Development and Evaluation of a Loop-mediated Isothermal Amplification Assay for the Rapid Detection and Identification of Pectobacterium carotovorum on Celery in the Field

Pectobacterium carotovorum is the causal agent of bacterial soft rot in a wide range of vegetable host species.Once P.carotovorum infects the plant,the spread of the disease is difficult to control.In this study,a rapid and sensitive method based on loop-mediated isothermal amplification(LAMP)was developed for detecting P.carotovorum in celery with soft rot using a primer set designed from the pmrA conserved sequence of P.carotovorum.The specificity of the LAMP primer set for P.carotovorum was extensively validated on both P.carotovorum strains and nontarget strains.The sensitivity was 1 pg of P.carotovorum genomic DNA,which demonstrated 10 times more sensitive than the conventional PCR assay.LAMP was also used to detect P.carotovorum in bacterial suspension.The lowest detection concentration was 104 CFU·mL^−1.In addition,a LAMP assay,in conjunction with a crude DNA extraction method,was successfully performed on P.carotovorum-infected samples derived from both artificially and naturally infected plants.In summary,the LAMP assay established in this study constitutes a simple,sensitive,and rapid method for the detection of P.carotovorum,and has potential application in the control of celery soft rot disease through early detection.

Technology and equipment development in laser-induced fluorescencebased remote and field detection of biological aerosols

Nowadays,biohazard events caused by laboratory pathogen leaks and infectious disease transmission have become an increasingly important threat to human safety.The principal form of transmission of pathogenic microorganisms is via aerosols,which could spread over a wide area within a short time.The system of biological aerosol recognition mainly involves early warning,field detection,as well as sampling and laboratory testing.Early warning about biological aerosols is essential for establishing a timely response.Once the presence of a biological aerosol is suspected,field detection is immediately performed to confirm the occurrence of the aerosol.Then,the aerosol is sampled using air sampling devices for laboratory identification of the microbial type.This review specifically intends to introduce the technologies and equipment for biological aerosol early warning and field detection based on laser-induced fluorescence(LIF).

Application of wavelet transform modulus maxima in boundary detection of potential fields

This study presents the use of the method of wavelet transform modulus maxima(WTMM) to detect boundaries of potential field sources.The boundaries of causative sources can be judged by calculating the local modulus maxima of wavelet coefficients at different scales.For the potential field data with noise,the detected boundaries at small scales are easy to be distorted by noise,however,at large scales,the noise can be suppressed greatly and presents more accurate boundary detection results.Therefore,we can get a better boundary judgment by considering the detected boundaries at all scales.Applying the WTMM method to synthetic models and a real data set of Meishan iron deposit,both get a good effect.

Edge detection using directional eigenvalues of potential field gradient tensor data

Edge detection of potential field interpretation is an important task. The traditional edge detection methods have poor ability in outlining weak amplitude anomalies clearly. The resolved edges position is blurred.We purposed new edge detection methods based on directional eigenvalues of potential field gradient tensor for the causative sources. In order to balance strong and weak amplitude anomalies simultaneously,we present one normalization method using different orders of vertical derivatives to improve the new filters. The presented filters were tested on synthetic and real potential field data to verify its feasibility. All of the results have shown that the new edge detection methods can not only display the sources edges precisely and clearly,but also bring out more geological subtle details.

Signal Detection of Large Volume Airgun Source Excitation in the Fixed Field of the Yangtze River

As this is the first time a large volume airgun has been excited in the "Yangtse River Geoscience Project",it is necessary to study the time-frequency characteristic based on the linear stacked seismic data from records from portable stations near the fixed fields and seismic stations. Airgun signal propagation distances were detected using stacked seismic data to analyze the environmental impact on signal propagation distance. The results showed that:( 1) the airgun signal produced by bubble pulses,pressure pulses and the surface wave can be received by a portable station near the fixed field;( 2) the dominant frequency of a bubble at 5Hz or so can be received by both near-field stations and far-field stations,pressure pulses rapidly weaken and the dominant frequency bands get narrower as epicentral distance increases;( 3) the longest spread distance of signal is 260 km,the nearest is 180 km,and the signal can travel further in the evening.

Complex field network-coded cooperation based on multi-user detection in wireless networks

Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation（CFNCC） scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation（CC） scheme and network-coded cooperation（NCC） scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability（SEP） performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio（SNR） values.

