Parallel Technologies with Image Processing Using Inverse Filter

Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.

An improved particle filtering algorithm based on observation inversion optimal sampling

According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter,an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly,virtual observations were generated from the latest observation,and two sampling strategies were presented. Then,the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally,the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method,sampling particles can make full use of the latest observation information and the priori modeling information,so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter,the extended Kalman particle filter and the unscented particle filter.

Adaptive inverse control of random vibration based on the filtered-X LMS algorithm

Random vibration control is aimed at reproducing the power spectral density （PSD） at specified control points. The classical frequency-spectrum equalization algorithm needs to compute the average of the multiple frequency response functions （FRFs）, which lengthens the control loop time in the equalization process. Likewise, the feedback control algorithm has a very slow convergence rate due to the small value of the feedback gain parameter to ensure stability of the system. To overcome these limitations, an adaptive inverse control of random vibrations based on the filtered-X least mean-square （LMS） algorithm is proposed. Furthermore, according to the description and iteration characteristics of random vibration tests in the frequency domain, the frequency domain LMS algorithm is adopted to refine the inverse characteristics of the FRF instead of the traditional time domain LMS algorithm. This inverse characteristic, which is called the impedance function of the system under control, is used to update the drive PSD directly. The test results indicated that in addition to successfully avoiding the instability problem that occurs during the iteration process, the adaptive control strategy minimizes the amount of time needed to obtain a short control loop and achieve equalization.

Performance Analysis of an Inverse Notch Filter and Its Application to <i>F₀</i>Estimation

In this paper, we analyze an inverse notch filter and present its application to F0 (fundamental frequency) estimation. The inverse notch filter is a narrow band pass filter and it has an infinite impulse response. We derive the explicit forms for the impulse response and the sum of squared impulse response. Based on the analysis result, we derive a normalized inverse notch filter whose pass band area is identical to unit. As an application of the normalized inverse notch filter, we propose an F0 estimation method for a musical sound. The F0 estimation method is achieved by connecting the normalized inverse notch filters in parallel. Estimation results show that the proposed F0 estimation method effectively detects F0s for piano sounds in a mid-range.

A Novel Multifunction CFOA-Based Inverse Filter

We present a novel multifunction inverse biquad configuration based on current feedback operational amplifiers (CFOAs) and grounded passive elements. The proposed scheme can be used to realize inverse lowpass, inverse bandpass and inverse highpass filter functions. The relevant coefficients of the inverse filters are orthogonal adjustable by independent passive elements. All the passive elements in the proposed scheme are grounded to benefit easier electronic tunability. With the high input impedance and low output impedance properties, the scheme is input and output cascadable for voltage operation. The feasibility of the proposed scheme is demonstrated by HSPICE simulations.

DOA Estimation Algorithm Based on Adaptive Filtering in Spatial Domain

In this paper, a novel DOA estimation methodology based upon the technology of adaptive nulling antenna is proposed. Initially, the nulling antenna obtains the weight vector by LMS algorithm and power inversion criterion.Afterwards, reciprocal of the antenna pattern is defined as the spatial spectrum and the extracted peak values are corresponded to the estimated DOA. Through observation of the spectrum and data analysis of variable steps and SNRs, the simulation results demonstrate that the proposed method can estimate DOA above board. Furthermore, the estimation error of the proposed technique is directly proportional to step size and is inversely proportional to SNR. Unlike the existing MUSIC algorithm, the proposed algorithm has less computational complexity as it eliminates the need of estimating the number of signals and the eigenvalue decomposition of covariance matrix. Also it outperforms MUSIC algorithm, the recently proposed MUSIC-Like algorithm and classical methods by achieving better resolution with narrow width of peaks.

