A New Electronic Instrumentation Approach for the Acquisition of Microscopic Blood Smear Images for the Automatic Diagnosis of Anemia

Anemia is a blood abnormality that affects the quantity and quality of red blood cells in the human body. This sometimes banal sign spares no continent and no social stratum. This anomaly is generally appreciated through biological analyzes of patients’ blood. These analyzes, which boil down to the knowledge of hemato-metric constants, cannot by themselves allow the characterization of certain forms of anemia in the sense that most anemia are related to the morphology and color of red blood cells. Our work in this paper is to perform blood smears on patients and perform a morphological and colorimetric analysis of red blood cells on these smears. This approach allowed us to highlight on each erythrocyte morphological and colorimetric descriptors to accurately identify the types of anemia by image processing methods. This identification is performed in an automated environment to allow pathologists to respond quickly to anemia-related emergencies and also improve the treatment to be conducted. This automation required the implementation of a new approach to electronic instrumentation and the acquisition of microscopic blood smear images for the automatic and rapid diagnosis of anemia.

Threshold decision for instrument control system and its application in nuclear power plant

Threshold decision is an important function of nuclear instrument control system based on physical parameters threshold decision. Because the conventional decision methods lack correlation with time and conditions, by analyzing the existing methods, some optimized methods are adopted. Considering safety, those methods are improved in data processing algorithms, floating threshold with multiple values, association with specific working condition, etc. These measures im- prove the nuclear instrument control system in fault tolerance and fault diagnosis, especially, the shutdown number of nucle- ar power plant decreases.

Measuring the Condition of Parking Lot by Image Processing

Aim To study the parking management in the condition of vehicles' increasing. Methods The methods of pattern recognition and image processing were used to analyze the eigenvalues of parking lot images. Results The automatic identification of every parking place in the parking plot was realized. The automatic measuring of parked vehicle count and parking lot utilization was completed. Conclusion It can complete the real time recognition, and has some practicabilities.

An acquisition system of digital nuclear signal processing for the algorithm development

A principle and method of constructing the digital acquisition system is presented in this work,which is convenient for the study on the theories and algorithms of digital nuclear signal processing.The hardware system of the digital acquisition system consists of front-end controller,waveform digitizer and PC workstation,on which the software system has been developed based on Visual C++under Windows environment.The alterable-frequency sampling(AFS)algorithm and the alterable-frequency trapezoidal filter(AFTF)algorithm have also been studied in the real-time environment,along with a digital nuclear spectrum acquisition system being set up based on the new algorithms and theγ-ray spectra of 241Am being shown.A useful experimental platform could be provided by this work for the successive work such as the development of global digitized nuclear measurement system and the study of digital nuclear signal processing.

Image format pipeline and instrument diagram recognition method based on deep learning

In this study,we proposed a recognition method based on deep artificial neural networks to identify various elements in pipelines and instrumentation diagrams(P&ID)in image formats,such as symbols,texts,and pipelines.Presently,the P&ID image format is recognized manually,and there is a problem with a high recognition error rate;therefore,automation of the above process is an important issue in the processing plant industry.The China National Offshore Petrochemical Engineering Co.provided the image set used in this study,which contains 51 P&ID drawings in the PDF.We converted the PDF P&ID drawings to PNG P&IDs with an image size of 8410×5940.In addition,we used labeling software to annotate the images,divided the dataset into training and test sets in a 3:1 ratio,and deployed a deep neural network for recognition.The method proposed in this study is divided into three steps.The first step segments the images and recognizes symbols using YOLOv5+SE.The second step determines text regions using character region awareness for text detection,and performs character recognition within the text region using the optical character recognition technique.The third step is pipeline recognition using YOLOv5+SE.The symbol recognition accuracy was 94.52%,and the recall rate was 93.27%.The recognition accuracy in the text positioning stage was 97.26%and the recall rate was 90.27%.The recognition accuracy in the character recognition stage was 90.03%and the recall rate was 91.87%.The pipeline identification accuracy was 92.9%,and the recall rate was 90.36%.

