A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery

Plant foliage density expressed as leaf area index (LAI) is used in many ecological, meteorological, and agronomic models, and as a means of quantifying crop spatial variability for precision farming. LAI retrieval using spectral vegetation indices (SVI) from optical remotely sensed data usually requires site-specific calibration values from the surface or the use of within-scene image information without surface calibrations to invert radiative transfer models. An evaluation of LAI retrieval methods was conducted using (1) empirical methods employing the normalized difference vegetation index (NDVI) and a new SVI that uses green wavelength reflectance, (2) a scaled NDVI approach that uses no calibration measurements, and (3) a hybrid approach that uses a neural network (NN) and a radiative transfer model without site-specific calibration measurements. While research has shown that under a variety of conditions NDVI is not optimal for LAI retrieval, its continued use for remote sensing applications and in analysis seeking to develop improved parameter retrieval algorithms based on NDVI suggests its value as a “benchmark” or standard against which other methods can be compared. Landsat-7 ETM+ data for July 1 and July 8 from the Soil Moisture EXperiment 2002 (SMEX02) field campaign in the Walnut Creek watershed south of Ames, IA, were used for the analysis. Sun photometer data collected from a site within the watershed were used to atmospherically correct the imagery to surface reflectance. LAI validation measurements of corn and soybeans were collected close to the dates of the Landsat-7 overpasses. Comparable results were obtained with the empirical SVI methods and the scaled SVI method within each date. The hybrid method, although promising, did not account for as much of the variability as the SVI methods. Higher atmospheric optical depths for July 8 leading to surface reflectance errors are believed to have resulted in overall poorer performance for this date. Use of SVIs employing green wavelengths, improved method for the definition of image minimum and maximum clusters used by the scaled NDVI method, and further development of a soil reflectance index used by the hybrid NN approach are warranted. More importantly, the results demonstrate that reasonable LAI estimates are possible using optical remote sensing methods without in situ, site-specific calibration measurements.     

基于FPGA的次声波合成的电路设计

次声波是频率在10-6~20 Hz的波,在生物医学、气象学、军事等领域有着广泛应用前景,此频段的信号无法通过天线直接发射。应用DDS原理产生2路相干超声波,在空间干涉产生次声波。主要完成了基于FPGA的DDS电路设计,并且完成了系统软、硬件设计,通过Max PlusⅡ和Matlab的仿真表明该设计的原理正确、方案可行。     

基于DSP Builder的NCO优化设计

首先介绍了数控振荡器（NCO,Numerically Controlled Oscillator）的原理和特点,研究了用DSP Builder实现数控振荡器的传统设计方法,并在此基础上提出了一种使用1/4周期存储的优化方法,并将其与整周期存储方法进行仿真比较。相对于传统整周期存储,采用1/4周期存储方法可以在不增加额外只读存储器（ROM,Read Only Memory）存储空间的情况下提高了频率分辨率。该方法在Matlab/Simulink中得以实现,在ModelSim中进行了功能仿真之后将设计模型下载到FPGA进行硬件仿真。     

基于多项式与查找表的预失真技术研究

为补偿功率放大器固有的非线性,提出了一种新型的预失真器。该预失真器结合查找表和多项式两种常用的预失真方法实现,在查找表中存储分段低阶多项式系数,根据输入信号的幅度查表得到相应的多项式系数对输入信号进行预失真处理。仿真结果表明,该预失真器对互调失真（IMD）的改善优于多项式预失真器,能有效改善信号因功放非线性失真而导致的信号星座图扭曲及频谱再生,提高通信系统的性能。     

Transformed HCT for parallel Huffman decoding

This paper proposes a novel and generic Huffman code table (HCT) transform and a simple parallel Huffman decoding method. Codes of the original HCT are left‐aligned, reordered in value, and partitioned into sub‐bands. Two kinds of modification to the codes are introduced in order to reduce the number of sub‐bands. The Huffman decoder can be implemented with a minimized size of single LUT, and the parallel decoding can be completed easily, at a constant rate of up to one code per cycle. An example of MP3 decoder and AAC decoder has been designed to demonstrate the efficiency of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.     

基于FPGA的反正切函数的优化算法

主要描述了一种基于FPGA利用Verilog HDL实现的反正切函数计算的优化算法.反正切函数的计算在相位检测,偏振光检测等检测系统有重要的应用.讨论了泰勒展开式法和直接LUT查找表法缺点,提出一种新的优化LUT查找表算法.该算法能够被综合器综合实现,可以直接集成在FPGA应用系统、SOC系统或SOPC系统上,并且经软件仿真和硬件验证,达到了设计要求.     

YCbCr空间中一种基于贝叶斯判决的肤色检测方法

皮肤颜色是人脸检测、定位、跟踪时的一种十分有效的特征,而且裸露的皮肤区域也是色情图像的最重要特征之一.为了有效地进行图像的皮肤检测,提出了一种新的肤色检测方法.该方法首先通过统计1809 502个肤色像素点和1763682个非肤色像素点,并使用贝叶斯规则来建立肤色分类器;然后考虑亮度对肤色的影响,采用Y-Cb和Y-Cr两个子空间的查询表来建立肤色模型.为了联合使用两个查询表,先采用高斯归一化和线性化方法来将阈值范围调整至[0,1];同时对查询表进行中值滤波处理,以除去离散孤立点.实验表明,与其他3种方法相比,该方法不仅有着较低的漏检率（9.814%）和误检率（3.5%）,而且对于不同光照条件也有较好的检测效果.