CLOUD IMAGE DETECTION BASED ON MARKOV RANDOM FIELD

In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.

Combination of structure tensor and tilt angle in the edge detection of potential field data

Edge detection is a commonly requested task in the interpretation of potential field data. Different methods have different results for varied depths and shapes of geological bodies. In this paper,we propose using the combination of structure tensor and tilt angle to detect the edges of the sources,which can display the edges of shallow and deep bodies simultaneously. Through tests on synthetic potential field data,it is obvious that the proposed edge detection methods can display the sources edges more clearly and precisely,compared with other commonly used methods. The application on real potential field data shows similar result,obtaining the edges of layers and faults clearly. In addition,another advantage of the new method is its insensitivity to noise.

Advanced derivative ratio filters for edge detection of potential field data

Edge location is an important information of the source,and can be obtained by the potential field data. Most edge detection methods of potential field data are the functions of horizontal and vertical derivatives.The authors provide a new strategy to establish edge detection filters that can improve the resolution to identify small bodies,which use the ratio functions of different-order derivatives to recognize the edges of the sources.The new filter is named as advanced derivative ratio( ADR) filter and balanced outputs can be produced for different forms of ADR filters. The ADR filters are tested on synthetic data and real potential field data. The advantage of the ADR filters is that they can detect the edges of the causative sources more precisely and clearly,and the model testing results show that the resolution of ADR filters is higher than other existing filters. The ADR filters were applied to real data,with more subtle details obtained.

Magnetic Barkhausen Noise Profile Analysis: Effect of Excitation Field Strength and Detection Coil Sensitivity in Case Carburized Steel

The sensitivity of magnetic Barkhausen noise (MBN) profile to changes in the excitation field strength and the number of turns of the detection coil was investigated in inhomogeneous material. Generally, the 0.5 mm case depth EN 36 steel specimen shows a double peak profile indicative of inhomogeneity through the detected depth the magnetized landscape. Various excitation field amplitudes were applied to the specimen and the induced MBN emission was analyzed for each magnetizing current. Excitation field at the lowest level induced an MBN emission of two peaks of equivalent heights. The first peak occurs at lower field than the second peak in the magnetization period. As the excitation field increased, the height of both peaks increased but the second peak increased in a higher rate than that of the first peak. Beyond certain excitation level, both peaks began to saturate and no increase in the MBN intensity was observed with increased excitation field strength. However, peak position and the number of Barkhausen events, indicated linearly as a function of the applied field strength. The experiment also establishes that the number of turns in the detection coil is important parameter which controls the height of the first peak. Low field peak height increases as the number of turn of the detection coil increases. The results indicate that the potential difference applied to the electromagnet and the sensitivity of the detection coil, determine the MBN profile characteristics.

基于小波变换和压缩感知的工频磁异常信号降噪方法

针对强噪声背景下水下目标微弱工频磁异常信号提取困难的问题,提出一种基于小波变换的压缩感知降噪方法。该方法利用噪声信号不具有稀疏性的特点,将含噪信号在小波域内分解,并在此基础上进行压缩感知的重构与降噪处理,实现微弱工频磁异常信号与噪声最大限度地分离。仿真及试验表明,该方法得到很好的降噪效果,避免了小波阈值滤波中阈值选取对降噪效果的影响,能够最大程度地保留原始信号的固有特征。

Detection of Environmental Toxins in Mixed Matrices of Tap Water, Soil, Food Waste, Serum and Milk Using Hememics Biosensor

Exposure to toxins can lead to a wide range of adverse health effects, including respiratory problems, neurological disorders, cancer, and reproductive issues. Toxins can come from various sources, such as industrial waste, agricultural runoff, and household chemicals. Therefore, detecting and monitoring toxins in the environment is crucial for protecting human health and the environment. This study aimed to evaluate the performance of Hememics biosensor system in detecting environmental toxins such as Ricin and Staphylococcal enterotoxin B (SEB) in mixed matrixes. When Ricin and SEB are spiked into soil, chopped lettuce, tap water, milk and serum, the biosensor was able to detect these toxins, without sample processing, at a level of detection comparable to lab testing with high sensitivity and specificity. Furthermore, Hememics biosensor system is designed to be network-enabled, which means that results can be transmitted to relevant agencies for quick decisions. This feature is crucial in cases where quick action is needed to prevent further contamination or exposure to harmful toxins.

