舌控智能辅助系统的通信装置研究

为提高舌控智能辅助系统舌部运动信息的采集精度,利用STM32H743和LabVIEW设计了一种对舌机接口辅助系统进行信号采集、传输的通信装置.该装置通过舌部触碰嵌入式电极传感器发出信号并传输给单片机(进行舌控信号采集),然后通过串口传给上位机以此最终实现数据的采集与保存.实验结果表明,该装置可有效完成舌控智能辅助系统对控制信号的采集与传输,并具有实时显示、自动保存和查询历史数据等功能; 因此,该装置在舌控智能辅助系统的信号采集中具有良好的应用前景.     

参激随机共振及纠错码技术的应用

为了有效地降低数字信号传输过程中的比特误码率,提出了纠错编码与参数调节随机共振技术的结合.在经过噪声污染后,依据参数调节随机共振技术的性质,给出通过系统参数调节的二进制脉冲幅值调制（PAM）输入信号的渐近误码率公式,应用纠错码技术采用中间值估计的方法得到修正的比特误码率.数字仿真试验证实,理论计算结果与试验结果相吻合,纠错码技术与参激随机共振技术的结合应用于二进制基带信号传输,可以降低误码率,从而可以得到更好的效果,提高通信的可靠性和有效性.     

基于DSP芯片的斜齿轮插齿数控加工技术研究

国内斜齿轮加工一般采用滚齿机或者进口插齿机,由于产品体积过大和机床价格昂贵而受到限制.文章对采用DSP芯片的数控插齿加工技术进行了研究,提出了虚拟轴插补技术以及用软件实现电子齿轮差动技术,利用DSP芯片的高速处理能力进行跟踪插补,经初步试验证明该方法理论上可行,实际中可以运用.     

Bayesian decision-based pattern recognition on spectrum signal of metal transfer modes

A software system of pattern recognition on spectrum signal of metal transfer mode has been developed using Visual Basic under Windows environment. On the basis of the coincidence relation between the spectrum signal and metal transfer mode, according to the geometrical pattern feature of the spectrum signal, several key characteristic parameters are extracted. The correspondent recognition function and a minimum distance classifier have been constructed based on Bayesian decision theory. The results show that using this system, the metal transfer mode of MIG, MAG, CO2 welding can be recognized automatically which provides the basis for automatically controlling of the metal gas arc welding metal transfer.     

基于单片机的PID温控系统在塑料挤出机中的应用

针对在塑料挤出过程中温度控制的重要性，介绍了一种新型的PID温度控制系统。该系统采用高性能单片机芯片，可以方便地对系统加热、冷却周期及PID的各个参数进行在线设定和修改，提高了塑料挤出机的温控精度。     

Quantum DCS系统及其应用体会

中国联合水泥公司3000t／d熟料生产线电气控制系统采用了法国施耐德公司的Quantum DCS系统，实现对全厂所有设备的工艺操作和控制。全面介绍了QuantumDCS系统的特点、功能系统构成网络，并详细总结了确保该系统稳定可靠运行的使用和维护的经验。     

用于虚拟现实的360度视频编码技术新进展*

随着虚拟现实技术不断发展,360度视频编码开始成为研究热点。应用于虚拟现实的360度视频与传统的视频相比分辨率更高,编码数据量更大,在实际应用中面临传输带宽有限的瓶颈,编码效率问题有待解决。归纳分析了国际标准组织联合视频研究组（joint video exploration team, JVET）正在制定的360度视频编码投影变换技术及其编码优化方法,综合对比了各变换技术的编码性能。根据当前最新研究成果,对下一步研究需要解决的问题进行了探讨总结,给出未来的研究方向和思路。     

基于Flash的关键变量容错存储技术研究

嵌入式系统中往往需要保存少量复位或掉电后依赖的关键变量,利用处理器片内Flash存储参数已经成为常规选择,但与常规Flash存储器件相比,片内Flash擦除次数更为有限,难以满足系统生命周期要求。研究了如何延长微处理器片内Flash使用寿命以及关键系统变量容错存储技术,针对典型嵌入式系统应用,分析Flash擦写管理机制,基于写磨损均衡算法进行改良,进一步提升系统容错能力。该技术在基于STM32单片机硬件平台进行了测试验证。     

Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. This paper presents a new method for classification of ictal and seizure-free EEG signals. The proposed method is based on the empirical mode decomposition (EMD) and the second-order difference plot (SODP). The EMD method decomposes an EEG signal into a set of symmetric and band-limited signals termed as intrinsic mode functions (IMFs). The SODP of IMFs provides elliptical structure. The 95% confidence ellipse area measured from the SODP of IMFs has been used as a feature in order to discriminate seizure-free EEG signals from the epileptic seizure EEG signals. The feature space obtained from the ellipse area parameters of two IMFs has been used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier. It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance. Experimental results on EEG database available by the University of Bonn, Germany, are included to illustrate the effectiveness of the proposed method.     

Feature extraction of the first difference of EMG time series for EMG pattern recognition

This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from 18 subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2–8%.