Reservoir history matching and inversion using an iterative ensemble Kalman filter with covariance localization

Reservoir inversion by production history matching is an important way to decrease the uncertainty of the reservoir description. Ensemble Kalman filter （EnKF） is a new data assimilation method. There are two problems have to be solved for the standard EnKF. One is the inconsistency between the updated model and the updated dynamical variables for nonlinear problems, another is the filter divergence caused by the small ensemble size. We improved the EnKF to overcome these two problems. We use the half iterative EnKF （HIEnKF） for reservoir inversion by doing history matching. During the H1EnKF process, the prediction data are obtained by rerunning the reservoir simulator using the updated model. This can guarantee that the updated dynamical variables are consistent with the updated model. The updated model can nonlinearly affect the prediction data. It is proved that HIEnKF is similar to the first iteration of the EnRML method. Covariance localization is introduced to alleviate filter divergence and spurious correlations caused by the small ensemble size. By defining the shape and size of the correlation area, spurious correlation between the gridblocks far apart is alleviated. More freedom of the model ensemble is preserved. The results of history matching and inverse problem obtained from the HIEnKF with covariance localization are improved. The results show that the model freedom increases with a decrease in the correlation length. Therefore the production data can be matched better. But too small a correlation length can lose some reservoir information and this would cause big errors in the reservoir model estimation.

Sequential Filtering for Surface Wind Speed Estimation from Ambient Noise Measurement

Many research results show that ocean ambient noise and wind speed are highly relevant, and the surface wind speed can be effectively inverted using ocean noise data. In most deep-sea cases, the ambient noise of medium frequency is mainly determined by the surface wind, and there is a conventional relationship between them. This paper gives an equation which shows this relationship firstly, and then a surface-wind inversion method is proposed. An efficient particle filter is used to estimate the speed distribution, and the results exhibit more focused close to the actual wind speed. The method is verified by the measured noise data, and analysis results showed that this approach can accurately give the trend of sea surface wind speed.

LINEARIZED INVERSE SCATTERING FOR ELASTIC PARAMETERS IN A HALF-SPACE WITH REFLECTION DATA

The multi-parameter inversion of elastic wave equation in a half-space within the Born approximation is studied. A method of simultaneously reconstructing the configurations of the density and Lame parameters of the medium was presented by use of a wideband measuring schemes in which transmitters and receivers scan over the half-space surface. It is shown that the reflected waves generated by horizontal and vertical pulses on the surface can be decomposed into P-->P, P-->S, S-->P and S-->S types of scattering components. As is expected, these components contain enough information for desired restruction. From them the density and two Lame parameters are determined explicitly, and the results obtained have the form of filtered back-propagation as in the acoustic diffraction tomography.

A Low Complexity Linear Moving Average Filtering Technique for PAR Reduction in OFDM Systems

In this paper, a linear moving average recursive filtering technique is proposed to reduce the peak-to-average power ratio (PAR) of orthogonal frequency division multiplexing (OFDM) signals. The proposed low complexity technique is analyzed in an oversampled OFDM system and a simple distribution approximation of the oversampled and linearly filtered OFDM signals is also proposed. Corresponding time domain linear equalizers are developed to recover originally transmitted data symbols. Through extensive computer simulations, effects of the new filtering technique on the oversampled OFDM peak-to-average power ratio (PAR), power spectral density (PSD) and corresponding linear equalizers on the frequency selective Rayleigh fading channel transmission symbol-error-rate (SER) performance are investigated. The newly proposed recursive filtering scheme results in attractive PAR reduction, requires no extra fast Fourier transform/inverse fast Fourier transform (FFT/IFFT) operations, refrains from transmitting any side information, and reduces out-of-band radiation. Also, corresponding linear receivers are shown to perform very close to their frequency domain counterparts.

Pre-stack AVO inversion with adaptive edge preserving smooth filter regularization based on Aki-Richard approximation

With the development of exploration of oil and gas resources,the requirements for seismic inversion results are getting more accurate.In particular,it is hoped that the distribution patterns of oil and gas reservoirs can be finely characterized,and the seismic inversion results can clearly characterize the location of stratigraphic boundaries and meet the needs of accurate geological description.Specifically,for pre-stack AVO inversion,it is required to be able to distinguish smaller geological targets in the depth or time domain,and clearly depict the vertical boundaries of the geological objects.In response to the above requirements,we introduce the preprocessing regularization of the adaptive edge-preserving smooth filter into the pre-stack AVO elastic parameter inversion to clearly invert the position of layer boundary and improve the accuracy of the inversion results.