High-resolution Solar Image Reconstruction Based on Non-rigid Alignment

Suppressing the interference of atmospheric turbulence and obtaining observation data with a high spatial resolution are an issue to be solved urgently for ground observations. One way to solve this problem is to perform a statistical reconstruction of short-exposure speckle images. Combining the rapidity of Shift-Add and the accuracy of speckle masking, this paper proposes a novel reconstruction algorithm-NASIR(Non-rigid Alignment based Solar Image Reconstruction). NASIR reconstructs the phase of the object image at each frequency by building a computational model between geometric distortion and intensity distribution and reconstructs the modulus of the object image on the aligned speckle images by speckle interferometry. We analyzed the performance of NASIR by using the correlation coefficient, power spectrum, and coefficient of variation of intensity profile in processing data obtained by the NVST(1 m New Vacuum Solar Telescope). The reconstruction experiments and analysis results show that the quality of images reconstructed by NASIR is close to speckle masking when the seeing is good, while NASIR has excellent robustness when the seeing condition becomes worse. Furthermore, NASIR reconstructs the entire field of view in parallel in one go, without phase recursion and block-by-block reconstruction, so its computation time is less than half that of speckle masking. Therefore, we consider NASIR is a robust and highquality fast reconstruction method that can serve as an effective tool for data filtering and quick look.

Terrain reconstruction from Chang'e-3 PCAM images

The existing terrain models that describe the local lunar surface have limited resolution and accuracy, which can hardly meet the needs of rover navigation,positioning and geological analysis. China launched the lunar probe Chang＇e-3 in December, 2013. Chang＇e-3 encompassed a lander and a lunar rover called ＂Yutu＂（Jade Rabbit）. A set of panoramic cameras were installed on the rover mast. After acquiring panoramic images of four sites that were explored, the terrain models of the local lunar surface with resolution of 0.02 m were reconstructed. Compared with other data sources, the models derived from Chang＇e-3 data were clear and accurate enough that they could be used to plan the route of Yutu.

An accelerated direct demodulation method for image reconstruction using spherical data from the hard X-ray modulation telescope

The hard X-ray modulation telescope （HXMT） mission is mainly devoted to performing an all-sky survey at 1- 250 keV with both high sensitivity and high spatial resolution. The observed data reduction as well as the image reconstruction for HXMT can be achieved by using the direct demodulation method （DDM）. However the original DDM is too computationally expensive for multi-dimensional data with high resolution to be employed for HXMT data. We propose an accelerated direct demodulation method especially adapted for data from HXMT. Simulations are also presented to demonstrate this method.

A METHOD ANALYSING PHOTOCCLUSAL IMAGE WITH COMPUTER

Depending on the techniques of pattern recognition and image processing, we established a computer analytic system for photocclusal image. The analysing results made by this system are more accurate and reliable than those by the naked eye and grid for analysing photocclusal image. We analysed photocclusal images for a patient with prematurity of lower first right molar be fore and after occlusal adjustment with the system. The result appeared that occlusal adjustment mainly brought about distributive variation of occlusal stress rather than alteration of absolute value of overall occlusal force.

A coarse-to-fine strategy for the registration of the multi-wavelength high-resolution solar images

The registration of multi-wavelength high-resolution solar images is an important task in the research of solar physics. This paper proposed a coarse-to-fine strategy to realize the accurate registration of high-resolution photospheric images and chromospheric images observed by the New Vacuum Solar Telescope(NVST) whose field-of-view is about 2′~ 3′, and the spatial resolution can reach 0.1′′after image reconstruction. In this strategy, the full-disk solar images with relatively lower resolution taken by other space-or ground-based telescopes are taken as transition images, and the Fourier-Merlin transform,Template matching and a local statistical information based algorithm are used in combination. After registration, the geometric transformation between multi-wavelength images of NVST are corrected at the level of sub-arcseconds, including the rotation, scaling and translation relations. Two sets of data observed in active regions(i.e., the NOAA 11982 and the NOAA 12673) are used to illustrate our method step by step.The result shows that the registration accuracy can reach less than 1′′. Moreover, this work also has facilitated the combination of high-resolution observations of NVST with the continuum, ultraviolet passbands and magnetic field observations of the Solar Dynamic Observation(SDO), which is highly beneficial to the multi-instrument joint measurement of solar activities.

Design of recognition algorithm for multiclass digital display instrument based on convolution neural network

Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments.We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory(LSTM).First,the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure.Next,the digital display instrument identification network was constructed based on a convolutional neural network using twoway LSTM and Connectionist temporal classification(CTC)of indefinite length.Finally,an automatic calibration system for digital display instruments was built,and a multiclass digital display instrument dataset was constructed by sampling in the system.We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.

