Invariant image reconstruction from irregular samples and hexagonal grid splines

We present an algorithm for the reconstruction of images from irregularly placed samples, using linear splines with control points on a hexagonal grid. Several spline approximations are computed for different transformations of the control point grid (e.g. translations and rotations). These approximations are then merged together after compensation of the transformations, yielding a high-quality invariant image reconstruction. Evaluations show that the use of hexagonal grids of the “invariance by integration” principle improves reconstruction quality. An application to image coding is also presented.     

便携式时域反射仪硬件系统设计

介绍了用于土壤参数测量的时域反射仪硬件系统的设计与实现方案,并研制出样机TDR-I。该样机使用DSP做主控板,利用阶跃恢复二极管(SRD)产生140ps上升沿的快沿发射脉冲,采用快慢斜波比较法获得等效采样步进精度为8.7ps的取样脉冲。经过实验数据获取验证,该系统测量效果良好。     

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。     

基于压缩感知理论的图像压缩初步研究

在传统的压缩编码技术中,采样均遵循奈奎斯特定律,该定律规定采样速率要高于原信号频率的两倍。针对这一方法无法克服的巨大计算量及资源浪费,将最近提出的压缩感知理论用于图像压缩编码,可大大降低采样速率,该文着重讨论了基于压缩感知理论的图像压缩算法,仿真实验证明了这一算法的可行性。     

等间距采样的高速数据采集系统设计

简单介绍通过对窄脉冲等间距采样来测试电缆故障的基本原理,分析其脉冲的特点和处理要求;采用FPGA和MSP430F149作为主控芯片,设计了单路多次低速数据采集系统;利用QuartusⅡ软件编写主控程序,并在Modelsim下进行仿真验证。实验结果表明,该系统方案切实可行,可有效解决电缆故障测距过程中的高精度数据采集问题。     

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.     

面向轨迹数据流的KNN近似查询

提出一种基于滑动窗口的K-最近邻(KNN)近似查询算法。将滑动窗口内数据通过聚类划分成若干大小不一的基本窗口,针对每个基本窗口给定一个采样率,对窗口内数据进行偏倚采样,形成数据流摘要,并基于该摘要,采用计算几何平面扫描算法执行分布式最近邻查询。仿真实验结果表明该算法有效,且具有较好的可扩展性。     

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.