Development of a Post-Processing Automation Procedure for the GPS-Based Travel Time Data Collection Technique

The travel time data collection method is used to assist the congestion management. The use of traditional sensors (e.g. inductive loops, AVI sensors) or more recent Bluetooth sensors installed on major roads for collecting data is not sufficient because of their limited coverage and expensive costs for installation and maintenance. Application of the Global Positioning Systems (GPS) in travel time and delay data collections is proven to be efficient in terms of accuracy, level of details for the data and required data collection of man-power. While data collection automation is improved by the GPS technique, human errors can easily find their way through the post-processing phase, and therefore data post-processing remains a challenge especially in case of big projects with high amount of data. This paper introduces a stand-alone post-processing tool called GPS Calculator, which provides an easy-to-use environment to carry out data post-processing. This is a Visual Basic application that processes the data files obtained in the field and integrates them into Geographic Information Systems (GIS) for analysis and representation. The results show that this tool obtains similar results to the currently used data post-processing method, reduces the post-processing effort, and also eliminates the need for the second person during the data collection.

白光显微干涉测量曲面样品形貌误差的校正方法

白光显微干涉术在平面阶跃型结构的形貌测量中具有显著优势。但在测量斜率变化的曲面样品时,由于物镜数值孔径的限制,样品表面反射光随着斜率的增大而减弱,干涉信号对比度降低,导致形貌测量结果的误差增大。基于表面传递函数(surface transfer function, STF)计算得到的逆滤波器可用于校正曲面样品的形貌测量误差,但现有方法的逆滤波器增益受限,无法有效提升频谱中的高频信号,对最大可测量斜率的提升有限。针对该问题,提取由白光干涉仪特性参数计算获得的虚拟STF的模作为振幅增益函数,由干涉图傅里叶变换得到的实测STF的相位作为相位补偿函数,形成虚实融合型逆滤波器,据此实现白光干涉仪曲面形貌测量误差的校正。应用该方法校正微球的形貌测量结果,校正后最大可测量斜率从8.09°提升到21.20°,均方根误差从0.545 5μm降低至0.175 9μm,实现了提升曲面样品的最大可测量斜率和减小测量误差的目的,有效提升了仪器针对曲面样品的测量范围。

基于车载激光点云的城市道路标线提取方法

针对车载激光点云道路标线反射强度特性,提出了一种基于车载激光点云的城市道路标线提取方法。具体而言,首先提出一种联合布料模拟滤波和高差偏度平衡的地面滤波方法,利用偏度平衡滤波的自适应性,剔除布料滤波后残留的低矮植被的问题;随后利用基于法向量密度聚类以提取路面点云,并通过反距离加权插值将路面点云转为强度特征图;为了缓解标线提取出的锯齿状现象,引入快速引导滤波来平滑道路标线的边缘信息;最后采用最大熵阈值分割和形态学比值滤波对道路标线进行精化处理。实验表明,该方法能够有效地提取出道路标线点云,提取的平均召回率为80.98%,平均准确率为96.89%,平均综合评定指标为88.19%,能够利用道路标线点云强度信息较为完整地提取出道路标线点云。

GNSS/SINS组合导航系统的改进变分贝叶斯自适应滤波算法

针对系统噪声及测量噪声统计量不准确的组合导航系统线性高斯状态模型,提出一种组合导航系统的改进变分贝叶斯自适应滤波算法(modified variational Bayesian adaptive filter,MVBAKF)。首先,选择Wishart分布作为已知均值的高斯分布协方差矩阵的共轭先验,并给出测量噪声方差、状态向量及其预测误差协方差矩阵的联合概率分布函数;然后,利用变分贝叶斯方法给出测量噪声方差及状态向量预测误差协方差矩阵的计算公式,进而提出具有迭代性质的MVBAKF算法;最后,进行基于MVBAKF算法的GNSS/SINS组合导航系统仿真实验。结果表明,相对于传统VBAKF算法,MVBAKF算法可较准确地估计测量噪声方差,有效克服系统噪声统计量不准确对滤波精度的影响,进而提高组合导航系统的滤波精度。

