Digital Signal Processing Based Real Time Vehicular Detection System

Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is presented to obtain various traffic parameters through vehicular video detection system(VVDS).VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time.This algorithm uses the background differencing method,and vehicles can be detected through luminance difference of pixels between background image and current image.Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy.Then a hardware implementation of a digital signal processing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP′s high performance.In the end,VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation.

Digital coherent detection research on Brillouin optical time domain reflectometry with simplex pulse codes

The digital coherent detection technique has been investigated without any frequency-scanning device in the Brillouin optical time domain reflectometry （BOTDR）, where the simplex pulse codes are applied in the sensing system. The time domain signal of every code sequence is collected by the data acquisition card （DAQ）. A shift-averaging technique is applied in the frequency domain for the reason that the local oscillator （LO） in the coherent detection is fix-frequency deviated from the primary source. With the 31-bit simplex code, the signal-to-noise ratio （SNR） has 3.5-dB enhancement with the same single pulse traces, accordant with the theoretical analysis. The frequency fluctuation for simplex codes is 14.01 MHz less than that for a single pulse as to 4-m spatial resolution. The results are believed to be beneficial for the BOTDR performance improvement.

Digital Cross-Correlation Detection of Multi-Laser Beams Measuring System for Wind Field Detection

A cross-correlation detection method to process backscatter signals of multi-laser beams measuring （MLBM） is presented, which can be firstly filtered by the digital filter composed of average median filter and finite impulse response （FIR） digital filter. The processing of backscatter signals using single-pulse and three-pulse cross-correlation detection methods is depicted in detail. From calculation results, the multi-pulse cross-correlation detection could effectively improve signal-to-noise ratio （SNR）. Finally, both wind velocity and direction are determined by the peak-delay method based on the correlation function which shows high measuring precision and high SNR of the MLBM system with the assistance of the digital cross- correlation detection.

Wideband Signal Detection Based on MWC Discrete Compressed Sampling Structure

In order to solve the cross-channel signal problem caused by the uniform channelized wideband digital receiver when processing wideband signal and the problem that the sensitivity of the system greatly decreases when the bandwidth of wideband digital receiver increases,which both decrease the wideband radar signal detection performance,a new wideband digital receiver based on the modulated wideband converter(MWC)discrete compressed sampling structure and an energy detection method based on the new receiver are proposed.Firstly,the proposed receiver utilizes periodic pseudo-random sequences to mix wideband signals with baseband and other sub-bands.Then the mixed signals are low-pass filtered and downsampled to obtain the baseband compressed sampling data,which can increase the sensitivity of the system.Meanwhile,the cross-channel signal will all appear in any subbands,so the cross-channel signal problem can be solved easily by processing the baseband compressed sampling data.Secondly,we establish the signal detection model and formulate the criterion of the energy detection method.And we directly utilize the baseband compressed sampling data to carry out signal detection without signal reconstruction,which decreases the complexity of the algorithm and reduces the computational burden.Finally,simulation experiments demonstrate the effectiveness of the proposed receiver and show that the proposed signal detection method is effective in low signal-to-noise ratio(SNR)compared with the conventional energy detection and the probability of detection increases significantly when SNR increases.

A new method of demodulating the digital frequency modulation signals

A new method of estimating the frequency-known signals from the strong background noise was presented first. Then the new method was used in the demodulation of the digital frequency modulation (FSK) signals. The new demodulation method can complete the demodulation of the FSK signals only with the carrier frequency and without any carrier phase information. The simulation results show that the performance of anti-noise of the new method is better than that of the incoherent demodulation method and the fluctuation of the carrier phase has little effect on the new method. So the new demodulation method has a fine prospect in the practical applications.

On the Small SNR Processing Ability of IR Point Target Detection

Three-dimensional (3-D) matched filtering has been suggested as a powerful processing technique for detecting weak, moving IR point target immersed in a noisy field. Based on the theory of the 3-D matched filtering and the optimal linear processing, the optimal point target detector is being analyzed in this paper. The performance of the detector is introduced in detail. The results provide a standard reference to evaluate the performance of any other point target detection algorithms.

Automatic System for Failure Detection in Hydro-Power Generators

This paper presents an automatic system for failure detection in hydro-power generators. The main idea of this system is to detect failure using current and voltage signals acquired without any type of internal interference in the generator operation. The detected failures could be mechanical or electrical origins, such as: problems in bearings, unwanted vibrations, partial discharges, misalignment, unbalancing, among others. It is possible because the generator acts as a transducer for mechanical problems, and they appear in current and voltage signals. This automatic system based on electric signature analysis has been installed in Itapebi Power Plant generators since 2012. Some results are presented in this paper.

