空域极化域联合抗干扰算法

在期望信号功率较小情况下,强同频干扰容易造成系统灵敏度下降,比如卫星导航和卫星通信。采用一个4阵元正交极化阵列,可以通过空域和极化域联合方式对干扰进行抑制。阵列参考阵元位于圆心,其余3个阵元均匀分布在圆周上。自适应滤波采用功率倒置准则下的LMS算法,该方法调整每个正交极化阵元权系数,使得阵列在干扰极化下等效的极化匹配因子接近为0,实现干扰抑制。算法无需信号空间信息,实现简单,同时使阵列自由度达到6,从而增加抗干扰个数。计算机仿真证明了4个阵元阵列可以有效抑制最多6个干扰,而且,通过极化域处理,即使干扰信号与期望信号入射角度相同,只要极化不同,也可以抑制干扰而保持对期望信号增益。     

鲜香菇外部品质计算机视觉检测与分级研究

根据鲜香菇图像特点和分级标准,运用计算机视觉技术和神经网络算法对香菇进行自动检测与分级。采用掩模去背景、中值滤波、边缘亮度补偿等技术对图像进行处理。选取香菇菇盖最大直径、圆形度、色调均值及缺陷区域总面积与香菇图像总面积的比值作为鲜香菇分级的特征参数。通过BP神经网络建立了特征参数与鲜香菇等级之间的关系模型,试验结果表明,其预测识别结果达到94.2%。     

K近邻的自适应谱聚类快速算法

利用时域递归展开算法对墙体内热湿耦合传递方程进行求解。以木板为例,应用该算法进行了热湿耦合传递的分析计算,在时间域和空间域上分别运用递归展开法和控制容积法进行离散,从而得到递归形式的线性方程组,运用MATLAB软件对这一过程进行求解。计算结果与有限差分算法、解析解计算结果以及实验数据吻合良好,表明该算法能够用于求解多孔介质热湿耦合传递模型。时间步长的改变对计算结果影响较小,可通过增加时间步长方法来减少工作量。     

基于物候特征参数的橡胶树种植信息遥感提取研究?

为了更好地监测橡胶树的生长状态,客观准确地获取橡胶树的种植的地理位置信息,采用非对称高斯函数对海南岛内MODIS EVI时间序列数据进行滤波处理,定义作物物候特征参数并计算海南岛每个像元的特征参数值;统计分析基于MODIS EVI数据的海南岛橡胶树样区作物关键物候特征参数值变化规律;根据橡胶树样区特征参数值变化规律建立海南岛橡胶树种植信息提取算法,提取结果采用不同比例的橡胶树混合像元分类法表示。根据提取算法对海南岛2011年橡胶树种植信息进行提取,用验证数据对该提取算法进行精度检验与实用性评价,检验结果显示该算法的平均分类精度达85.97%,可以用来提取橡胶树种植信息。     

基于MERSI数据的处理及旬植被指数生成方法

为了实现自动批量处理国产FY-3A/B MERSI光学影像数据,提取全国范围植被指数特征,为今后全国植被类型制图方法研究做准备,对MERSI数据经过物理量计算、几何校正、拼接、掩膜等处理、提取植被指数特征和水云标识数据、按旬生成最大植被指数时间序列影像和合成旬水云标识影像等方法进行了研究;利用IDL 7.1编程语言实现了自动批量处理全国范围MERSI数据,按旬生成时间序列250 m空间分辨率的植被指数数据的算法程序;并对基于以上方法生成的归一化植被指数(NDVI)和增强性植被指数(EVI)结果进行比较分析。结果表明：NDVI的旬影像比EVI旬影像植被指数值分布更均匀,能够更好地反映全国植被分布与物候变化特点。该研究采用程序算法实现对FY-3A MERSI光学影像数据的处理,操作简单易行,节省了大量人力和时间;且生成的结果可为今后按MERSI NDVI时间序列影像进行全国植被类型分布研究、监测植被长势、开展国产数据在林业中的应用等后续工作做准备。     

烟叶化学成分协调性的近红外光谱检测模型研究

对烟粉全部近红外光谱数据采取不同的预处理方法来探究烟叶化学成分协调性（施木克值、糖碱比、氮碱比）,基于有效波长光谱数据建立相应的近红外光谱的检测模型,并利用偏最小二乘法(PLS)通过训练集的交叉验证建立回归模型。通过全部波长数据中值滤波平滑处理后建立施木克值的回归模型,预测集r=0.9861、RMSE=0.0548;通过全部波长数据卷积平滑二阶求导处理后建立糖碱比的回归模型,预测集r=0.9498、RMSE=0.9095;在对光谱数据处理较优的Norris一阶导数数组基础上选取30个有效波长建立氮碱比的回归模型,预测集r=0.9202、RMSE=0.6947。结果表明：利用近红外光谱可以较好地预测烤烟施木克值、糖碱比和氮碱比。     

机载 LiDAR生成DEM的一些关键问题评述

提取DEM是机载LiDAR技术应用研究关键内容之一,从LiDAR数据滤波、DEM模型选择、内插、误差控制和数据压缩等几个方面,分析当前利用机载激光雷达数据提取DEM的主要研究进展.着重讨论插值滤波、倾角滤波和形态学滤波算法,定量分析滤波算法的性能;分析比较确定性内插方法和地学统计内插方法,并指出内插方法在提高精度同时也要考虑稳定性和序同构性;论述DEM格网压缩和三角网压缩方法,并指出在满足地形可视化需要的同时提高压缩效率;并进行了总结展望,为相关研究提供参考.     

