Quick System-Level DDR3 Signal Integrity Simulation Research

---Double data rate synchronous dynamic random access memory （DDR3） has become one of the most mainstream applications in current server and computer systems. In order to quickly set up a system-level signal integrity （SI） simulation flow for the DDR3 interface, two system-level SI simulation methodologies, which are board-level S-parameter extraction in the frequency-domain and system-level simulation assumptions in the time domain, are introduced in this paper. By comparing the flow of Speed2000 and PowerSI/Hspice, PowerSI is chosen for the printed circuit board （PCB） board-level S-parameter extraction, while Tektronix oscilloscope （TDS7404） is used for the DDR3 waveform measurement. The lab measurement shows good agreement between simulation and measurement. The study shows that the combination of PowerSI and Hspice is recommended for quick system-level DDR3 SI simulation.

New Method to Measure the Fill Level of the Ball Mill Ⅱ-Analysis of the Vibration Signals

The exact measurement of the fill level is the key and basic problem for automatic control and optimized operation of the coal pulverizing system. Because the ball mill pulverizing system is non-linearity, long time delay and time-varying, the reliable and effective method for measuring the fill level was lacked at present. In order to reduce the influence by various factors on measuring the fill level and improve the measuring accuracy of the fill level, a novel characteristic variable is proposed. A set of wireless transmission device was designed to record vibration signals, and an accelerometer with high accuracy and large measuring range was mounted directly on the mill shell to pick up the vibration signals from the mill shell. A series of data acquisition experiments under various ball load and water content of coal conditions were conducted in an industrial mill to investigate the relationship between the fill level and the angular position of the maximum vibration point of the mill shell through the analysis of the vibration signals. The analytical result of test data clearly show that the angular position of the maximum vibration point on the mill shell decreases as the fill level increases. At the same time, comparing with the traditional characteristic variable, the feature variable of the fill level proposed in this paper is not subject to the effect of the ball load and water content of coal, which provides a new solution and reliable basis for the accurate measurement of the fill level.

Pedestrian perception-based level-of-service model at signalized intersection crosswalks

Pedestrian level of service(PLOS)is an important measure of performance in the analysis of existing pedestrian crosswalk conditions.Many researchers have developed PLOS models based on pedestrian delay,turning vehicle effect,etc.,using the conventional regression method.However,these factors may not effectively reflect the pedestrians'perception of safety while crossing the crosswalk.The conventional regression method has failed to estimate accurate PLOS because of the primary assumption of an arbitrary probability distribution and vagueness in the input data.Moreover,PLOS categories in existing studies are based on rigid threshold values and the boundaries that are not well defined.Therefore,it is an important attempt to develop a PLOS model with respect to pedestrian safety,convenience,and efficiency at signalized intersections.For this purpose,a video-graphic and user perception surveys were conducted at selected nine signalized intersections in Mumbai,India.The data such as pedestrian,traffic,and geometric characteristics were extracted,and significant variables were identified using Pearson correlation analysis.A consistent and statistically calibrated PLOS model was developed using fuzzy linear regression analysis.PLOS was categorized into six levels(A–F)based on the predicted user perception score,and threshold values for each level were estimated using the fuzzy c-means clustering technique.The developed PLOS model and threshold values were validated with the fieldobserved data.Statistical performance tests were conducted and the results provided more accurate and reliable solutions.In conclusion,this study provides a feasible alternative to measure pedestrian perception-based level of service at signalized intersections.The developed PLOS model and threshold values would be useful for planning and designing pedestrian facilities and also in evaluating and improving the existing conditions of pedestrian facilities at signalized intersections.

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

Read channel for signal waveform modulation multi-level disc

A novel read channel for signal waveform modulation multi-level disc is presented in this paper. This read channel employs timing recovery system and partial response maximum likelihood detector. Compared to the previous read channel composed of level detection and run-length detection, the present read channel shows superiority in capacity increase and robust performance. Especially, relying on the partial response maximum likelihood detection, lower bit error rate can be obtained.

Improved partial response maximum likelihood method combining modulation code for signal waveform modulation multi-level disc

In this paper, we describe an improved adaptive partial response maximum likelihood （PRML） method combining modulation code tbr signal waveform modulation multi-level disc. This improved adaptive PRML method employs partial response equalizer and adaptive viterbi detector combining modulation code. Compared with the traditional adaptive PRML detector, the improved PRML detector additionally employs illogical sequence detector and corrector. Illogical sequence detector and corrector can aw）id the appearance of illogical sequences effectively, which do not follow the law of modulation code for signal waveform modulation multi-level disc, and obtain the correct sequences. We implement the improved PRML detector using a DSP and an FPGA chip. The experimental results show good performance. The higher efficient and lower complexity can be obtained by using the improved PRML method than by using the previous PRML method. Meanwhile, resource utilization of the improved PRML detector is not changed, but the bit error rate （BER） is reduced by more than 20%.

