针对被动微波AMSR-E数据的土壤水分反演算法

提出了一种针对被动微波AMSR-E数据的土壤水分反演算法。用改进的积分方程模型(AIEM)模拟表明:在给定的粗糙度条件下,土壤水分和发射率之间有很好的线性关系;18.7GHz与10.7GHz的垂直极化(V)微波指数与土壤水分有很好的关系,而且部分消除了土壤粗糙度的影响,相关系数的平方(R-Square)是0.98。因此,建立标准化的微波指数与土壤水分之间的关系是可行的。算法敏感性分析表明,当有降雨时此算法比较敏感。相对于全国农业遥感地面监测东北网点县实测数据,算法平均误差大约是21.5%。此算法低估了土壤水分,用实测数据对反演结果做进一步修正后的误差为7.4%。用AMSR-E数据对2009年2月1日中国主要陆地表面进行了实际反演分析,结果表明反演结果符合实际土壤水分的分布情况,表明算法可行。     

利用AMSR-E被动微波数据反演地表温度的神经网络算法

结合对地观测卫星AQUA多传感器/多分辨率的特点,研究了利用AMSR-E被动微波数据反演地表温度的神经网络算法.MODIS地表温度(LST)产品被作为地表温度实测数据,对应的平均温度被用作对应AMSR-E像元的实际地表温度,从而克服由于AMSR-E像元尺度太大和云的影响而难以获得地表实测数据的难点.反演结果分析表明,利用神经网络能够精确地由AMSR-E数据反演地表温度.当使用5个频率10个通道反演时,反演精度最高,说明使用更多的通道能更好地消除土壤水分、粗糙度、大气和其它因素的影响.相对于MODIS温度产品,用此算法反演的平均误差约低于2K.     

A spline-based algorithm for continuous time-delay estimation using sampled data

Time delay estimation (TDE) lies at the heart of signal processing algorithms in a broad range of application areas, including communications, coherent imaging, speech processing, and acoustics. In medical ultrasound for example, TDE is used in blood flow estimation, tissue motion measurement, tissue elasticity estimation, phase aberration correction, and a number of other algorithms. Because of its central significance, TDE accuracy, precision, and computational cost are of critical importance. Furthermore, because TDE is typically performed on sampled signals-and delay estimates are usually desired over a continuous domain-time delay estimator performance should be considered in conjunction with associated interpolation. In this paper we present a new time-delay estimator that directly determines continuous time-delay estimates from sampled data. The technique forms a spline-based, piecewise continuous representation of the reference signal then solves for the minimum of the sum squared error between the reference and the delayed signals to determine their relative time delay. Computer simulation results clearly show that the proposed algorithm significantly outperforms other algorithms in terms of jitter and bias over a broad range of conditions. We also describe a modified version of the algorithm that includes companding with only a minor increase in computational cost.     

海面噪声垂直相干特性反演地声参数

声学反演是快速、低成本获取浅海局部地声参数的有效方法之一,其中,利用海面噪声的垂直相干函数反演海底参数,只需要两个垂直排列的水听器,不需要专门的发射声源,隐蔽性好,有较高的军事应用价值.通过已有的海面噪声场模型,分析了海底声速、密度及衰减系数对噪声垂直相干函数的敏感度,并利用中国某海区的海洋环境噪声试验数据,结合差异进...     

Fusion algorithm for poultry skin tumor detection using hyperspectral data

We consider a feature selection method to detect skin tumors on chicken carcasses using hyperspectral (HS) reflectance data. Detection of chicken tumors is difficult because the tumors vary in size and shape; some tumors are small, early-stage tumor spots. We make use of the fact that a chicken skin tumor consists of a lesion region surrounded by a region of thickened skin and that the spectral responses of the lesion and the thickened-skin regions of tumors are considerably different and train our feature selection algorithm to separately detect lesion regions and thickened-skin regions; we then fuse the two HS detection results to reduce false alarms. To the best of our knowledge, these techniques are new. Our forward selection and modified branch and bound algorithm is used to select a small number of lambda spectral features that are useful for discrimination. Initial results show that our method offers promise for a good tumor detection rate and a low false alarm rate.     

A new algorithm for minimizing surplus parts in selective assembly by using genetic algorithm

Precision assemblies are produced from low precision subcomponents by partitioning and assembling them randomly from their corresponding groups. Surplus part is one of the important issues, which reduces the implementation of selective assembly in real situations. A new algorithm is introduced in this present paper to reduce surplus parts almost to zero and it is achieved in two stages by using a genetic algorithm. For demonstrating the proposed algorithm, a gearbox shaft assembly is considered as an example problem in which the shaft and pulley are manufactured in wider tolerance and partitioned in three to nine bins. The surplus parts are divided into three bins equally and a best combination of groups is obtained for both cases. It is observed that nearly 995 assemblies are produced out of one thousand subcomponents with the manufacturing cost savings of 19.5% for T _max and 992 assemblies are produced with 13.5% saving in manufacturing cost for 0.9T _max.     