基于多尺度特征融合的绝缘子缺陷程度检测

针对绝缘子不同程度缺陷特征相似、像素信息少、不同程度缺陷检测效果不佳的问题,提出了一种基于多尺度特征融合的绝缘子缺陷程度检测网络(multi-scale feature fusion defect degree detection network,MFFD3Net)。该网络采用重构的ResNeSt50架构提高了对绝缘子缺陷程度数据集的特征提取能力。设计了基于反卷积的多尺度特征融合模块,丰富了不同尺寸特征图的表达能力,提高了对不同尺度目标的检测性能。同时,在输入检测模块的浅层特征图后增加多感受野的特征提取模块(receptive field block,RFB),使得更多绝缘子缺陷信息进入有效感受野,对最终特征图产生影响,提升不同程度绝缘子缺陷的检测精度。MFFD3Net在绝缘子缺陷程度数据集上的全类平均精度达到85.02%,其中绝缘子轻微破损与绝缘子轻微闪络小目标的检测精度分别为78.37%、79.98%,能够完成不同程度绝缘子缺陷的识别与定位。因此,该文提出的MFFD3Net对于完善电力系统故障预警、保障电网安全稳定运行具有重要意义。

基于回波幅值信号的医用超声探头性能快速检测系统的研制

目的研制一套基于回波幅值信号的医用超声探头阵元快速检测系统,用以对超声探头性能进行评估与质量控制。方法运用现场可编程门阵列作为系统主控制芯片,以控制系统内部集成的电脉冲激励信号源,进而激励医用超声探头的阵元。阵元振动后产生超声波并在遇到空气后反射。基于超声脉冲回波测试方法,以回波幅值为主要检测参数,实现对超声探头阵元工作状态的检测与评估。结果选取同型号不同使用年限及故障状态的探头进行系统测试,并与所成超声图像进行对比:全新完好探头的回波幅值信号范围为550~575 mV,整体误差小于0.5 dB;使用1年的探头回波幅值信号范围为550~590 mV,较全新探头整体误差低0.6 dB左右;使用3年的探头回波幅值信号中存在十多个3 dB、6 dB及个别10 dB等不同程度的衰减;故障探头中存在三十多个成片连续衰减10 dB阵元。探头衰减位置及程度与所成超声图像缺损位置及程度相对应。结论本系统具有灵敏度高、稳定性好、操作简单快速等特点,在超声探头阵元性能评估方面具有很强的实际应用和推广价值。

基于DeepSportLab的篮球检测及球员姿态估计研究

为了提升团队体育运动中的智能分析效果,提出通过结合部件强度场和空间嵌入原理来训练多任务,同时实现运动场景中的篮球检测、球员姿态预测和球员实例掩码分割的统一框架(DeepSportLab),以解决团队运动场景的复杂性和特殊性,例如强遮挡和运动模糊。首先,部件强度场提供了篮球和球员的位置信息,以及球员关节的位置。然后,采用空间嵌入技术将球员实例像素与球员各自中心点相关联,并将球员的关节点组合成骨架信息。在DeepSport篮球数据集上进行了验证,并取得了与具有独立任务的单个模型相当的良好性能。

Light Field Flow Estimation Based on Occlusion Detection

Light field cameras have a wide area of applications, such as digital refocusing, scene depth information extraction and 3-D image reconstruction. By recording the energy and direction information of light field, they can well solve many technical problems that cannot be done by conventional cameras. An important feature of light field cameras is that a microlens array is inserted between the sensor and main lens, through which a series of sub-aperture images of different perspectives are formed. Based on this feature and the full-focus image acquisition technique, we propose a light-field optical flow calculation algorithm, which involves both the depth estimation and the occlusion detection and guarantees the edge-preserving property. This algorithm consists of three steps: 1) Computing the dense optical flow field among a group of sub-aperture images;2) Obtaining a robust depth-estimation by initializing the light-filed optical flow using the linear regression approach and detecting occluded areas using the consistency;3) Computing an improved light-field depth map by using the edge-preserving algorithm to realize interpolation optimization. The reliability and high accuracy of the proposed approach is validated by experimental results.