A new tracking error detection method using amplitude difference detection for signal waveform modulation multi-level discs

The sub-land/sub-pit affects the characteristic of the tracking error signal which is generated by the conventional differential phase detection （DPD） method in the signal waveform modulation multi-level （SWML） read-only disc. To solve this problem, this paper proposes a new tracking error detection method using amplitude difference. Based on the diffraction theory, the amplitude difference is proportional to the tracking error and is feasible to be used for obtaining the off-track information. The experimental system of the amplitude difference detection method is developed. The experimental results show that the tracking error signal derived from the new method has better performance in uniformity and signal-to-noise ratio than that derived from the conventional DPD method in the SWML read-only disc.

Radio Frequency Environment Effect Algorithms and Implementation in Signal-level Radar System Simulation

In radar system simulation,the reliability of simulation results depends not only on radar and target models,but also on radio frequency (RF) environment models,including clutter,multipath,diffraction,atmosphere refraction and attenuation.In traditional radar function simulation,all of these factors are grouped into a single pattern-propagation factor and can only give limited information for radar models.In signal-level simulation,radar models require simulated echoes should include information such as delay,doppler frequency,polarization,etc.By discussing and analyzing the principles and algorithms of RF environment effects (clutter,multipath,diffraction,atmosphere refraction and attenuation),this paper is supposed to provide a general RF environment model in signal-level.Algorithms for the Weibull clutter with Gaussian power spectral density (PSD) is discussed;A standard multipath and diffraction algorithm is analyzed,and the spherical earth and knife edge(SEKE)diffraction algorithm is introduced;The ray-tracing algorithm and the effective earth model are discussed;Algorithms for the absorption of oxygen and vapor are introduced;For certain algorithms,some practical advice is given.Finally,an object-oriented RF environment effects model is implemented,which has been dedicatedly designed for signal-level simulations and can provide relatively authentic simulated RF environment for the signal-level simulation of radar systems.Two simulation examples including clutter model and multipath and diffraction model are carried out and analyzed.

基于GNSS-IR高度角偏差修正方法的研究与评估

针对高度角偏差使全球导航卫星系统干涉反射测量(GNSS-IR)反演海面高度的精度降低这一问题,提出一种基于电离层区域的高度角偏差修正方法,为验证方法的可靠性,将修正高度角偏差前后的反演结果进行对比。结果表明,经过高度角偏差修正的反演结果精度更高,利用高度角偏差修正方法可以较好地修正电离层引起的高度角偏差。

A conditioned level-set method with block-division strategy to flame front extraction based on OH-PLIF measurements

A novel approach to extract flame fronts, which is called the conditioned level-set method with block division （CLSB）, has been developed. Based on a two-phase level-set formulation, the conditioned initialization and region-lock optimiza-tion appear to be beneficial to improve the efficiency and accuracy of the flame contour identification. The original block- division strategy enables the approach to be unsupervised by calculating local self-adaptive threshold values autonomously before binarization. The CLSB approach has been applied to deal with a large set of experimental data involving swirl- stabilized premixed combustion in diluted regimes operating at atmospheric pressures. The OH-PLIF measurements have been carried out in this framework. The resulting images are, thus, featured by lower signal-to-noise ratios （SNRs） than the ideal image; relatively complex flame structures lead to significant non-uniformity in the OH signal intensity; and, the mag- nitude of the maximum OH gradient observed along the flame front can also vary depending on flow or local stoichiometry. Compared with other conventional edge detection operators, the CLSB method demonstrates a good ability to deal with the OH-PLIF images at low SNR and with the presence of a multiple scales of both OH intensity and OH gradient. The robustness to noise sensitivity and intensity inhomogeneity has been evaluated throughout a range of experimental images of diluted flames, as well as against a circle test as Ground Truth （GT）.

Estimating rock properties using sound signal dominant frequencies during diamond core drilling operations

In many engineering applications such as mining,geotechnical and petroleum industries,drilling operation is widely used.The drilling operation produces sound by-product,which could be helpful for preliminary estimation of the rock properties.Nevertheless,determination of rock properties is very difficult by the conventional methods in terms of high accuracy,and thus it is expensive and timeconsuming.In this context,a new technique was developed based on the estimation of rock properties using dominant frequencies from sound pressure level generated during diamond core drilling operations.First,sound pressure level was recorded and sound signals of these sound frequencies were analyzed using fast Fourier transform (FFT).Rock drilling experiments were performed on five different types of rock samples using computer numerical control (CNC) drilling machine BMV 45 T20.Using simple linear regression analysis,mathematical equations were developed for various rock properties,i.e.uniaxial compressive strength,Brazilian tensile strength,density,and dominant frequencies of sound pressure level.The developed models can be utilized at early stage of design to predict rock properties.

