Rapid detection of Pseudomonas aeruginosa by cross priming amplification

Pseudomonas aeruginosa(PA)is an opportunistic pathogen of humans and animals and a common source of nosocomial infections especially of the respiratory tract.Pseudomonas aeruginosa is also a major bacterial disease of poultry and in particular,eggs and newly hatched chicks.In this study,we developed a simple,accurate and rapid molecular detection method using cross priming amplification(CPA)with a nucleic acid test strip to detect P.aeruginosa.The assay efficiently amplified the target gene within 45 min at 62℃only using a simple water bath.The detection limit of the method was 1.18x 102 copiesμL^-1 for plasmid DNA and 4.4 CFU mL^-1 for bacteria in pure culture,and was 100 times more sensitive than conventional PCR.We screened 83 clinical samples from yellow-feather broiler breeder chickens and hospitalized/treated dogs and cats using CPA,PCR and traditional culture methods.The positive sample ratios were 15.3%(13/83)by CPA,13.3%(11/83)by PCR and 12.1%(10/83)by the culture method.The established CPA method has significant advantages for detecting P.aeruginosa.The method is easy to use and possesses high specificity and sensitivity without the requirements of complicated experimental equipment.The PA-CPA assay is especially fit for outdoor and primary medical units and is an ideal system for the rapid detection and monitoring of P.aeruginosa.

Digital image processing-based automatic detection algorithm of cross joint trace and its application in mining roadway excavation practice

This paper proposes a digital image processing-based detection algorithm for cross joint traces of coal roadway heading face.Initially,the acquired images were preprocessed,i.e.,adaptive correction was conducted for non-uniform illumination images based on the 2D gamma function.The edge detection algorithm was then applied to extract the edges of the structural plane,followed by the filtration of the non-structural plane noises.Moreover,the Hough transform algorithm was applied to extract the linear edges;finally,the edges were locally connected in accordance with the angle and distance criteria.The experimental results show that this algorithm can be used to reduce the noise caused by non-uniform illumination and avoid the mutual interference of multi-scale edges,so as to effectively extract the traces of the cross joint.Furthermore,Q-system and rock mass rating(RMR),were applied to conduct a quantitative evaluation on the stand-up time of unsupported roof in the four test images.The Q-system quality scores are 26.7,43.3,3.1,and 6.7,and the RMR quality scores are 56.84,58.73,48.42,and 51.42,respectively.The stand-up time of unsupported roofs with a span of 4.6 m are 30,36,7.7 and 14 d,respectively.

Linearized Phase Detector Zero Crossing DPLL Performance Evaluation in Faded Mobile Channels

Zero Crossing Digital Phase Locked Loop with Arc Sine block (AS-ZCDPLL) is used to linearize the phase difference detection, and enhance the loop performance. The loop has faster acquisition, less steady state phase error, and wider locking range compared to the conventional ZCDPLL. This work presents a Zero Crossing Digital Phase Locked Loop with Arc Sine block (ZCDPLL-AS). The performance of the loop is analyzed under mobile faded channel conditions. The mobile channel is assumed to be two path fading channel corrupted by additive white Gaussian noise (AWGM). It is shown that for a constant filter gain, the frequency spread has no effect on the steady state phase error variance when the loop is subjected to a phase step. For a frequency step and under the same conditions, the effect on phase error is minimal.

Precise calibration of zero-crossing temperature and drift of an ultralow expansion cavity with a clock transition spectrum

We report a clock transition spectrum approach, which is used to calibrate the zero-crossing temperature and frequency drift of an ultralow expansion（ULE） cavity with a Hertz level resolution. With this approach, the linear and nonlinear drifts of the ULE cavity along a variety of controlled temperatures are clearly presented. When the controlled temperature of ULE cavity is tuned away from the zero-crossing temperature of the ULE cavity, the cavity shows larger and larger nonlinear drift. According to our theoretical analysis and experimental results, we investigate more details of the drift property of the ULE cavity around the zero-crossing temperature, which has seldom been explored before. We can definitely conclude that the zero-crossing temperature of our ULE cavity used in an ytterbium（Yb） lattice clock is around 31.7℃.

Role of Cross Layer Based Intrusion Detection System for Wireless Domain

Wireless mesh networks are very common both for organizations and individuals. Many laptops, computers have wireless cards pre-installed for buyer. However a wireless networking has many security issues. An intrusions detection system aim to detect the different attacks against network and system. An intrusion detection system should be capable for detecting the misuse of the network whether it will be by the authenticated user or by an attacker. They detect attempts and active misuse either by legitimate users of the information systems or by external. The present paper deals with cross layer based intrusion detection system for wireless domain—a critical anlaysis. The present paper deals with role of cross layer based intrusion detection system for wireless domain.