Transfer-Learning for Automated Seizure Detection Based on Electric Field Encephalography Reconstructed Signal

Building an automatic seizure onset prediction model based on multi-channel electroencephalography (EEG) signals has been a hot topic in computer science and neuroscience field for a long time. In this research, we collect EEG data from different epilepsy patients and EEG devices and reconstruct and combine the EEG signals using an innovative electric field encephalography (EFEG) method, which establishes a virtual electric field vector, enabling extraction of electric field components and increasing detection accuracy compared to the conventional method. We extract a number of important features from the reconstructed signals and pass them through an ensemble model based on support vector machine (SVM), Random Forest (RF), and deep neural network (DNN) classifiers. By applying this EFEG channel combination method, we can achieve the highest detection accuracy at 87% which is 6% to 17% higher than the conventional channel averaging combination method. Meanwhile, to reduce the potential overfitting problem caused by DNN models on a small dataset and limited training patient, we ensemble the DNN model with two “weaker” classifiers to ensure the best performance in model transferring for different patients. Based on these methods, we can achieve the highest detection accuracy at 82% on a new patient using a different EEG device. Thus, we believe our method has good potential to be applied on different commercial and clinical devices.

多感受野特征自适应融合及动态损失调整的初烤烟叶等级检测

初烤烟叶等级的快速准确检测对开发烟叶智能分级设备以促进农产品精细化管理有着重要意义。针对相似度较高但等级不同的初烤烟叶难以区分的问题,本文提出多感受野特征自适应融合及动态损失调整的初烤烟叶等级检测网络(Flue-cured Tobacco Leaf Grade Detection Network,FTGDNet)。首先,FTGDNet采用CSPNet作为特征提取主干网络,采用GhostNet作为辅助特征提取网络以增强模型的特征提取能力;其次,在主干网络末端嵌入显式视觉中心瓶颈模块(Explicit Visual Center Bottleneck module,EVCB)以实现全局特征信息与局部细节特征信息融合;然后,构建多感受野特征自适应融合模块(Multi-Receptive Field Feature Adaptive Fusion module,MRFA_d),利用注意力特征融合机制(Attention Feature Fusion,AFF)将不同感受野特征图进行自适应加权融合,在增强模型局部感受野的同时突出有效通道信息;最后,设计了一种新的定位损失函数(More Complete IoU Loss,MCIoU_Loss),结合预测框与真实框面积损失以解决在回归定位过程中二者宽高比相等且中心点重合时CIoU_Loss性能退化导致定位精度下降问题,此外,引入矩形相似度衰减系数在训练过程中对真实框与预测框的相似度判别项进行动态调整,加快模型拟合。实验结果表明,FTGDNet对十个等级的初烤烟叶的验证精度达到90.0%,测试精度达到87.4%,且推理时间仅为12.6 ms。相较于多种先进目标检测算法,FTGDNet具有更高的检测精度和更快的检测速度,可为高精度初烤烟叶等级检测提供关键技术支撑。

海上油气田机泵机械密封泄漏信号检测方法设计

为避免出现大型海上油气田开采事故,保证机泵机械密封装置正常运行,提出针对海上油气田机泵机械密封的泄漏信号检测方法。利用小波包分解细化海上油气田机泵机械密封振动信号高频部分,运用希尔伯特变换重构信号,归一化处理信号矢量提取机械装置异常信号,使用匹配追踪法去除信号噪声。计算油体流动规律,通过音波评估信号波动范围,以温度与压力为前提,将高斯函数作为分类核函数,采用支持向量机构建泄漏信号检测判断函数,完成机械密封泄漏信号检测。实验结果表明:所提方法信号降噪能力较强,在保证机械密封泄漏检测准确性的同时,检测耗时最短,有效提高了海上油气田开采效率,可带来可观的经济收益。

基于双通路视觉系统的自适应轮廓检测模型

在轮廓检测领域,背景纹理的干扰容易造成轮廓提取不完整。针对这一问题,本文提出了一种基于双通路视觉系统的自适应轮廓检测模型。首先从皮层下通路的信息采集与评估过程出发,对图像整体的显著性进行评估,以此获得轮廓信息的可能性分布;然后采用自适应尺度的高斯导函数对经典视觉通路中感受野的动态特性进行模拟,加强了模型对轮廓细节的捕获;最后在外周抑制算法的基础上,结合像素的空间分布对所有边缘的稀疏性进行度量,更加准确地区分了轮廓和纹理边缘。实验结果表明,本文模型可以有效抑制背景纹理,提升轮廓连续性,具有较好的轮廓检测性能。