连续振动载荷在线复现控制技术研究

提出一种主从分布式振动试验方法,建立了连续振动载荷在线复现控制技术,研制了系统样机。其中,提出一种信号分帧处理与重构技术,解决了长时连续振动载荷的在线波形复现难题;提出一种基于多量级样本库构建与加权平均的传递函数动态估计方法,抑制了系统非线性对控制精度的影响;采用谱误差反馈修正方法,改善了系统的频域控制精度。开展了“1主2从”联台试验,结果表明:时域和频域控制精度良好,整体延迟时间在亚秒量级,全程总均方根值误差在1%以内,为主从分布式振动试验提供了关键技术支撑。

Novel Frame Shift and Integral Technique for Enhancing Low-Light-Level Moving Images

A novel frame shift and integral technique for the enhancement of low light level moving image sequence is introduced. According to the technique, motion parameters of target are measured by algorithm based on difference processing. To obtain spatial relativity, images are shifted according to the motion parameters. As a result, the processing of integral and average can be applied to images that have been shifted. The technique of frame shift and integral that includes the algorithm of motion parameter determination is discussed, experiments with low light level moving image sequences are also described. The experiment results show the effectiveness and the robustness of the parameter determination algorithm, and the improvement in the signal-to-noise ratio （SNR） of low light level moving images.

New Modeling for Generation of Normal and Abnormal Heart Rate Variability Signals

This research is performed based on the modeling of biological signals. We can produce Heart Rate (HR) and Heart Rate Variability (HRV) signals synthetically using the mathematical relationships which are used as input for the Integral Pulse Frequency Modulation (IPFM) model. Previous researches were proposed same methods such as one model of ECG signal synthetically based on RBF neural network, a model based on IPFM with random threshold, method was based on the estimation of produced signals which are dependent on autonomic nervous system using IPFM model with fixed threshold, a new method based on the theory of vector space that based on time-varying uses of IPMF model (TVTIPMF) and special functions, and two different methods for producing HRV signals with controlled characteristics and structure of time-frequency (TF) for using non-stationary HRV analysis. In this paper, several chaotic maps such as Logistic Map, Henon Map, Lorenz and Tent Map have been used. Also, effects of sympathetic and parasympathetic nervous system and an internal input to the SA node and their effects in HRV signals were evaluated. In the proposed method, output amount of integrator in IPFM model was compared with chaotic threshold level. Then, final output of IPFM model was characterized as the HR and HRV signal. So, from HR and HRV signals obtaining from this model, linear features such as Mean, Median, Variance, Standard Deviation, Maximum Range, Minimum Range, Mode, Amplitude Range and frequency spectrum, and non-linear features such as Lyapunov Exponent, Shanon Entropy, log Entropy, Threshold Entropy, sure Entropy and mode Entropy were extracted from artificial HRV and compared them with characteristics as extracted from natural HRV signal. Also, in this paper two patients that called high sympathetic Balance and Cardiovascular Autonomy Neuropathy (CAN) which is detected and evaluated by HRV signals were simulated. These signals by changing the values of the some coefficients of the normal simulated signal and with extracted frequency feature from these signals were simulated. For final generation of these abnormal signals, frequency features such as energy of low frequency band (EL), energy of high frequency band (HL), ratio of energy in low frequency band to the energy in high frequency band (EL/EH), ratio of energy in low frequency band to the energy in all frequency band (EL/ET) and ratio of energy in high frequency band to the energy in all frequency band (EH/ET) from abnormal signals were extracted and compared with these extracted values from normal signals. The results were closely correlated with the real data which confirm the effectiveness of the proposed model. Various signals derived from the output of this model can be used for final analysis of the HRV signals, such as arrhythmia detection and classification of ECG and HRV signals. One of the applications of the proposed model is the easy evaluation of diagnostic ECG signal processing devices. Such a model can also be used in signal compression and telemedicine application.