利用直达波和海面反射波到达时延反演深海声速剖面

研究了利用直达波与海面反射波到达时延反演深海声速剖面的方法,采用最严经验模态方法组织利用历史水文数据并进行反演,并利用 2016年南海中南部冬季调查的实验数据进行了验证。反演结果与实测声速剖面在深度小于 710 m处的平均绝对误差的最大值为 2.15 m·s^-1,反演结果与实测值基本相符。     

Identification of sporulated and vegetative bacteria using statistical analysis of fourier transform mid-infrared transmission data

A combined mid-infrared spectroscopic/statistical modeling approach for the discrimination and identification, at the strain level, of both sporulated and vegetative bacterial samples is presented. Transmission mode spectra of bacteria dried on ZnSe windows were collected using a Fourier transform mid-infrared (FT-IR) spectrometer. Five Bacillus bacterial strains (B. atrophaeus 49337, B. globigii Dugway, B. thuringiensis spp. kurstaki 35866, B. subtilis 49760, and B. subtilis 6051) were used to construct a reference spectral library and to parameterize a four-step statistical model for the systematic identification of bacteria. The statistical methods used in this initial feasibility study included principal component analysis (PCA), classification and regression trees (CART), and Mahalanobis distance calculations. Internal cross-validation studies successfully classified 100% of the samples into their correct physiological state (sporulated or vegetative) and identified 67% of the samples correctly as to their bacterial strain. Analysis of thirteen blind samples, which included reference and other bacteria, nonbiological materials, and mixtures of both nonbiological and bacterial samples, yielded comparable accuracy. The primary advantage of this approach is the accurate identification of unknown bacteria, including spores, in a matter of minutes.     

A data analysis algorithm for programmed field-flow fractionation

An algorithm that employs numerical integration for analysis of field-flow fractionation (FFF) data is presented. The algorithm utilizes detector response, field strength, and channel flow rate data, monitored at discrete time intervals during sample elution to generate a distribution of sample components according to particle size or molecular weight. The field strength and channel flow rate may either be held constant or programmed as functions of time, and it is not necessary for these programs to follow specific mathematical functions. If experimental conditions are monitored during a run, the algorithm can account for any deviation from nominal set conditions. The algorithm also allows calculation of fractionating power for the actual conditions as monitored during the run. The method provides greatly increased flexibility in the application of the FFF family of techniques. It removes the limitations on experimental conditions incurred by adherence to analytically available solutions to FFF theory, allowing ad hoc variation of field strength and other experimental parameters as necessary to increase sensitivity and specificity of the method. An implementation of the algorithm is described that is independent of the FFF technique (i.e., independent of field type) and mode of operation. To reduce computation time, it uses mathematical techniques to reduce the required number of numerical integrations. This is of particular importance when the perturbations to ideal FFF theory, such as those due to the effects of hydrodynamic lift forces, particle-wall or particle-particle interactions, and secondary relaxation, necessitate relatively lengthy numerical calculations.     

多普勒计程仪的数据降噪算法

多普勒计程仪输出的船速数据中含有偏差较大的点,即野点,在数据处理时必须将其去除,否则可能会导致组合导航系统中的卡尔曼滤波发散。同时输出数据由于受到随机误差的影响,会导致数据的平滑性能变差。提出了一种多普勒计程仪的数据降噪算法,该算法首先利用改进的中值滤波方法去除数据中的野点,再利用小波阈收缩去噪算法去除随机误差。仿真结果表明,与传统的中值滤波相比,该算法能极大地提高处理增益,并且有很高的应用价值。     

Automated test data generation for branch testing using incremental genetic algorithm

Cost of software testing can be reduced by automated test data generation to find a minimal set of data that has maximum coverage. Search-based software testing (SBST) is one of the techniques recently used for automated testing task. SBST makes use of control flow graph (CFG) and meta-heuristic search algorithms to accomplish the process. This paper focuses on test data generation for branch coverage. A major drawback in using meta-heuristic techniques is that the CFG paths have to be traversed from the starting node to end node for each automated test data. This kind of traversal could be improved by branch ordering, together with elitism. But still the population size and the number of iterations are maintained as the same to keep all the branches alive. In this paper, we present an incremental genetic algorithm (IGA) for branch coverage testing. Initially, a classical genetic algorithm (GA) is used to construct the population with the best parents for each branch node, and the IGA is started with these parents as the initial population. Hence, it is not necessary to maintain a huge population size and large number of iterations to cover all the branches. The performance is analyzed with five benchmark programs studied from the literature. The experimental results indicate that the proposed IGA search technique outperforms the other meta-heuristic search techniques in terms of memory usage and scalability.     