微波滤波器的装调融合建模与反演

微波滤波器作为关键的选频装置,在设计制造周期中存在一些突出问题,如设计制造周期数据割裂以及数据缺乏有效利用,从而导致设计制造阶段脱节,降低了设计制造迭代效率。文中提出了一种微波滤波器装调融合建模与反演方法,根据滤波器的设计信息、制造数据以及滤波器机理采用自顶向下的方式构建滤波器知识图谱,利用知识图谱对微波滤波器的数据与机理进行规范化存储;利用机理数据融合建模的方法构建微波滤波器正向演进模型;基于正向演进模型,结合渐进空间映射算法,构建微波滤波器快速装调反演模型。演进与反演实验结果表明,预测电性能与实际电性能基本重合,故障滤波器通过两次装调即可满足设计指标。该方法能够实现微波滤波器设计制造数据的有效关联、微波滤波器性能演进的有效预测以及快速装调反演,提高了设计制造周期数据的利用率,增强了滤波器设计与制造的协同。

非对称弦测法中基于梯度下降的轨道不平顺复原方法

本研究提出了一种基于梯度下降的轨道不平顺复原方法。基于非对称弦测法理论,轨道不平顺的复原过程可描述为一个欠定线性系统的优化问题。本文提出采用梯度下降法求解该优化问题,采用了考虑正则项的二次型代价函数。为了评估所提出方法的性能,本研究搭建了一个轨道检测小车,并在一个缩尺桥梁模型上进行了现场试验。通过与水准仪测量值的比较,验证了基于梯度下降轨道不平顺复原方法具有良好精度。与传统的需要专门设计逆滤波器的轨道不平顺复原方法相比,本研究所提出的方法物理意义明确,只需基于非对称弦测法原理建立优化模型,这表明本方法在现场实测中具有良好的可操作性。此外,重复性评估表明,在调整不同的弦长以及多次重复测量时,本研究所提出的方法可复现一致的轨道不平顺结果,表明该方法具有良好的测量重复性。本研究所提出的方法可对轨道不平顺进行准确复原,为轨道平顺性状态及时有效评估提供数据支撑,进而保证列车运行的平稳与安全。

竞赛用垃圾分拣智能车位姿优化设计

为解决生活垃圾清理中人力资源成本高、效率低的问题,提出一种垃圾分拣智能车设计方案。该研究对机械臂抓取效率和精度进行提升,优化机械臂的运动学求解算法和结构设计;为解决智能车因轮子打滑导致的定位偏差,通过实测位移数据,使用MATLAB计算拟合曲线四次多项式进行优化;为处理陀螺仪零漂的问题,利用卡尔曼滤波算法进行优化。通过综合改进,使智能车机械臂和车身位姿控制更精准可靠。结果表明,设计的垃圾分拣智能车能获得准确可靠的位姿信息,从而高效完成分拣任务。

A Novel Method for the Reconstruction of Road Profiles from Measured Vehicle Responses Based on the Kalman Filter Method

The estimation of the disturbance input acting on a vehicle from its given responses is an inverse problem.To overcome some of the issues related to ill-posed inverse problems,this work proposes a method of reconstructing the road roughness based on the Kalman filter method.A half-car model that considers both the vehicle and equipment is established,and the joint input-state estimation method is used to identify the road profile.The capabilities of this methodology in the presence of noise are numerically demonstrated.Moreover,to reduce the influence of the driving speed on the estimation results,a method of choosing the calculation frequency is proposed.A road vibration test is conducted to benchmark the proposed method.