一种基于可编程逻辑器件的多功能信号源设计

为适应不同应用场景下对多种信号类型和特性的需求改变,设计一种基于可编程逻辑器件的多功能信号源,以解决通信信号输出过程存在偏差问题。基于可编程逻辑器件进行多功能信号源的电路排布,采用巴特沃斯低通滤波器对信号噪声进行处理,确定波形优化指标,利用数字频率合成技术进行信号波形优化,设计顶层逻辑模块布局。对设计的多功能信号源进行性能测试,结果表明:在改变载波频率和调制度的情况下,设计的多功能信号源在传输过程中输出的信号频谱和解调波形的偏置精度,均优于传统的信号源。

基于经验小波变换的多通道微弱电流信号噪声控制

多通道微弱电流信号受很多噪声因素的干扰,导致输出的微弱电流信号与原始信号之间的相对误差增加,控制效果变差,为此提出基于经验小波变换的多通道微弱电流信号噪声控制方法;采集多通道微弱电流信号,利用经验小波变换技术分解采集到的微弱电流信号,判断电流信号中是否存在噪声并确定噪声类型;设计微弱电流信号噪声控制器,根据电流信号中的噪声量与噪声类型生成控制指令,对不同通道微弱电流信号噪声进行控制;在两种不同噪声强度的实验环境下,该方法控制下的多通道微弱电流信号信噪比较高,输出信号与原始信号间的相对误差较低,控制任务完成时间更短;以此证明该方法的多通道微弱电流信号噪声控制效果更好,可以广泛应用至电流信号噪声控制领域。

Novel non-coherent integration method using binary phase-coded radar signal

The m series with 511 bits is taken as an example being applied in non-coherent integra- tion algorithm. A method to choose the bi-phase code is presented, which is 15 kinds of codes are picked out of 511 kinds of m series to do non-coherent integration. It is indicated that the power in- creasing times of larger target sidelobe is less than the power increasing times of smaller target main- lobe because of the larger target＇ s pseudo-randomness. Smaller target is integrated from larger tar- get sidelobe, which strengthens the detection capability of radar for smaller targets. According to the sidelobes distributing characteristic, a method is presented in this paper to remove the estimated sidelobes mean value for signal detection after non-coherent integration. Simulation results present that the SNR of small target can be improved approximately 6. 5 dB by the proposed method.

基于选频电平表设计的信号与系统综合实验

针对学生的工程实践能力培养,为信号与系统实验课程设计了项目驱动式综合实验“选频电平表设计”。基于CDIO工程教育模式设计了实验内容,指导学生通过团队协作完成电子产品从研发到运行的完整周期。实验项目涉及傅里叶变换、频谱搬移、滤波等信号的频域分析和处理方法,同时结合了模拟电路设计、单片机技术、 EDA技术、电路焊接与调试等内容,综合性强,紧贴工程实际。教学实践表明,该实验能帮助学生加深对理论知识的理解,积累实践经验,提高学习兴趣和实践能力。

基于A3C的认知物联网通信干扰消除算法

针对频谱资源干扰管理的智能化需求,提出一种基于异步优势行动者-评论家(A3C)的干扰消除算法,旨在应对认知物联网(CIoT)通信系统中由频谱资源共享引起的干扰问题。通过智能体的学习和优化,帮助次级用户(SU)在受到干扰影响时做出最优的决策,从而改善通信质量和系统性能。在该算法中,当SU遭受干扰影响通信质量时,智能体通过学习和优化,使SU能够根据当前的位置信息、发射功率、接收功率以及干扰程度选择最低干扰程度的行动,并执行该行动后获得的奖励。智能体通过尝试不同减少干扰的行动,并根据奖励的反馈调整策略,达到最大化定义干扰程度指标和信号质量指标的奖励函数的目的,从而最大程度地减少干扰对通信质量的影响。实验结果表明,与传统k-means算法以及深度递归Q网络(DRQN)和深度Q网络(DQN)优化算法相比,基于A3C的干扰消除算法具有更短的收敛时间、更高的执行效率以及更高的系统吞吐量,较3种基准方法在吞吐量性能上至少提高7%,能够有效地减少干扰对通信质量的不利影响。

面向射电天文的微秒量级射频信号检测系统

瞬态信号对射电天文观测的影响越来越大,开发射频信号检测系统,可提升瞬态干扰检测能力,为进一步的瞬态信号特征分析提供重要技术支撑。本文基于USRP X310和GNU Radio平台,采用多相滤波、快速傅里叶变换、积分处理等,开发了微秒量级射频信号检测系统,实现了140 MHz实时带宽、30 MHz~6 GHz工作带宽、0.5μs时间分辨率的信号检测,并通过实测给出了检测系统的幅度测量精度、动态范围。实例应用表明,本文系统可应用于电子设备的辐射发射测量和电磁环境瞬态信号测量,为射电望远镜电磁兼容设计和干扰缓减提供技术支持。