Automatic de-noising and recognition algorithm for drilling fluid pulse signal

Wavelet forced de-noising algorithm is suitable for denoising of unsteady drilling fluid pulse signal, including baseline drift rectification and two-stage de-noising processing of frame synchronization signal and instruction signal. Two-stage de-noising processing can reduce the impact of baseline drift and determine automatic peak detection threshold range for signal recognition by distinguishing different features of frame synchronization pulse and instruction pulse. Rising and falling edge relative protruding threshold is defined for peak detection in signal recognition, which can make full use of the degree of the signal peak change and detect peaks flexibly with rising and falling edge relative protruding threshold combination. A synchronous decoding method was designed to reduce position uncertainty of the frame synchronization pulse and eliminate the accumulative error of time base drift, which determines the first instruction pulse position according to position of the frame synchronization pulse and decodes subsequent instruction pulse by taking current instruction pulse as new bit synchronization pulse. Special tool software was developed to tune algorithm parameters, which has a decoding success rate of about 95% for the universal coded signals. For the special coded signals with check byte, decoding success rate using the automatic threshold adjustment algorithm is as high as 99%.

An Improved Image Denoising Method Based on Wavelet Thresholding

VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.

A New Image Denoising Scheme Using Soft-Thresholding

The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. The experimental results show that our proposed scheme has better performance than the VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e., visual quality of the image.

A Novel Peak-to-Average Power Ratio Reduction Scheme via Tone Reservation in OFDM Systems

In this paper, a novel signal-to-clipping noise ratio and least squares approximation tone reservation scheme(SCR-LSA TR) is proposed to reduce the peak-to-average power ratio for orthogonal frequency division multiplexing systems. During the SCR procedure, only the element with the maximal amplitude is picked for processing, which not only decreases the algorithm complexity, but also helps to overcome the BER deterioration. With the LSA method, the amplitude of the peak-cancelling signals can approximate to that of the original clipping noise as much as possible. Through the combination of the optimization factor in the LSA method, the classic SCR method can achieve better PAPR reduction with faster convergence. Simulation results show that the proposed SCR-LSA TR scheme has less in-band distortion and smaller out-of-band spectral radiation. The BER of the proposed scheme shows a better performance especially under the 16-QAM over the additive white Gaussian noise channel.

Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

A Robust Denoising Algorithm for Sounds of Musical Instruments Using Wavelet Packet Transform

In this paper, a robust DWPT based adaptive bock algorithm with modified threshold for denoising the sounds of musical instruments shehnai, dafli and flute is proposed. The signal is first segmented into multiple blocks depending upon the minimum mean square criteria in each block, and then thresholding methods are used for each block. All the blocks obtained after denoising the individual block are concatenated to get the final denoised signal. The discrete wavelet packet transform provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle detail of the signal but decision of optimal decomposition level is very important. When the sound signal corrupted with additive white Gaussian noise is passed through this algorithm, the obtained peak signal to noise ratio (PSNR) depends upon the level of decomposition along with shape of the wavelet. Hence, the optimal wavelet and level of decomposition may be different for each signal. The obtained denoised signal with this algorithm is close to the original signal.

小型磁选态铯原子钟性能测试数据的统计与分析

LIP Cs-3000是一种国产的小型磁选态铯原子钟,在时频计量、卫星导航等领域获得应用。对近期交付的LIP Cs-3000性能测试数据进行统计与分析,得到了准确度的分布与范围,同时得到了峰谷比、信噪比、Ramsey线宽等指标的分布。从理论上估计了铯原子钟输出信号的稳定度指标范围,验证了该范围与实际测量相符。同时对电子倍增器电压每日增幅进行了统计,结合装铯量给出了LIP Cs-3000铯钟倍增器寿命的分布。统计与分析结果不仅有助于磁选态铯钟的性能提高,也有助于其他小型铯原子钟的性能提升。

矿场压裂停泵水击信号滤波效果评价指标研究

采用水击压力波监测方法进行压裂诊断时,为了准确评价停泵水击压力波信号滤波算法的性能,针对国内某水平井的实测水击信号及其对滤波结果的实际需求,采用倒谱响应分辨率R_(C)和倒频率峰值信噪比R_(QSN)作为水击信号滤波效果的评价指标,以该指标和滤波效果的同步性为基本依据,根据指标对滤波效果的灵敏性评价滤波指标的可靠程度。研究结果表明,高频水击信号的R_(C)和R_(QSN)与井场信号滤波效果正相关,且呈现出良好的灵敏性,因此能够采用倒谱响应分辨率和倒频率峰值信噪比来评价矿场压裂停泵水击压力波高频信号的滤波效果。研究结果为现场信号特征分析、滤波算法优化及滤波模型现场应用的有效性评估提供了技术途径。