Industrial shape detecting system of cold rolling strip

A high-precision shape detecting system of cold rolling strip is developed to meet industrial application, which mainly consists of the shape detecting roller, the collecting ring, the digital signal processing （DSP） shape signal processing board and the shape control model. Based on the shape detecting principle, the shape detecting roller is designed with a new integral structure for improving the precision of shape detecting and avoiding scratching strip surface. Based on the DSP technology, the DSP shape signal processing circuit board is designed and embedded in the shape detecting system for the reliability and stability of shape signal processing. The shape detecting system was successfully used in Angang 1 250 mm HC 6-high reversible cold rolling mill. The precision of shape detecting is 0.2 I and the shape deviation is controlled within 6 1 after the close loop shape control is input.

Digital image correlation-based structural state detection through deep learning

This paper presents a new approach for automatical classification of structural state through deep learning.In this work,a Convolutional Neural Network(CNN)was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame;the input was a series of vibration signals,and the output was a structural state.The digital image correlation(DIC)technology was utilized to collect vibration information of an actual steel frame,and subsequently,the raw signals,without further pre-processing,were directly utilized as the CNN samples.The results show that CNN can achieve 99%classification accuracy for the research model.Besides,compared with the backpropagation neural network(BPNN),the CNN had an accuracy similar to that of the BPNN,but it only consumes 19%of the training time.The outputs of the convolution and pooling layers were visually displayed and discussed as well.It is demonstrated that:1)the CNN can extract the structural state information from the vibration signals and classify them;2)the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN;3)the CNN has better anti-noise ability.

用于单片机实验教学的红外激光气体检测仪

为了实现科研反哺教学、促进教学与科研的深度融合,研制了一种基于嵌入式多核处理器和数字信号处理器的实验教学用红外激光气体检测仪。该检测仪包括光学系统和电学系统,其中电学系统包含光谱信息感知模块和嵌入式控制模块。利用研制的检测仪开展了氨制冷冷库现场的泄漏氨气浓度的检测应用。结果表明,与传统气体检测仪相比,该检测仪实现了检测仪的网络化与智能化,而且性能满足实验教学要求。

基于特征值的动态数字信道化子带检测算法

动态数字信道化接收结构通过检测所有子带,判断信号是否存在,为综合滤波器组处理提供依据,因此子带检测在接收结构中起着关键作用。针对传统检测算法存在低信噪比(signal-to-noise ratio,SNR)下检测性能不高的问题,依据随机矩阵理论,提出了基于最大最小特征值之差与平均特征值之比的检测算法,利用平均特征值和最小特征值的极限分布规律来推导算法的检测门限。其次,根据所有子带数据获取的特征值信息对所提算法进行了优化。最后,在动态数字信道化接收结构中,分析不同因素下算法的性能,表明了所提算法能够克服低SNR的影响,子带检测的性能更好。

基于数字孪生的工业互联网安全检测与响应研究

考虑传统网络安全防御方法不能够满足工业互联网对可靠性和稳定性的严格要求,基于数字孪生的思想研究了一种在数字空间中通过采集现场数据和使用孪生模型安全认知进行异常检测和响应的方法。首先,通过对数字孪生建模方案进行分析,总结出4类建模方法并集成到多模块数字孪生(DT)架构中;然后,通过引入信号时序逻辑技术将不同孪生模型认知转化为标准的信号时序逻辑(STL)规范集,根据规范集对系统行为的监测实现异常检测,多源认知增加了检测结果的可靠性;最后,通过对违反STL规范集情况的分析实现异常定位,并通过对已知设备故障的分析设计相应STL弱规范实现异常分类,对异常的两方面响应有利于帮助系统恢复正常运行。案例研究表明,所提方法在异常检测和响应方面非常有效。将所提方法与基于深度学习的入侵检测系统进行对比,实验结果表明,所提方法在对异常情况的检测时检出率提高了25%~40.9%。

基于圆循环恒虚警的多雷达信号被动检测方法

低截获概率(LPI)雷达由于具有发射功率较低的特点,使得雷达侦察接收机接收到的雷达信号信噪比较低,现有的被动检测方法无法有效检测微弱信号。针对此问题,在高斯白噪声背景下,提出了一种基于圆循环恒虚警率(CFAR)的多雷达信号被动检测方法,并分别对基于圆循环的单元平均恒虚警率(CA-CFAR)、最大选择恒虚警率(GO-CFAR)、最小选择恒虚警率(SO-CFAR)、有序统计恒虚警率(OS-CFAR)4种检测器的虚警概率与检测概率进行了推导。仿真结果表明,在高斯白噪声背景下,存在一个信号时,基于圆循环的CA-CFAR检测性能最优;存在多个信号时,基于圆循环的CA-CFAR检测性能较差,OS-CFAR检测性能更佳。此外,采用多帧检测可以进一步提高检测性能。所提方法复杂度较低且实时性较高,可满足工程实际需求。

