A deep bidirectional LSTM approach for video-realistic talking head

This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera. The audio streams are converted into acoustic feature, i.e. Mel-Frequency Cepstrum Coefficients (MFCCs), and their textual labels are also extracted. The visual streams, in particular, the lower face region, are compactly represented by active appearance model (AAM) parameters by which the shape and texture variations can be jointly modeled. Given pairs of the audio and visual parameter sequence, a DBLSTM model is trained to learn the sequence mapping from audio to visual space. For any unseen speech audio, whether it is original recorded or synthesized by text-to-speech (TTS), the trained DBLSTM model can predict a convincing AAM parameter trajectory for the lower face animation. To further improve the realism of the proposed talking head, the trajectory tiling method is adopted to use the DBLSTM predicted AAM trajectory as a guide to select a smooth real sample image sequence from the recorded database. We then stitch the selected lower face image sequence back to a background face video of the same subject, resulting in a video-realistic talking head. Experimental results show that the proposed DBLSTM approach outperforms the existing HMM-based approach in both objective and subjective evaluations.     

Generalized measuring-worm algorithm: high-accuracy mapping and movement via cooperating swarm robots

Recently, many extensive studies have been conducted on robot control via self-positioning estimation techniques. In the simultaneous localization and mapping (SLAM) method, which is one approach to self-positioning estimation, robots generally use both autonomous position information from internal sensors and observed information on external landmarks. SLAM can yield higher accuracy positioning estimations depending on the number of landmarks; however, this technique involves a degree of uncertainty and has a high computational cost, because it utilizes image processing to detect and recognize landmarks. To overcome this problem, we propose a state-of-the-art method called a generalized measuring-worm (GMW) algorithm for map creation and position estimation, which uses multiple cooperating robots that serve as moving landmarks for each other. This approach allows problems of uncertainty and computational cost to be overcome, because a robot must find only a simple two-dimensional marker rather than feature-point landmarks. In the GMW method, the robots are given a two-dimensional marker of known shape and size and use a front-positioned camera to determine the marker distance and direction. The robots use this information to estimate each other’s positions and to calibrate their movement. To evaluate the proposed method experimentally, we fabricated two real robots and observed their behavior in an indoor environment. The experimental results revealed that the distance measurement and control error could be reduced to less than 3 %.     

基于Tri-Training的事件关系分类方法研究

事件关系分类是一项研究事件之间存在何种逻辑关系的自然语言处理技术。针对事件关系分类任务中训练语料不足的问题,提出了基于Tri-Training的事件关系分类方法。该方法首先根据已标注的语料训练三个不同的分类器,以多数投票的方式从未标注集中抽取置信度较高的样本对训练集进行扩充,然后利用新的训练集重新训练分类器,反复迭代,不断完善分类模型,最终达到提升事件关系分类性能的目的。实验结果表明,以F1值为评价标准,基于Tri-Training的事件关系分类方法在四大类事件关系上的分类性能为64.36%。     

SIM300和nRF24LE1的多通道无线数据采集系统设计

设计了由GSM/GPRS模块SIM300和射频收发芯片nRF24LE1组成的无线数据采集系统,实现了对数据的采集与无线传输。系统由嵌入高速单片机的nRF24LE1芯片完成对多路数据的采集、处理和收发,并由SIM300将数据通过网络传递给TCP服务器和手机终端。在完成系统的硬件设计和软件开发的过程中,重点探究了nRF24LE1芯片的多通道收发过程和TCP服务器的建立。该系统具有功耗低、扩展方便等优点,适合多种无线数据采集和传输的场合。     

Performance analysis on Lv distribution and its applications

A new signal analysis method, known as Lv distribution (LVD), has been reported recently to provide improved estimation accuracy of centroid frequency and chirp rate. In this paper, performances of the LVD on signal concentration, detection, representation errors and computational complexity are discussed and compared with polynomial Fourier transform (PFT) and fractional Fourier transform (FrFT). Based on the results of our theoretical analysis and Monte Carlo simulations, it is shown that the LVD achieves desirable performance improvement compared with those achieved by other methods. By using the accurate estimation of chirp rate provided by the LVD, the performance of local polynomial periodogram (LPP) is investigated. Comparisons with other time–frequency representations, such as the inverse LVD (ILVD) and the PFT-based LPP, are made on signal concentration in the time–frequency domain.     

用dsPIC30F3010实现无刷直流电动机正弦波驱动

针对具有霍尔传感器的无刷直流电动机,以dsPIC30F3010为核心控制芯片,绘制出系统的总体设计框图,同时采用空间矢量调制法生成六路SVPWM信号,从而实现无刷直流电机的正弦波驱动.实验结果表明采用空间矢量脉宽调制法,可以实现正弦波驱动无刷直流电机的平稳运行.     

基于Modelica的CAN通信系统建模仿真

首先对CAN2.0B通信协议做了分析,介绍了Modelica多领域统一建模语言的优越性.并在CAN2.0B通信协议基础上,采用Modelica统一建模语言,建立了CAN总线的电路模型和时序状态模型.通过对改变双绞线长度和通信节点数两种工况进行仿真,确定了模型的可用性.同时分析了CAN总线相关参数对其通信性能的影响,为CAN通信设计和验证提供参考.     

基于LabVIEW的调节阀流量特性实验平台设计

本设计利用LabVIEW软件对调节阀流量特性试验平台进行设计,采用PCI-1710HG多功能数据采集卡对调节阀位移信号、输出流量信号进行双路采集;对调节阀流量特性的修正和改变;实验过程的相关数据进行监测、记录、显示和保存等功能.利用LabVIEW软件进行实验平台设计可以用软件程序实现一些硬件仪器的功能,这样节约成本,外界环境对实验的干扰小,控制的效果明显.     

探析办公自动化的网络安全

随着社会科学以及社会经济的高速发展,计算机已经普及到了日常的工作、生活,而利用计算机的办公自动化,更是办公的阶段性提高.但是,办公的自动化也伴随着一些网络的安全问题,而提高网络的安全监管,防止信息的失真、修改、盗取,就显得非常重要了.本文就这个问题进行简单的探讨.