基于DSP的Si_3N_4陶瓷磨削力信号检验

采用数字信号处理 (DSP)方法对Si3 N4陶瓷的磨削力信号进行了检验 ,结果表明 :Si3 N4陶瓷的磨削力信号具有不同于金属及其它非金属材料的分布规律 ,是一种具有平稳性、各态历经性的周期振动信号 ,且在总体上不服从正态分布。     

用于火灾探测的离子感烟探测器的设计研究

本文论述了自动火灾离子感烟探测器的结构组成及其电路工作原理。通过对探测器电路的具体分析，进一步阐明自动火灾离子感烟探测器的相关功能。     

基于Hilbert的单边带调制随机共振的微弱信号检测

为了克服经典随机共振中的小参数检测条件的限制,介绍了基于Hilbert变换的单边带频率调制技术,并与二阶随机共振系统相结合,提出了运用调制随机共振的方法实现工程中大信号检测的应用。针对小采样频率和大采样频率进行了分开讨论,并对基频信号的选取做了相关研究。研究结果发现,小采样频率下,Hilbert单边带频率调制技术结合二阶系统有很好的检测效果。大采样频率下,可以结合变尺度处理进行优化。数值仿真分析表明,基频信号取在频率轴分辨率的10～60倍时,会有一个较高的并且较稳定的输出信噪比。并将变尺度Hilbert单边带频率调制技术运用于实际的轴承内外圈故障信号检测中,能明显、准确的检测出单频故障大信号。     

基于CSI的轻量级自适应井下定位算法

针对传统井下定位成本高、工作危险系数大的问题,提出一种基于信道状态信息(channel state information, CSI)的轻量级自适应井下定位(lightweight self-adaptive underground positioning algorithm, LSA)方法。LSA方法以细粒度的CSI替代粗粒度的接收信号强度(received signal strength indicator, RSSI)来获得更高的定位精度,采用逆傅里叶变换将原始CSI数据转换为信道脉冲响应,以此选取视距信号,并通过构建CSI视距信号衰减模型实现轻量级的精确测距;基于井下现有WiFi网络中的访问接入点(access points, APs)位置和井下巷道特征,计算目标相对AP的方向,根据方向和测距结果完成定位。该方法能够自适应于AP在巷道中的任意位置部署,并利用拐角识别优化算法进一步提高定位的精度。试验结果表明,该方法能够使得定位中位数误差达到0.53 m,且无需在井下单独部署任何定位系统,性能明显优于已提出的CDPF、FILA等其他定位算法。     

小波分析在信号处理中的应用

在简述小波分析、小波识别原理的基础上，详细介绍了小波分析在信号处理中的应用，包括信号消噪、特征提取、奇异性检测等。计算机硬件技术的日趋发展，特别是DSP技术的发展，将为小波分析应用于工程中提供有力的保障。     

基于8PSK 和16PSK 调制的全双工RoF 系统研究

误码率(bit error rate, BER)是信号传输质量的重要衡量指标,为了对比分析8进制相移键控(eight hexadecimal phase shift keying, 8PSK)与16进制相移键控(sixteen hexadecimal phase shift keying, 16PSK)信号的传输性能,建立了一个全双工光载无线通信(radio over fiber, RoF)链路模型。系统的下行链路是对基于马赫增德尔调制器(Maher-Zehnder modulator, MZM)的抑制奇次边带两路信号,分别进行8PSK和16PSK调制。上行链路是取下行链路中一路未被调制的抑制奇次边带信号,作为上行基带信号的载波信号,节约系统的成本。该系统分别在不同的光信噪比(optical signal to noise ratio, OSNR)和不同传输距离条件下,仿真分析了下行链路两种调制信号的光谱图及误码率曲线,通过分析接收信号的星座图和误码特性证明:与16PSK信号相比,当OSNR 25时, 8PSK信号误码率量级可以达到10-4(lg BER-3),当传输距离为40 km时, 8PSK的误码率为4.8×10-3(lg BER=-2.32),仍然可以有效传输数据信息。且在不同OSNR和不同传输距离条件下, 8PSK的误码率均低于16PSK,因此8PSK具有更可靠的传输性能,且更适用于远距离传输。     

Pulse Signal Recovery Method Based on Sparse Representation

Pulse signal recovery is to extract useful amplitude and time information from the pulse signal contaminated by noise.It is a great challenge to precisely recover the pulse signal in loud background noise.The conventional approaches,which are mostly based on the distribution of the pulse energy spectrum,do not well determine the locations and shapes of the pulses.In this paper,we propose a time domain method to reconstruct pulse signals.In the proposed approach,a sparse representation model is established to deal with the issue of the pulse signal recovery under noise conditions.The corresponding problem based on the sparse optimization model is solved by a matching pursuit algorithm.Simulations and experiments validate the effectiveness of the proposed approach on pulse signal recovery.     

Network Sorting Algorithm of Multi-Frequency Signal with Adaptive SNR

An signal noise ratio (SNR) adaptive sorting algorithm using the time-frequency (TF) sparsity of frequency-hopping (FH) signal is proposed in this paper.Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition;then the SNR adaptive pivot threshold setting method is used to find the TF single source.The mixed matrix is estimated according to the TF matrix of single source.Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source.Theoretical analysis and simulation results show that this algorithm has good effectiveness and performance.     

Identification of Driving Intention Based on EEG Signals

The driver''s intention is recognized by electroencephalogram(EEG) signals under different driving conditions to provide theoretical and practical support for the applications of automated driving. An EEG signal acquisition system is established by designing a driving simulation experiment, in which data of the driver''s EEG signals before turning left, turning right, and going straight, are collected in a specified time window. The collected EEG signals are analyzed and processed by wavelet packet transform to extract characteristic parameters. A driving intention recognition model, based on neural network, is established, and particle swarm optimization (PSO) is adopted to optimize the model parameters. The extracted characteristic parameters are inputted into the recognition model to identify driving intention before turning left, turning right, and going straight. Matlab is used to simulate and verify the established model to obtain the results of the model.The maximum recognition rate of driving intention is 92.9%. Results show that the driver''s EEG signal can be used to analyze the law of EEG signals. Furthermore, the PSO-based neural network model can be adapted to recognize driving intention.