基于轻量级YOLO-v4模型的变电站数字仪表检测识别

为了在变电站实际场景中准确获取数字仪表读数,智能管控变电站的安全风险,同时推动变电站智能化发展,以实际场景中变电站数字仪表作为研究对象,综合考虑实时性及准确度等,提出一种基于轻量级YOLOv4模型的变电站数字仪表检测识别方法.首先,通过从鄂尔多斯变电站实际拍摄变电站数字仪表图像数据,使用Albumentations框架对数字仪表图像进行数据扩充,构建变电站数字仪表目标检测数据集;然后,以YOLO-v4网络为基础,结合注意力机制构建一个有效通道注意(efficient channel attention,ECA)改进的深度可分离卷积模块(ECA-bneck-m);最后,提出一个轻量级YOLO-v4模型,进行模型大小与性能的对比实验.实验结果表明:本文方法可以在几乎不损失检测准确度的情况下,将整个模型存储大小压缩为原先的1/5,同时将模型推理速度从24.0帧/s提升至36.9帧/s,其实时性能够满足实际变电站检测识别的工程需要.

面向新工科建设的仪器类专业数字图像处理课程思政教学案例设计与探索

该文面向新工科建设的“复旦共识”“北京指南”“天大行动”,围绕“新工科建设”和“课程思政育人”两大目标,推进仪器类专业课程思政建设,把思想政治教育贯穿到人才培养体系的全过程,引导学生自尊、自信、自立和自强,强化学生“仪器报国、仪器强国”的使命担当,厚植家国情怀,培养担当民族复兴大任的时代新人。同时探讨结合学校的办学定位、专业特色、人才培养要求,探索课程思政教学与知识教学的有机统一。针对面向仪器类本科生开设的数字图像处理课程,从教学大纲、教案、教材等方面整理补充和仪器仪表行业相关的思政教育元素,设计题材新颖、具有时代特色的仪器类思政教学案例,实现具有“专业知识-思政育人-空天报国-敢为人先”特色的课程思政育人目标,进一步落实课程思政“立德树人成效”根本标准的具体化和可操作化。

指针式仪表读数识别的研究现状与发展

在高温高压强磁场等恶劣环境下,指针式仪表有着优于数显式仪表的良好性能表现,对指针式仪表读数识别进行研究具有重要的现实意义。近年来,深度学习与计算机视觉紧密结合,基于深度学习的指针式仪表读数识别技术是前沿研究的重点方向。文中首先概述指针式仪表读数识别的过程,然后从3个方面论述指针式仪表读数识别技术的研究现状与进展:仪表图像的预处理、仪表表盘区域的检测和仪表的读数识别,论述过程中分别阐述基于传统机器学习的方法和基于深度学习的方法。最后介绍公开的仪表数据集和应用场景,并从深度学习算法、巡检机器人工作特点和公共数据集的建立3个方面对未来的研究进行展望与建议。

基于轻量化神经网络和SimAM注意力机制的电力仪表检测与识别

通过图像识别算法对电力仪表进行识别与读数可有效避免人工巡检过程中的诸多不确定问题。针对传统仪表检测识别方法工作量大以及指针分割不清晰导致读数不准确的问题,提出了一种基于轻量化神经网络模型和SimAM注意力机制的电力仪表检测与识别方法。首先,通过引入SimAM注意力机制以及YOLOv5s轻量化神经网络模型实现对电力仪表的识别与分类以及表盘区域的分割;然后分别采用CRNN文本识别算法和Hough圆检测方法对数字式仪表以及指针式仪表进行读数。模型结合多种场景进行训练,并对识别与读数2个阶段分别给出评价标准。实验结果表明,该方法能够高效完成电力仪表的检测与识别工作,在提高检测准确率的同时将总体检测时间控制在1.00 s以内,具有一定的实用性。

无监督学习步态识别综述

在光学技术高速发展的现代,步态特征因非接触、非侵入、难伪造、远距离采集等优势受到了学界的广泛关注。目前步态识别算法主要为依赖标签数据的有监督学习方法,庞大的标签标注量在实际应用中面临多重挑战。无监督学习不需要标注就能完成对数据内在特征的自动分析,更贴合实际应用的需求。为了全面认识无监督学习步态识别发展现状及趋势,对领域相关工作进行了梳理。介绍了步态识别常用数据集、通用制作方式以及主流评价指标。从基于GAN的步态识别方法、基于聚类的步态识别方法、基于无监督域适应的步态识别方法和其他方法四个方向详细介绍了目前基于无监督学习的步态识别相关研究思路;选取了CASIA-B、OU-MVLP和OU-ISIR LP三个典型数据集,对主要无监督算法性能进行综合对比;对各方向研究侧重点进行总结讨论,针对存在的交叉研究情况进行评论综述,为未来研究提供借鉴思路。研究分析了无监督步态识别算法目前面临的挑战,并以此展望步态领域未来的发展方向。