基于分式二次规划的互模糊函数赋形方法

在开展认知雷达波形设计时,由于发射波形与接收滤波器的非匹配体制,互模糊函数赋形相比传统模糊函数赋形优化自由度更高。该文针对强杂波条件下微弱运动目标检测问题,以最大化信干噪比为优化准则,提出了一种联合发射相位编码序列与接收滤波器设计的互模糊函数赋形方法。在恒模约束下,优化问题被建模为二次分式规划形式;然后通过引入辅助变量,并利用共轭梯度法求解Stiefel流形空间上的最小化问题,非凸优化据此转化为恒模约束二次优化问题;通过交替循环和类幂迭代算法求得最优解。此外考虑到发射波形受硬件限制而难以实现严格恒模,该文构建了一种低峰均比约束二次优化问题模型,并利用最近邻向量法求得最优解。最后,不同参数下的仿真与实测数据实验表明,该文赋形方法相较于传统方法具有较高的信干噪比增益和收敛速度。

一种结合卷积自编码和补丁惩罚的生成对抗网络单图像去雨方法

针对传统的图像去雨方法存在去雨图像失真、生成伪影等问题,提出一种结合卷积自编码和补丁惩罚的生成对抗网络单图像去雨方法。首先,该方法采用卷积自编码组成生成器网络,使用对称跳跃连接提高生成器网络的训练效率和收敛性能,实现对图像细节信息和二维信号空间信息的重构;其次,引入马尔可夫鉴别器在图像补丁层次上进行惩罚,去除生成图像中的伪影;最后,提出一种新的精细化损失函数参与训练网络模型,进一步增强模型的去雨深度。采用峰值信噪比和结构相似性作为模型的评价标准,实验结果表明,该方法在现实雨图和合成雨图的去雨处理上都有良好的表现,基本还原了图像细节内容,并保证了较高的视觉质量。

基于降噪技术的高光谱图像实时优化研究

为了获得理想的高光谱图像,针对当前高光谱图像优化方法存在的问题,设计了基于降噪技术的高光谱图像实时优化方法。首先分析当前高光谱图像优化的研究进展,找到当前方法存在的不足,采集高光谱图像,采用自适应阈值的小波变换对高光谱图像进行去噪处理,以改善高光谱图像质量,然后采用Retinex理论模型对去噪后高光谱图像进行增强操作,丰富高光谱图像细节信息,最后采用卷积神经网络进行了高光谱图像分类,测试结果表明:本方法优化后高光谱图像的峰值信噪比和结构相似度均值分别为31.18和0.981,不但提升了高光谱图像质量,而且使得高光谱图像分类正确率超过了92%,高光谱图像优化时间控制在4.5 s以内,相对于其他高光谱图像优化方法,具有十分明显的优越性。

基于拉曼光谱数据处理和谱峰识别的变压器油绝缘老化研究

针对变压器油的拉曼光谱分析通常受到噪声和荧光背景等的干扰以及谱峰位置难以识别的问题,提出了一种改进的数据处理和谱峰识别算法,用于变压器油老化评估时的拉曼光谱分析。提出一种自适应Savitzky-Golay滤波法,引入自适应窗口大小拉曼光谱数据进行去噪处理。采用改进的多项式拟合算法对去噪后的数据进行去除荧光背景处理,减小荧光背景对拟合结果的影响。通过数据点与期望的拉曼信号的接近程度为每个数据点赋予权重,以实现更准确的去荧光背景处理。利用谱峰识别技术判别变压器油的老化程度,采用大小两种尺度高斯窗口判别法来识别谱峰,并结合局部加权信噪比(local weighted signal-to-noise ratio,LW_SNR)来判断疑似拉曼谱峰的真实性。最后通过实验验证了所提算法在变压器油老化评估中的有效性。

一种基于改进SIFT的视频稳像方法

为提高计算效率并保持良好的稳像效果,本文提出一种基于改进SIFT的视频稳像方法。首先对SIFT进行改进,并命名为BO-SIFT(Binarized Octagonal SIFT)。该算法引入了同心八边形环特征描述子,通过降维和二值化对特征向量进行处理,然后使用汉明距离进行特征点匹配,有效缩短了描述和匹配时间。其次将BO-SIFT算法应用于视频稳像,提取视频帧的特征点进行匹配,并计算出帧与帧之间的运动偏移量,以此实现运动估计。最后采用卡尔曼滤波器对估计出的运动偏移量进行平滑处理,并利用仿射变换对视频帧进行逆向补偿,从而得到稳定的图像序列。实验结果表明:相较于原始的SIFT算法,BO-SIFT算法使稳像时间减少了56.404%;相较于现有的较好算法,BO-SIFT算法稳像后的视频具有更高的平均峰值信噪比。此外,本文算法在不同视频上进行稳像效果测试,也具有一定的可靠性和优越性。

