▍1. Underdetermined-DOA-Estimation
比较新的稀疏重构宽带DOA方法，供学习阵列信号处理的参考(Relatively new broadband DOA sparse reconstruction method for learning the reference array signal processing)
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▍2. A-comprehensive-survey-on-vehicular-Ad-Hoc-networ
Vehicular ad hoc networks (VANETs) are classified as an application of mobile ad hoc network (MANET) that has the potential in improving road safety and in providing travellers comfort. Recently VANETs have emerged to turn the attention of researchers in the field of wireless and mobile communications, they differ from MANET by their architecture, challenges, characteristics and applications. In this paper we present aspects related to this field to help researchers and developers to understand and distinguish the main features surrounding VANET in one solid document, without the need to go through other relevant papers and articles starting from VANET architecture and ending up with the most appropriate simulation tools to simulate VANET protocols and applications
▍3. 111
计算混凝土泵车臂架疲劳损伤时采集的应变数据中含有奇异值会对损伤结果产生较大影响， 而传统的3 准则的判定值为常系数，不适合泵车臂架应变信号的变化． 对此，结合泵车臂架应变信号的特点，提出了剔除奇异值的新方法(Fatigue damage calculation when the boom concrete pump strain data collected will contain singular values greater impact damage results, and the traditional three criteria for determining the value of the constant coefficient, is not suitable for pump truck boom strain signal changes. This, combined with the pump arm strain signals, the proposed new method of removing the singular value)
▍4. 111
计算混凝土泵车臂架疲劳损伤时采集的应变数据中含有奇异值会对损伤结果产生较大影响， 而传统的3 准则的判定值为常系数，不适合泵车臂架应变信号的变化． 对此，结合泵车臂架应变信号的特点，提出了剔除奇异值的新方法(Fatigue damage calculation when the boom concrete pump strain data collected will contain singular values greater impact damage results, and the traditional three criteria for determining the value of the constant coefficient, is not suitable for pump truck boom strain signal changes. This, combined with the pump arm strain signals, the proposed new method of removing the singular value)
▍5. mcmc_gibbs_intro
对马尔科夫链蒙特卡洛模拟和吉布斯采样的一个介绍文章，对了解这些方法非常有用！(We focus here on Markov Chain Monte Carlo (MCMC) methods, which attempt to simulate direct draws from some complex distribution of interest. MCMC approaches are so-named because one uses the previous sample values to randomly generate the next sample value, generating a Markov chain (as the transition probabilities between sample values are only a function of the most recent sample value).)
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