一种大尺度Gauss模糊的快速采样算法

针对大模板Gauss模糊的传统算法计算复杂度较高的缺点,提出了一种基于快速采样的新算法,将计算复杂度从O(N2)降到O(N)级别.利用极大似然估计理论,对传统Gauss模板和采样模板进行分析,给出了采样算法和传统算法的联系与区别.实验表明,新算法在极大的提高算法的速度的同时,可以将两者之间的能量误差控制在1%左右,当采样点等于10N时,人眼基本无法察觉两种算法的差异.     

基于谓词的无线传感器网络水质达标采样算法

水质达标检测是水资源优化利用的基础。利用无线传感器对水质进行采样,通过无线传感器网络将达标结果发送给基站进行融合处理,数据服务器在接收到采样数据后实施应用决策。在无线传感器网络、基站和数据服务器三层结构的基础上讨论基于谓词的水质达标采样算法,对算法进行分析、设计和仿真。实验结果表明,该算法实用且高效。     

AD0809与微机直接互连采样分析

在单片机外围扩展中,模拟至数字转换器（ADC）常用的标准电路在采样芯片和单片机之间需要多个元件对信号进行转换,虽然精度很高,但结构复杂,成本较高。该文对某些特定条件下用单片机进行ADC采样的方法进行了初步的探索,利用单片机直接与ADC0809进行连接,通过单片机l产生各种时钟和控制信号来完成ADC采样,简化了采样的线路。对比了该电路与传统电路的差别,并讨论了该采样方法的使用条件和应用参数。     

AD73322与C54X DSP接口设计

在语音设备的设计过程中,有一重要环节就是选择合适的模拟通道器件与DSP配合工作;在所完成的某小型便携式语音终端的硬件平台开发中,选取TMS320C5416用于语音编解码等复杂语音信号处理;而在众多可以与该DSP配合的AD、DA器件中,集A/D和D/A转换于一身的AD73322成为最好的选择;文中以实际设计案例给出了单片AD73322及多片AD73322级联(外部音频通道较多的场合)与TMS320C5416芯片配合使用的硬件连接方法和编程策略;对于类似的DSP+AD/DA硬件平台设计具有普遍的参考意义。     

随机采样的2DPCA人脸识别方法

在2DPCA的基础上提出一种随机采样的2DPCA人脸识别方法--RRS-2DPCA.同传统通过对特征或投影向量进行采样的方法不同的是,RRS-2DPCA(Row Random Sampling 2DPCA)将随机采样建立于图像的行向量集中,然后在行向量子集中执行2DPCA.在ORL、Yale和AR人脸数据集上进行实验,结果表明RRS-2DPCA不仅具很好的识别性能和运算效率,而且对参数具有很大的稳定性.另外针对2DPCA和RRS-2DPCA对光线、遮挡等不鲁棒问题,进一步提出了局部区域随机采样的2DPCA方法LRRS-2DPCA(Local Row Random Sampling 2DPCA),将RRS-2DPCA执行在人脸图像的局部区域中.实验结果表明LRRS-2DPCA不仅具有较好的鲁棒性更大大的提高了RRS-2DPCA的识别性能.     

Corrective classification: Learning from data imperfections with aggressive and diverse classifier ensembling

Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms to avoid developing overly complicated structures that overfit the noise. The essential goal is to reduce noise impact and eventually enhance the learners built from noise-corrupted data. In this paper, we propose a novel corrective classification (C2) design, which incorporates data cleansing, error correction, Bootstrap sampling and classifier ensembling for effective learning from noisy data sources. C2 differs from existing classifier ensembling or robust learning algorithms in two aspects. On one hand, a set of diverse base learners of C2 constituting the ensemble are constructed via a Bootstrap sampling process; on the other hand, C2 further improves each base learner by unifying error detection, correction and data cleansing to reduce noise impact. Being corrective, the classifier ensemble is built from data preprocessed/corrected by the data cleansing and correcting modules. Experimental comparisons demonstrate that C2 is not only more accurate than the learner built from original noisy sources, but also more reliable than Bagging [4] or aggressive classifier ensemble (ACE) [56], which are two degenerated components/variants of C2. The comparisons also indicate that C2 is more stable than Boosting and DECORATE, which are two state-of-the-art ensembling methods. For real-world imperfect information sources (i.e. noisy training and/or test data), C2 is able to deliver more accurate and reliable prediction models than its other peers can offer.     