X射线数字成像图像质量影响因素探讨

X射线数字成像检测系统是在射线透射工件的层面的同时,通过计算机及数字处理软件直接观察工件内部情况。通过X射线数字成像检测系统采集不同参数下的图像,对管电压、管电流、增益、单帧曝光时间及积分次数与图像灰度值和图像信噪比间的关系进行了统计分析,结果表明增益与图像灰度值基本上成反相关关系,增益几乎对图像信噪比无影响。该实验为图像灰度值和图像信噪比的影响因素的研究提供了有力的数据依据。

基于星座图恢复和斑点检测的MPSK信号调制识别研究

在无线电信号分析、监测中,信号的调制机制识别是其中的关键。多进制数字相位调制信号(MPSK)的识别在实际应用中需求广泛,能准确识别出MPSK的具体调制方式意义重大。本文针对接收信号为MPSK时,通过基于星座图恢复处理及斑点检测的组合算法,精确区分不同多进制数字相位调制信号。通过对BPSK、QPSK、8PSK信号的仿真分析,证明本文所提算法能准确识别出各种多进制数字相位调制信号。

基于声学特性的禽蛋裂纹实时在线检测系统

设计了一套基于数字信号处理器DSP(TMS320F2812)的禽蛋裂纹在线检测系统,系统以禽蛋声学特性为基础,通过采集并分析敲击禽蛋产生的响应信号,可实现禽蛋裂纹的在线、实时检测。根据大量完好和裂纹禽蛋敲击响应信号特征的差异,提取了5个特征参数,建立相应判别模型,快速检测裂纹禽蛋。系统对75个完好和95个裂纹鸡蛋进行了在线检测试验,判对率分别为92.0%和90.5%,检测速率为5个/s,满足在线检测的要求。

基于DSP的全数字闭环光纤陀螺

介绍了一种采用ＤＳＰ（ＤｉｇｉｔａｌＳｉｇｎａｌＰｒｏｃｅｓｏｒ）技术实现的全数字闭环光纤陀螺．该闭环光纤陀螺采用以多功能集成光学器件为核心的全保偏结构，以方波为偏置调制，数字阶梯波反馈．文中对该闭环光纤陀螺的前置放大、信号检测、数字解调、数字滤波等部分进行了设计和实现，对采用ＤＳＰ技术解决系统精度和实时性矛盾的方案进行了讨论，并对闭环光纤陀螺的性能和参数进行了理论分析和实验测试．测试结果表明系统达到了小于０．３（°）／ｈ的零漂和１００×１０－６的非线性度指标．

一种新型基于时域的谐波电流检测算法的研究

在传统的基于DFT的谐波电流检测算法的基础上,提出了一种基于时域的电流检测算法,该算法具有DFT算法的灵活性,但克服了DFT需要一个工频周期的延时的缺点,同时又具有物理意义明确的特点,采用该算法可以方便、快速地检测出基波、基波无功、基波有功、基波正序、基波负序、基波正序有功以及各次谐波,该算法实现简单,易于编程,且检测效果很好,为有源电力滤波器满足不同的补偿目的提供了可靠保证。最后通过实验验证了该算法的正确性。

一种基于瞬时无功功率理论谐波检测的离散滤波方法

根据三相三线电路系统对称分量法 ,以瞬时无功功率理论为基础 ,推出基波和各次谐波电流的正序及负序分量离散检测方法。用复化积分算法取代传统的低通滤波器 ,具有截止频率特性好 ,检测结果稳定 ,易于编程实现等特点。利用MATLAB动态仿真工具对不同检测方法进行对比分析 ,结果表明 :该方法比传统的检测方法具有更快的响应速度。实验验证了该方法的正确性。

一种气体超声波流量计信号处理方法研究

针对气体超声波流量计信号处理中很难确定特征点的问题,提出一种基于可变阈值过零检测的信号处理方法,准确地找到信号的特征波,从而求得超声波信号的传播时间,准确测量气体流量。在以DSP和FPGA为双核心的气体超声波流量计信号处理系统上实时实现这种方法。采用判断"台阶"的方法,剔除噪声干扰所带来的误差,提高测量结果的稳定性。采用分段线性校正的方法,修正非线性误差,提高系统的测量精度。气体流量标定实验结果验证了方法和系统的有效性。