Cross correlation mediated by distant Majorana zero modes with no overlap

Existing studies via shot noise calculation conclude that the cross correlation between the currents in the two leads connected by a pair of Majorana zero modes(MZMs)vanishes when their coupling energy𝜖∈_(M)→0.Motivated by the intrinsic nature of nonlocality of the MZMs,we revisit this important problem and propose an experimental scheme to demonstrate the nonvanishing cross correlation even at the limit𝜖∈_(M)→0.The proposed scheme employs the Andreevprocess-associated branch circuit currents,which are theoretically obtained by applying a decomposition analysis for the total currents while are accessible directly in practical measurement.For different bias voltage setup,we find intriguing results of both negative and positive correlations and carry out simple physical understanding using a quantum jump technique.Importantly,combining together with the evidence of the zero-bias-peak of conductance,the nonlocal cross correlation predicted in this work can help to confirm the existence of the nonlocal MZMs.

Simulation of distributed optical fiber disturbance detection system based on Sagnac/Mach-Zehnder interferometer and cross-correlation location

A distributed optical fiber disturbance detection system consisted of a Sagnac interferometer and a Mach-Zehnder interferometer is demonstrated. Two interferometers outputs are connected to an electric band-pass filter via a detector respectively. The central frequencies of the two filters are selected adaptively according to the disturbance frequency. The disturbance frequency is obtained by either frequency spectrum of the two interferometers outputs. An alarm is given out only when the Sagnac interferometer output is changed. A disturbance position is determined by calculating a time difference with a cross-correlation method between the filter output connected to the Sagnac interferometer and derivative of the filter output connected to the Mach-Zehnder interferometer. The frequency spectrum, derivative and cross-correlation are obtained by a signal processing system. Theory analysis and simulation results are presented. They show that the system structure and location method are effective, accurate, and immune to environmental variations.

Application of Weighted Cross-Entropy Loss Function in Intrusion Detection

The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence problem. Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network;Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection. In order to compare the effect of the experiment, the KDDcup99 data set, which is commonly used in intrusion detection, is selected as the experimental data and use accuracy, precision, recall and F1-score as evaluation parameters. The experimental results show that the model using the weighted cross-entropy loss function combined with the Gelu activation function under the deep neural network architecture improves the evaluation parameters by about 2% compared with the ordinary cross-entropy loss function model. Experiments prove that the weighted cross-entropy loss function can enhance the model’s ability to discriminate samples.

Improving the target position detection in the crossed array trackers seeker using the pulses number distribution in the FOV

Seeking in a field of view(FOV) is influenced by the existence of jammers,noise,shine background or flying perturbations.All these factors may push the target out of the FOV and cause missing the target.In all the seekers the FOV is not fully exploited which means the target can be missed before becoming out of the FOV,this results of the nonlinearity of the reticle structure.In this paper,a novel method of the target position detection a crossed four slits or crossed array trackers(CAT) seeker will be designed,simulated and evaluated.The idea of this method depends on dividing the FOV into main regions up to a certain parameter,which is the pulses number;then,each main region will be divided into sub-regions up to a second parameter which will be the pulses distribution a spin period.The errors sources will be discussed and evaluated.Other new idea will be applied which is exploiting some area of the FOV where a part of the position data is missed in the information signal by pushing the target to the region where the information signal carries the total position data.

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.

基于过零点斜率加窗解调的ACARS信号接收方法

飞机通信寻址与报告系统(ACARS)允许飞机和地面站之间发送和接收报文,在保障飞行安全、实现空中交通管制等方面具有重要意义。通信链路的噪声和频偏是影响ACARS信号接收性能的关键因素。针对ACARS信号接收过程中的失真等问题,深入研究频偏、噪声等因素与ACARS报文接收性能之间的关系;在充分考虑噪声与多普勒频移等因素的基础上,提出基于过零点斜率加窗解调的ACARS信号接收方法,并从理论和仿真分析2方面探讨不同方法的抗频偏抗噪声能力;设计并搭建基于软件无线电平台的ACARS信号接收系统。通过对ACARS报文接收性能的仿真分析和真实报文接收性能的对比分析,表明所提方法在提升抗噪声和频偏能力、降低误码率等方面均具有优势。