Trend analysis of the power law process using Expectation-Maximization algorithm for data censored by inspection intervals

Trend analysis is a common statistical method used to investigate the operation and changes of a repairable system over time. This method takes historical failure data of a system or a group of similar systems and determines whether the recurrent failures exhibit an increasing or decreasing trend. Most trend analysis methods proposed in the literature assume that the failure times are known, so the failure data is statistically complete; however, in many situations, such as hidden failures, failure times are subject to censoring. In this paper we assume that the failure process of a group of similar independent repairable units follows a non-homogenous Poisson process with a power law intensity function. Moreover, the failure data are subject to left, interval and right censoring. The paper proposes using the likelihood ratio test to check for trends in the failure data. It uses the Expectation-Maximization (EM) algorithm to find the parameters, which maximize the data likelihood in the case of null and alternative hypotheses. A recursive procedure is used to solve the main technical problem of calculating the expected values in the Expectation step. The proposed method is applied to a hospital's maintenance data for trend analysis of the components of a general infusion pump.     

一种适用于流数据分析的快速EMD算法

采用具有局域控制特性的B样条函数直接拟合均值线,采用局域标准差终止准则判断原型模态函数（Proto-mode Function,PMF）的对称性,得到一种适用于流数据分析的快速经验模态分解（Empirical Mode Decomposition,EMD）算法。相比经典EMD算法,该快速算法不仅时间复杂度和空间复杂度均有所下降,而且分解精度基本维持不变。该快速算法适用于战场侦察传感网等流数据分析场合,实时性好,能够得到与序列等长的连贯本征模态函数（Intrinsic Mode Function,IMF）。     

Computationally efficient method for retrieving aerosol optical depth from ATSR-2 and AATSR data

We present a robust and computationally efficient method for retrieving aerosol optical depth (AOD) from top-of-atmosphere ATSR-2 (Along-Track Scanning Radiometer) and AATSR (Advanced ATSR) reflectance data that is formulated to allow retrieval of the AOD from the 11 year archive of (A)ATSR data on the global scale. The approach uses a physical model of light scattering that requires no a priori information on the land surface. Computational efficiency is achieved by using precalculated lookup tables (LUTs) for the numerical inversion of a radiative-transfer model of the atmosphere. Estimates of AOD retrieved by the LUT approach are tested on AATSR data for a range of global land surfaces and are shown to be highly correlated with sunphotometer measurements of the AOD at 550 nm. (Pearson's correlation coefficient r(2) is 0.71.).     

New frontiers for mid-infrared sensors: towards deep sea monitoring with a submarine FT-IR sensor system

A sub-sea deployable fiber-optic sensor system for the continuous determination of a range of environmentally relevant volatile organic compounds in seawater has been developed. The prototype of a robust, miniaturized Fourier transform infrared (FT-IR) spectrometer for in situ underwater pollution monitoring was designed, developed, and built in our research group. The assembled instrument is enclosed in a sealed aluminium pressure vessel and is capable of maintenance-free operation in an oceanic environment down to depths of at least 300 m. The whole system can be incorporated either in a tow frame or a remotely operated vehicle (ROV). A suitable fiber-optic sensor head was developed, optimized in terms of sensitivity and hydrodynamics, and connected to the underwater FT-IR spectrometer. Due to a modular system design, various other sensor head configurations could be realized and tested, ensuring facile adaptation of the instrument to future tasks. The sensor system was characterized in a series of laboratory and simulated field tests. The sensor proved to be capable of quantitatively detecting a range of chlorinated hydrocarbons and monocyclic aromatic hydrocarbons in seawater down to the low ppb (microg/L) concentration range, including mixtures of up to 6 components. It has been demonstrated that varying amounts of salinity, turbidity, or humic acids, as well as interfering seawater pollutants, such as aliphatic hydrocarbons or phenols, do not significantly influence the sensor characteristics. In addition, the sensor exhibits sufficient long-time stability and a low susceptibility to sensor fouling.     

A surface fitting method for three dimensional scattered data

A method is presented that may be used to empirically establish the type of relationship that is present between a response variable and its influencing factors, by fitting a mathematical model to three dimensional scattered data. The generated response surface is composed of continuous triangular planes that are fitted to the corresponding data in the least squares sense. The method may be easily implemented. It requires some fairly large number of scattered data, two initial boundary conditions and a desired accuracy for the band-wise partitioning of the data. The proposed surface fitting technique has been successfully applied to solar radiation modelling for a number of different data combinations.     

A numerical method for the determination of optimal surface temperatures of steel ingots

A numerical method is presented for the determination of control temperatures to be applied to the surface of a steel ingot to induce a predetermined temperature distribution within the ingot. The one-dimensional case of an infinite plate is considered and the problem converted to one of static optimization which is then solved using a partial quadratic interpolation technique.     

A wavelet-based multisensor data fusion algorithm

This paper presents a wavelet transform-based data fusion algorithm for multisensor systems. With this algorithm, the optimum estimate of a measurand can be obtained in terms of minimum mean square error (MMSE). The variance of the optimum estimate is not only smaller than that of each observation sequence but also smaller than the arithmetic average estimate. To implement this algorithm, the variance of each observation sequence is estimated using the wavelet transform, and the optimum weighting factor to each observation is obtained accordingly. Since the variance of each observation sequence is estimated only from its most recent data of a predetermined length, the algorithm is self-adaptive. This algorithm is applicable to both static and dynamic systems including time-invariant and time-varying processes. The effectiveness of the algorithm is demonstrated using a piecewise-smooth signal and an actual time-varying flow signal.