基于多重滤波的高分辨率影像中线性特征提取

在高分辨率遥感影像中,线性特征提取一直是个难题,难主要表现在线性地物的边缘信息和噪音是一对矛盾体,边缘信息越明显,噪音就越强。本文利用高通滤波和统计滤波器,构建一个多重滤波,对IKONOS影像进行处理,通过三种不同高通滤波窗口对IKONOS影像处理结果的比对,得到一个较好的高通滤波处理窗口,处理后的IKONOS影像边界特征突出、区域模糊程度减小,目标地物大致轮廓已经区分出来,在此基础上对影像进行统计滤波处理,噪音明显减少,灰度分布更加集中和平滑,对高通滤波和统计滤波处理后的图像进行边缘检测,经过统计滤波后的图像较只进行高通滤波后的图像检测结果较好。实验结果表明：滤波后的影像噪音明显降低,信噪比明显提高,统计滤波后的影像,能够提高检测精度,表明了从高分辨率卫星影像上自动提取矢量信息的可行性,并为边缘检测的细化与跟踪处理提供了可靠的数据来源。     

机器视觉检测鲜烟叶的分级装置设计

针对农民在烟叶送烤前对烟叶分级的非重视度和非客观性等问题,本文提出基于机器视觉技术的烟叶图像检测分类方法对烟叶编烟送烤前进行成熟度划分,设计了全自动化的鲜烟叶检测分级装置。该系统的机械结构由自动上样抓取烟叶机构、烟叶输送台、检测机构,分拣机构等四个部分组成。自动上样的烟叶在传送带上被CCD检测并进行图像处理。首先对图像的噪点采用邻域平均和中值滤波组合的方法进行区域去噪处理；使用最小误差阈值分割方法分离背景和烟叶,然后增强图像信号,提取感性区域的颜色信息。采用烟叶的4个特征信息(R,G,B颜色值和色调H值)来表征烟叶的级别特性。通过装置自主学习建立样本库,然后参考学习的样本库对未知样品进行检测分级。实验结果表明根据烟叶色泽的不同,相邻类型之间的色泽差异越大,分类准确度越高。检测分类的平均速度在2-3秒/片,满足现场即时检测要求。     

基于物联网的日光温室环境监测在线校准

温室环境条件特别是温度对于作物生长和发育具有十分显著的影响。日光温室调控的主要环境因子之一是温度。然而,自然环境下的光照对温度产生作用,影响空气温度的监测精度。采用机器学习中的支持向量机算法(SVM),对日光温室内的温度智能监测算法进行了研究,根据光照情况对实时监测的温度数据进行校准。通过与实验测量的数据进行对比分析,结果表明：所提出的监测方法可以较为准确地实时监测空气温度,从而无需使用隔热材料或者遮阳处理,就可以基于监测的数据更精确地对相应的环境因素进行调节。基于该方法,可采用常用的工业设备实现温室大棚内实时温度数据的监测,既可以节约设备和人力成本,又可以为温室控制提供准确的数据。     

A Fast Median Filtering Algorithm

A fast median filtering algorithm basing on the coherence of data in adjacent windows is presented.During median filtering,only the first median value is found by conventional squencing algorithm,the other median values are obtained by bisecting search method and quickinserting new element in sequenced window.For convenience of moving filter window along signal data,a data structure is desinged,which can make the new input element to cover output element and greatly reduce the quantity of computation.A 2-dimensional fast median filtering method by moving filter window along snack path is also introduced.     

A time-varying signal processing method for Coriolis mass flowmeter

The mass flowrate of Coriolis mass flowmeter in practical application has slow changes with time. To solve the problem, an improved time-varying signal model whose frequency, amplitude and phase are time-varying based on the random walk model is established firstly. A new algorithm of adaptive notch filter with the capability of tracking frequency variation is applied to filter the sensor output signal of Coriolis mass flowmeter and its frequency is calculated next. An adaptive line enhancer based on the mentioned notch filter extracts fundamental frequency signal from noisy data. Then, by short window intercepting, the revised sliding DTFT recursive algorithm is introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and phase difference obtained, the time interval between the two signals is calculated and then the mass flowrate is derived. The simulations and field test results show that the proposed method can not only track the change of frequency and phase, but also ensure the calculation accuracy when measuring small phase difference. The computational load of the algorithm is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.     

Digital Filtering with Zero Phase Error

Aiming at the problem of phase error caused by normal filters in signal processing , summarizes the important meaning of digital filtering with zero phase error. Puts forward RRF Filtering which is a new way to realize digital filtering with zero phase error through reversing the time serials of the signal. Another way to realize digital filtering with zero phase error is suggested through reversing the time serials of normal filter. Testifies the way to realize digital filtering with zero phase error. Points out how to design the digital filter with zero phase frequency peculiarity. Solves the problem of phase error in DSP.     