AF-CenterNet:基于交叉注意力机制的毫米波雷达和相机融合的目标检测

对于自动驾驶领域而言,确保在各种天气和光照条件下精确检测其他车辆目标是至关重要的。针对单个传感器获取信息的局限性,提出一种基于cross-attention注意力机制的融合方法(AF),用于在特征层面上融合毫米波雷达和相机信息。首先,将毫米波雷达和相机进行空间对齐,并将对齐后的点云信息投影成点云图像。然后,将点云图像在高度和宽度方向上进行扩展,以提高相机图像和点云图像之间的匹配度。最后,将点云图像和相机图像送入包含AF结构的CenterNet目标检测网络中进行训练,并生成一个空间注意力权重,以增强相机中的关键特征。实验结果表明,AF结构可以提高原网络检测各种大小目标的性能,特别是对小目标的检测提升更为明显,且对系统的实时性影响不大,是提高车辆在多种场景下检测精度的理想选择。

图像语义特征引导与点云跨模态融合的三维目标检测方法

受到场景的复杂性和目标尺度变化、遮挡等影响,三维目标检测仍面临着诸多挑战.虽然跨模态特征融合图像和激光点云信息能够有效地提升三维目标检测性能,但在融合效果和检测性能上仍有待提升,为此,提出图像语义特征引导与点云跨模态融合的三维目标检测方法.首先设计图像语义特征学习网络,采用双分支自注意力并行计算方式,实现全局语义特征增强,降低目标错误分类;然后提出图像语义特征引导的局部融合模块,采用元素级数据拼接将检索的图像局部语义特征引导融合点云数据,更好地解决跨模态信息融合存在的语义对齐问题;提出多尺度再融合网络,设计融合特征与激光雷达点云交互模块,学习融合特征和不同分辨率特征间的再融合,提高网络的检测性能;最后采用4种任务损失实现anchor-free的三维目标检测.在KITTI和nuScenes数据集中与其他方法进行对比,针对三维目标检测准确率达87.15%,并且实验结果表明,文中方法优于对比方法,具有更优的三维检测性能.

基于向量叉乘标签分配的遥感图像目标检测算法

近年来遥感图像目标检测受到了广泛的关注,主流的遥感图像目标检测器通过预设锚框与真实框之间的交并比(IoU)进行正负样本的划分。为了解决基于IoU的标签分配方法在遥感图像小而密集目标中存在复检和漏检的问题,本文提出了一种基于向量叉乘标签分配的遥感图像目标检测算法YOLOXR。首先,提出了一种标签粗分配策略,通过向量叉乘的方法判断特征图的像素点是否在旋转目标内或者目标中心点附近的旋转正方形框内,从而确定其是否为候选正样本。其次,为了降低边缘低质量候选正样本对标签分配的影响,提出了旋转中心度量方法,通过向量叉乘判断像素点距离中心点的远近程度进而赋予不同的权重。最后,基于最优传输的方法(sim OTA)选取真实框和样本点的最优匹配对,使得总体代价最小,进而为旋转目标分配合适的标签。此外,为了解决旋转IoU损失不可导以及Smooth L1损失难以权衡旋转框各个参数的问题,通过计算真实框和预测框二维高斯分布的Kullback-Leibler散度(KLD)来替代IoU。在公开的遥感图像目标检测数据DOTA、HRSC 2016和UCAS-AOD上的大量实验表明,所提方法优于目前绝大多数旋转目标检测算法。

定点形变数据中暂态短持时异常信号的检测方法研究

分别将线性叠加及零延迟相乘算法用于人工合成数据中的暂态信号检测实验,对比2种算法在暂态信号识别中的效果。结果表明,将同台多道、多台多道数据进行零延时相乘能够更有效地压制数据中的干扰和噪声,使准同步暂态信号得到放大凸显,实现暂态异常信号的初步检测与识别。在此基础上,利用零延时相乘算法处理2002~2022年云南定点形变观测数据,从中识别出11组暂态短持时信号,并进一步分析信号与云南境内M_(S)5.0以上地震的时空关联性。

