浅谈微机控制中的滤波方法

在微机应用系统中,由于干扰的存在,常需对样信号进行滤波处理,本文介绍了几种常用的数字滤波方法。     

On Collection Size and Retrieval Effectiveness

The relationship between collection size and retrieval effectiveness is particularly important in the context of Web search. We investigate it first analytically and then experimentally, using samples and subsets of test collections. Different retrieval systems vary in how the score assigned to an individual document in a sample collection relates to the score it receives in the full collection; we identify four cases.We apply signal detection (SD) theory to retrieval from samples, taking into account the four cases and using a variety of shapes for relevant and irrelevant distributions. We note that the SD model subsumes several earlier hypotheses about the causes of the decreased precision in samples. We also discuss other models which contribute to an understanding of the phenomenon, particularly relating to the effects of discreteness. Different models provide complementary insights.Extensive use is made of test data, some from official submissions to the TREC-6 VLC track and some new, to illustrate the effects and test hypotheses. We empirically confirm predictions, based on SD theory, that P@n should decline when moving to a sample collection and that average precision and R-precision should remain constant. SD theory suggests the use of recall-fallout plots as operating characteristic (OC) curves. We plot OC curves of this type for a real retrieval system and query set and show that curves for sample collections are similar but not identical to the curve for the full collection.     

一种宽频带峰值采样的电路设计

介绍了一种简单实用的峰值采样方法,能兼顾高、低频段,获取数字化的峰值信号,其电路的实现比较简单,适用频带较宽,经实验证明该方法切实可行。     

信号与系统课程“抽样定理”教学研究

结合信号与系统课程教学实践,从软件仿真和硬件电路验证两个方面演示了抽样定理,结合最新的学术前沿压缩感知技术,介绍了其和抽样定理之间的关系。教学实践表明,软件仿真、硬件系统演示以及前沿技术介绍等使学生能够多方面、多角度地理解和加深相关知识点,在课堂教学中取得了较好的效果。     

光伏逆变器内部电磁干扰抑制电路研究

在光伏逆变系统中,内部电磁干扰是影响其工作稳定性和可靠性的重要因素,能否有效地控制内部电磁干扰决定着系统的成败.文章以一款3 kW离网光伏逆变器设计为例,分析了系统内部主要电磁干扰的分布,并提出了具体解决方案.     

Combining integrated sampling with SVM ensembles for learning from imbalanced datasets

Learning from imbalanced datasets is difficult. The insufficient information that is associated with the minority class impedes making a clear understanding of the inherent structure of the dataset. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced, because they aim to optimize the overall accuracy without considering the relative distribution of each class. In this paper, we study the performance of SVMs, which have gained great success in many real applications, in the imbalanced data context. Through empirical analysis, we show that SVMs may suffer from biased decision boundaries, and that their prediction performance drops dramatically when the data is highly skewed. We propose to combine an integrated sampling technique, which incorporates both over-sampling and under-sampling, with an ensemble of SVMs to improve the prediction performance. Extensive experiments show that our method outperforms individual SVMs as well as several other state-of-the-art classifiers.     

Bias in research

By writing scientific articles we communicate science among colleagues and peers. By doing this, it is our responsibility to adhere to some basic principles like transparency and accuracy. Authors, journal editors and reviewers need to be concerned about the quality of the work submitted for publication and ensure that only studies which have been designed, conducted and reported in a transparent way, honestly and without any deviation from the truth get to be published. Any such trend or deviation from the truth in data collection, analysis, interpretation and publication is called bias. Bias in research can occur either intentionally or unintentionally. Bias causes false conclusions and is potentially misleading. Therefore, it is immoral and unethical to conduct biased research. Every scientist should thus be aware of all potential sources of bias and undertake all possible actions to reduce or minimize the deviation from the truth. This article describes some basic issues related to bias in research.     

基于低采样率数字去斜的合成孔径激光雷达成像处理

将低采样率数字去斜技术用于合成孔径激光雷达（SAL）,大幅度降低了系统的AD采样率.在此基础上,分析线性调频连续波（LFMCW）体制SAL系统的信号采样和成像处理问题.为降低回波延时误差带来的影响,给出一种延时估计方法.该方法通过截取单个扫频周期信号的回波进行去斜脉压,得到场景对应的延时;针对线性调频（LFM）信号的非线性失真,使用剩余视频相位（RVP）滤波法进行信号校正,保证去斜脉压质量.仿真实验验证了将低采样率数字去斜应用于SAL的可行性.     

一种基于minimax准则的压缩采样信号检测方法

压缩感知理论是在已知信号具有稀疏性或可压缩性的条件下,对信号数据进行采集、编解码的新理论.压缩感知理论指出,当观测矩阵满足等容性原理时,可以通过远小于奈奎斯特采样点数的信号点数去重建原始信号.本文将压缩采样的框架应用到信号检测模型中去,提出了一种使用minimax准则对压缩采样的信号进行检测的方法,并从理论上证明了这种方法有很好的检测性能,最后采用蒙特卡罗仿真实验验证了理论分析的结果.     

广义函数在BRATUMASS系统紧邻目标分析中的作用

在目前的BRATUMASS系统探测机制下,由于实际条件的限制,当空间目标紧邻时,将会在系统谱线上相互干扰.这对于本身是弱信号的目标回波来说,干扰直接影响后面的散射背景电平的提取.从BRATUMASS系统探测原理出发,应用广义函数,通过分析其干扰机制,找到基于冲击函数分解的迭代方法.实验处理的结果表明：该迭代方法有效地减小了这种紧邻目标之间的干扰.