Study on design and application of fully automatic miniature surface plasmon resonance concentration analyzer

A fully automatic miniature surface plasmon resonance (SPR) concentration analyzer having high performance and low cost and developed using a Spreeta™ sensor was designed for field applications and concentration analysis. As in the case of Biacore™ instruments, the automatic sampling system of this device can introduce air segments between the sample/regeneration solution and buffer solution in the pipeline, which effectively prevents mixing of the solutions. A temperature sensor (AD 590) and temperature compensation method are used, which make the device insensitive to temperature fluctuations. A real-time data-smoothing algorithm for the SPR detection data is adopted; this can reduce the noise level to 5 × 10⁻⁷ RIU (refractive index units). The noise level of the sensorgram is 3.5% of the original level. Two types of self-prepared sensing chips—SMX-BSA (bovine serum albumin coated with sulfamethoxazole) and SMX-CM5 (carboxymethyl dextran coated with sulfamethoxazole)—are used to analyze the concentrations of sulfamethoxazole (SMX) standard solutions. Each chip's SMX calibration curve is established within the measurement range of 0-2000 ng/ml, and both limits of detection (LOD) are 2 ng/ml. One cycle of assay time is less than 15 min.     

Monte Carlo simulations of systems with complex energy landscapes

Non-traditional Monte Carlo simulations are a powerful approach to the study of systems with complex energy landscapes. After reviewing several of these specialized algorithms we shall describe the behavior of typical systems including spin glasses, lattice proteins, and models for “real” proteins. In the Edwards-Anderson spin glass it is now possible to produce probability distributions in the canonical ensemble and thermodynamic results of high numerical quality. In the hydrophobic-polar (HP) lattice protein model Wang-Landau sampling with an improved move set (pull-moves) produces results of very high quality. These can be compared with the results of other methods of statistical physics. A more realistic membrane protein model for Glycophorin A is also examined. Wang-Landau sampling allows the study of the dimerization process including an elucidation of the nature of the process.     

Impact of orthorectification and spatial sampling on maximum NDVI composite data in mountain regions

Topography and accuracy of image geometric registration significantly affect the quality of satellite data, since pixels are displaced depending on surface elevation and viewing geometry. This effect should be corrected for through the process of accurate image navigation and orthorectification in order to meet the geolocation accuracy for systematic observations specified by the Global Climate Observing System (GCOS) requirements for satellite climate data records. We investigated the impact of orthorectification on the accuracy of maximum Normalized Difference Vegetation Index (NDVI) composite data for a mountain region in north-western Canada at various spatial resolutions (1 km, 4 km, 5 km, and 8 km). Data from AVHRR on board NOAA-11 (1989 and 1990) and NOAA-16 (2001, 2002, and 2003) processed using a system called CAPS (Canadian AVHRR Processing System) for the month of August were considered. Results demonstrate the significant impact of orthorectification on the quality of composite NDVI data in mountainous terrain. Differences between orthorectified and non-orthorectified NDVI composites (ΔNDVI) adopted both large positive and negative values, with the 1% and 99% percentiles of ΔNDVI at 1 km resolution spanning values between − 0.16 < ΔNDVI < 0.09. Differences were generally reduced to smaller numbers for coarser resolution data, but systematic positive biases for non-orthorectified composites were obtained at all spatial resolutions, ranging from 0.02 (1 km) to 0.004 (8 km). Analyzing the power spectra of maximum NDVI composites at 1 km resolution, large differences between orthorectified and non-orthorectified AVHRR data were identified at spatial scales between 4 km and 10 km. Validation of NOAA-16 AVHRR NDVI with MODIS NDVI composites revealed higher correlation coefficients (by up to 0.1) for orthorectified composites relative to the non-orthorectified case. Uncertainties due to the AVHRR Global Area Coverage (GAC) sampling scheme introduce an average positive bias of 0.02 ± 0.03 at maximum NDVI composite level that translates into an average relative bias of 10.6% ± 19.1 for sparsely vegetated mountain regions. This can at least partially explain the systematic average positive biases we observed relative to our results in AVHRR GAC-based composites from the Global Inventory Modeling and Mapping Studies (GIMMS) and Polar Pathfinder (PPF) datasets (0.19 and 0.05, respectively). With regard to the generation of AVHRR long-term climate data records, results suggest that orthorectification should be an integral part of AVHRR pre-processing, since neglecting the terrain displacement effect may lead to important biases and additional noise in time series at various spatial scales.     

Simulation and computation of the asymmetry of a clocked balanced tunnel-diode comparator

By means of simulation and computation, a clocked balanced tunnel-diode comparator, in which the clock signal conditioner is also a tunnel diode, is studied. Simulation and computation of the tunnel-diode asymmetry required for obtaining a minimum offset of the zero level of the compensation voltage are carried out. Simulation of the clocked balanced comparator is performed by means of the Tanner T-Spice program.