基于回溯原子支撑集的广义正交匹配追踪算法研究

为解决广义正交匹配追踪算法(gOMP)在迭代过程中无法删除匹配错误原子问题,提出了一种新的算法:基于回溯原子支撑集的广义正交匹配追踪算法(BgOMP)。该算法在每次迭代选择原子前,首先检验支撑集中是否存在由先前迭代错误选择的原子,并据此更新原子支撑集。这一改进策略允许算法在每次迭代中回溯并纠正先前的错误。通过仿真实验,使用一维信号的成功重构概率和二维信号的峰值信噪比作为评价指标来验证BgOMP算法的性能,实验结果表明:与原始的gOMP算法相比,BgOMP算法显著提高了信号的重构性能。

基于前景区域生成对抗网络的视频异常行为检测研究

为提高视频异常行为检测的准确率,本文提出了一种基于前景区域生成对抗网络的改进方法。通过提取实际视频帧的前景和背景掩码,确定生成对抗网络输出视频帧的待检测前景区域。针对待检测前景区域,应用前景区域峰值信噪比准则,计算异常行为检测得分,完成视频异常行为检测。实验结果表明,本文的检测方法在Avenue数据集、UCSD-Ped1数据集、UCSD-Ped2数据集上均能有效提高视频异常行为检测准确率,并能降低检测运行时间。

基于多路特征渐进融合和注意力机制的轻量级图像超分辨率重建

为进一步探索在计算和存储资源受限设备上应用超分辨率方法的可能性,本研究聚焦于深度卷积神经网络技术在单图像超分辨率中的应用,特别是如何在不显著增加网络规模的情况下,提升网络的性能。本文提出一种新的基于多路特征渐进融合和注意力机制的轻量级单图像超分辨率方法(multi-path feature fusion and attention mechanism,MPFFA)。MPFFA包括一个多路特征渐进融合块(multi-path feature progressive fusion,FPF),可以通过前面的特征,多路渐进地引导和校准后面特征的学习;还包括一个多路特征注意力机制(multi-path feature attention mechanism,FAM),通过加权拼接多路特征通道,可以提高特征信息的利用率和特征表达能力。实验结果表明:MPFFA显著优于当前其他代表性的方法,在模型复杂度和性能间达到了更好的平衡。本文提出的模型能够更好地应用于计算和资源受限的设备上。

视觉传达下的低分辨率激光图像高分辨率重建方法

当前低分辨率激光图像重建效果不理想,重建精度低,重建时间过长,为了克服当前低分辨率激光图像重建的缺陷,以提高低分辨率激光图像重建精度为目标,提出了视觉传达下的低分辨率激光图像高分辨率重建方法。首先采集待重建的低分辨率激光图像,并采用小波变换算法对原始低分辨率激光图像进行预处理,消除噪声对激光图像干扰,然后采用Retinex理论对预处理后的激光图像进行重建,并引入视觉传达技术的直方图均衡化法改善激光图像的对比度,最后进行了低分辨率激光图像重建仿真实验,实验结果表明:本方法较好地解决了低分辨率激光图像重建过程中难题,激光图像清晰度得到了明显改善,结构相似性超过了0.97,峰值信噪比达到了28 dB以上,激光图像重建时间控制在10 s以内,重建整体性能要远远优于其他低分辨率激光图像重建方法,具有更高的实际应用价值。

融合注意力机制的GAN病虫害图像超分辨率重建

收集咖啡和柑橘病虫害样本图片,利用TensorFlow深度学习框架,在原始SRGAN(Super-resolution generative adversarial networks)的超分辨率重建网络里加入了注意力模块,对重建图像视觉质量和峰值信噪比(PSNR)、结构化相似性(SSIM)指标进行分析。结果表明,设计的模型和原始SRGAN模型对比之后峰值信噪比提高了2.23,结构相似性提高了7%。在细节纹理方面可以获得更好的视觉效果,重建后的图像识别准确率提高了约4.42个百分点。因此,设计的模型可以对小样本性质的植物病虫害样本进行扩充。

基于生成对抗网络的船舶可见光图像到红外图像的转换

为了解决红外制导研究中舰船图像样本数量不足的问题,提出一种面向舰船图像的改进的生成对抗网络(generative adversarial network,GAN),能够生成高质量的红外图像。首先转换可见光图像颜色空间以更好地捕捉夜间低亮度下图像的轮廓信息,然后引入残差块生成网络降低低像素的可见光图像对生成的红外图像的影响并加深网络层数以更好地学习深层映射关系,最后引入更平滑的损失函数加快收敛速度,提高生成红外图像目标边缘清晰程度。在制作的无人机拍摄的红外可见光配对的数据集进行测试,改进后的方法平均生成图像峰值信噪比(peak signal to noiseratio,PSNR)提升20.3%,结构相似性度量(structural similarity,SSIM)提升30.4%。结果表明改进的网络可以生成质量更高的红外仿真图像,用于目标检测等任务有更好的效果。

基于纳米微晶玻璃的空间悬浮显示

本文介绍了基于纳米微晶玻璃的空间悬浮显示的研究。使用纳米微晶玻璃作为悬浮显示的承载介质,并设计其实验系统,引入峰值信噪比的图像评价指标,测量激光功率对图像质量的影响,实现高清晰度、高分辨率的空间悬浮显示。