基于跨模态注意力融合的煤炭异物检测方法

为解决原煤智能化洗选过程中煤流中夹杂的异物对比度低、相互遮挡导致异物图像检测时特征提取不充分的问题,提出了一种基于跨模态注意力融合的煤炭异物检测方法。通过引入Depth图像构建RGB图像与Depth图像的双特征金字塔网络(DFPN),采用浅层的特征提取策略提取Depth图像的低级特征,用深度边缘与深度纹理等基础特征辅助RGB图像深层特征,以有效获得2种特征的互补信息,从而丰富异物特征的空间与边缘信息,提高检测精度;构建了基于坐标注意力与改进空间注意力的跨模态注意力融合模块(CAFM),以协同优化并融合RGB特征与Depth特征,增强网络对特征图中被遮挡异物可见部分的关注度,提高被遮挡异物检测精度;使用区域卷积神经网络(R-CNN)输出煤炭异物的分类、回归与分割结果。实验结果表明:在检测精度方面,该方法的AP相较两阶段模型中较优的Mask transfiner高3.9%;在检测效率方面,该方法的单帧检测时间为110.5 ms,能够满足异物检测实时性需求。基于跨模态注意力融合的煤炭异物检测方法能够以空间特征辅助色彩、形状与纹理等特征,准确识别煤炭异物之间及煤炭异物与输送带之间的差异,从而有效提高对复杂特征异物的检测精度,减少误检、漏检现象,实现复杂特征下煤炭异物的精确检测与像素级分割。

注意力引导的多尺度红外行人车辆实时检测

红外成像技术通过捕捉目标热辐射特征进行成像,能实现复杂道路场景下的目标监测和道路冗杂信息滤除。针对红外行人和车辆目标检测模型参数量大、依赖高性能GPU资源和检测速度慢等问题,提出了一种注意力引导的多尺度红外行人车辆实时检测模型。首先,为精确匹配校准红外行人和车辆目标尺度与锚框尺寸,利用K-Means++算法对红外行人和车辆目标尺度进行先验框预置参数重聚类生成,并设计128×128精细尺度检测层;其次,设计注意力引导广域特征提取模块增强模型特征提取能力和空间及通道信息聚焦能力;随后,构建跨空间感知模块引入空间信息感知,强化不同尺度空间下的目标的特征表达能力;最后,针对资源受限设备,通过4倍通道剪枝方法降低模型参数量,增强移动端算法部署适应性。实验结果表明:所提IRDet算法与基准方法相比,模型平均检测精度提升4.3%,达到87.4%,模型权重值压缩60.4%,降至5.7 MB。

MMCSC:一种跨模态的假新闻检测方法

目前基于新闻内容的假新闻检测方法没有考虑到不同模态更高层的语义关联,缺少可以依据的信息对新闻进行判断,从而缺乏对有重要辨别特征的新闻的社交网络信息进行有效使用.针对这个问题,提出了基于新闻内容的假新闻检测方法,通过提取文本、图像和视频等多模态新闻的高层语义特征,分析不同模态高层语义信息,设计跨模态主题一致性和跨模态情感一致性计算方法.在此基础上,设计了一种跨模态内容语义一致性的假新闻检测模型MMCSC(multi-modal feature content semantic consistency).实验证明,相比于传统方法,所提出的MMCSC有较好的检测效果.

基于多度量多模型图像投票的织物表面瑕疵检测方法

为解决自动化生产线上织物表面瑕疵检测准确率低和计算速度慢的问题,利用织物表面具有周期纹理的特性提出了一种改进的RANSac检测方法,即多度量多模型图像投票。首先将输入图像裁剪为尺寸一致的子图,计算出子图多维度量的输出值矩阵;然后与改进RANSac计算出的无瑕疵背景的多维度量标准值分别对应作差,采用投票得出每张子图的基础分;再将其在4个记数模型下得到的综合评分排序,根据顺序和偏移量在输出端得到外点所代表的瑕疵子图。实验结果表明:在自采样的织物瑕疵数据集上,选择单度量和单模型的预测精度平均可达到90.9%,平均预测时间达到0.139 s,综合多度量多模型投票的平均预测精度可达到92.7%。该算法不需要大量前期数据进行训练,适用于纯色和条纹状织物的实时表面缺陷检测。

基于VMD的广义三次互相关管道泄漏定位检测

针对天然气管道泄漏检测声波定位技术中,二次互相关时延估计算法存在较大误差的问题,提出了一种基于变分模态分解(VMD)结合广义三次互相关的时延估计算法。该方法首先利用VMD算法对两路信号进行分解并重构信号;其次,在二次互相关的基础上再进行一次相关,并在互相关算法的峰值检测阶段引入希尔伯特变换(HT),对峰值进行尖锐化处理,成为一种新型的广义三次互相关时延估计算法。通过对平台搭建的油气管道泄漏检测系统采集数据进行模拟试验,分析了各算法的精度。试验表明,相较于二次互相关,改进广义三次互相关时延估计算法定位平均精度有明显的提升,有着更高的精度和更好的抗噪性能,在天然气管道泄漏定位方面有着更广泛的应用前景。