The Application of Narrowband Interference Suppression in Direct Sequence Spread Spectrum

In order to improve the system effect, it is necessary take the technique of digital signal process to suppress interference, because of the finite gain and the narrow-band interference in system of direct sequence spread spectrum. The authors design a new filter structure improving on tunable digital heterodyne adaptive notch filters. This filter can adaptively track the interference frequency and control the bandwidth and depth of notch, which has the excellent agility and utility. The experiment proves this method overcomes limitation of other interference suppressions, hardly weaken the system performances, enhances the stability of the filter system, and can quickly track the interference frequency.     

Design of nondestructive test system for plasma clots

One of the most important measures that are used to guarantee blood transfusion safety is to detect clots in the plasma before transfusion. To overcome the disadvantages of manual detection method, this research designs a nondestructive testing (NDT) system for plasma clots inspection based on machine vision technique and artificial neural networks. The key technology for system design are studied and presented. Image acquisition is performed by custom-designed software based on MATLAB platform, and the methods of image cut, reverse color, median filter as well as gray cutting are adopted to preprocess image. The use of fisher discrimination method, combined with iterative threshold segmentation method and the selection of connected domain, can successfully eliminate the interference of air bubble and correctly extract the image of plasma clots. Plasma clots are discriminated by a recognition model based on artificial neural network BP algorithms. The results of clinical contrast experiment shows that the system can effectively detect whether plasma contains plasma clots and the new system shows a much higher degree of repeatability and stability. From the image acquisition and processing to the recognition of plasma clots, the detecting time of a sample is no more than 1 min.     

Improved Sage-Husa algorithm and its application in industrial online measurement

An improved Sage-Husa adaptive filter algorithm is proposed based on the existing methodologies in light of the complex and changing environment of the industrial online measurement. This algorithm,after a new observed value is obtained,first judges whether the filter is in a normal state by the filtering divergence criterion,if not,it judges whether the new observed value is an outlier and takes countermeasures varying with the judgment so as to improve the performance of the Sage-Husa algorithm in industrial measurement. The result indicates that compared with traditional methodologies,this new algorithm is applicable to measure the location of the center point of laser beam in a simulated industrial environment,makes accurate judgment about the system status,features better precision,practicality and robustness and thus can satisfy the requirements of industrial online measurement.     

Temporal-spatial Filtering in the Preprocessing of Clinical EEG Signals

EEG spot-recording for clinical patients in the bed is always advocated due to easing patients and making them comfortable. But EEG signals recorded in the ward exposed to various noisesand interference is less effective in the EEG analysis. Atemporal-spatial filter is designed for noise removing of clinical EEG signals, the first filtering is temporal filtering using band-pass filter, and the second filtering is spatial filtering with ICA-based spatial filter. Experimental results indicate that various noises and interferences, such as power interference, blinking, eyes movements, muscle moments, ECG artifacts, etc., are removed effectively, individually or simultaneously.     

Real time monitoring for multivariate statistical process with on line multiscale filtering

By analyzing shortages of current MSPCA model, an on line multi variable statistical process monitoring method is proposed, which uses some concepts from online multi scale filtering and can be applied to sensor fault diagnosis. In the method, wavelet decomposition is employed to the signals using edge correction filter in a fixed length data window, and then wavelet denoising is conducted with wavelet threshold filtering. Next, an on line multi scale model is constructed for data combining wavelet transformation and adaptive PCA in the previous data window. This model avoids time waste in direct signal denoising and reduces time cost in multi scale data with conventional PCA, which eventually increases accuracy in fault diagnosis. Experiments on eight vibration signals of 6135D diesel engine under severe leak condition prove the practicability and feasibility of the proposed method.     

Signal Processing Method for Coriolis Mass Flowmeter Based on Lattice Notch Filter and DTFT

A novel signal processing method for Coriolis mass flowmeter is proposed based on time-varying signal model. First, an adaptive lattice notch filter is applied to filter the output signal, whose frequency, amplitude and phase are time-varying based on the random walk model, of Coriolis mass flowmeter to get its frequency and enhanced signal. Then, by short window intercepting, the DTFT algorithm with negative frequency contribution is introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference, the time interval between two signals is calculated. Simulation results show that the proposed method is efficient. Furthermore, the computation of algorithms is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.     

Single sample face recognition based on B-spline and image gradient

A single sample face recognition algorithm based on B-spline and image gradient is proposed. Image gradient method for face recognition has advantage of illumination invariant. But the recognition rate will be greatly decreased when the image contains noise which will seriously influence gradient information. Traditional methods to reduce noise smooth image at the same time and image gradient reorganization rate will be reduced. As the B-spline filter has the feature which can adjust the order, B-spline filter with different orders can be selected according to the image noise value to minimize noise while preserve image gradient information. Experiments prove that using B-spline and image gradient algorithm can achieve a better recognition rate than traditional filtering method on single sample face recognition problem.