YOLOX目标检测模型用于自动识别数字减影血管造影图中的血管腔内介入器械

目的 观察YOLOX目标检测模型用于自动识别数字减影血管造影(DSA)图中的血管腔内介入器械的价值。方法 收集37例接受腹部血管腔内介入治疗患者的DSA资料,截取4 435幅图像作为数据集,并按照9∶1比例将其分为训练集(n=3 991)与验证集(n=444)。对数据集中的6种介入器械进行标记后,以YOLOX算法对训练集数据进行深度学习训练,构建YOLOX目标检测模型;基于验证集评估该模型自动识别DSA图中的介入器械的效能。结果共对4 435幅DSA图像设置6 668个标签,分别针对Terumo 0.035in泥鳅导丝(n=587)、Cook Lunderquist超硬导丝(n=990)、Optimed 5F带刻度猪尾导管(n=1 680)、Cordis MPA多功能导管(n=667)、Boston Scientific V-18可控导丝(n=1 330)及Terumo 6F长鞘(n=1 414);训练集分别含上述标签527、875、1 466、598、1 185及1 282个,验证集分别含60、115、214、69、145及132个。YOLOX目标检测模型自动识别验证集中上述器械的像素准确率分别为95.23%、97.32%、99.18%、98.97%、97.60%及98.19%,平均像素准确率达97.75%。结论 YOLOX目标检测模型能够自动识别DSA图中的多种血管腔内介入器械。

基于机器视觉的齿轮参数测量评定系统设计

针对传统的齿轮参数检测中测量不便、效率低和人机交互不强等问题,以LabVIEW为开发平台,利用虚拟仪器技术、图像处理技术和机器视觉技术,设计一种基于机器视觉的齿轮参数检测评定系统。系统具有齿轮图像采集、图像处理、系统标定、轴孔中心检测以及齿轮参数检测评定等功能,可测量评定齿轮齿顶圆、齿根圆、单个齿距偏差f_(pt)及齿距总偏差F_(p)、公法线长度偏差等。采用0级标准量块对系统进行标定,并对设计的系统进行了测试和对比,通过将检测结果与齿轮的设计理论值进行对比评定系统的准确性。结果表明:该检测评定系统界面友好、易扩展、效率高、操作方便,并可对采集数据进行自动保存、回放和齿轮参数可视化评定。

The Impact of Bias Row Noise to Photometric Accuracy:Case Study Based on a Scientific CMOS Detector

We tested a new model of CMOS detector manufactured by the Gpixel Inc,for potential space astronomical application.In laboratory,we obtain some bias images under the typical application environment.In these bias images,clear random row noise pattern is observed.The row noise also contains some characteristic spatial frequencies.We quantitatively estimated the impact of this feature to photometric measurements,by making simulated images.We compared different bias noise types under strict parameter control.The result shows the row noise will significantly deteriorate the photometric accuracy.It effectively increases the readout noise by a factor of2-10.However,if it is properly removed,the image quality and photometric accuracy will be significantly improved.

Multi-field Split Curvature Wavefront Sensing and its Application in the Large Field Survey Telescope

The image quality of a large field survey telescope with a fast focal ratio of the primary mirror is high sensitivity to the optical elements’misalignments and the primary mirror’s deformations.To maintain good optical performance,the perturbations need to be aligned,and the tomographic reconstruction of these perturbations can be derived from wavefront sensing with multi-field points for alignment.This work undertakes a comprehensive examination related to the implementation and optimization of a multi-field split curvature wavefront sensing system,including defocus distance,signal pre-processing,deblending of overlapped doughnuts,field-dependent correction,and distorted coordinate correction.We also conduct experiments to demonstrate the application and performance of a multi-field split curvature wavefront sensing system in Mephisto.In the context of the decentering of the secondary mirror,the coefficient of determination(R)attains a high value of 0.993,indicating a robust linearity between the coma coefficients and the decentering of the secondary mirror.A comparative analysis of the simulated and experimental results shows that the difference between them is less than 0.1λ.