IEEE Open Journal of Instrumentation and Measurement最新文献_第4页

Smartphone System for Heart Rate and Breathing Rate Estimation 估算心率和呼吸频率的智能手机系统

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-10-11 DOI: 10.1109/OJIM.2024.3477572

Amit Nayak;Miodrag Bolic

引用次数: 0

Research on a Surface Roughness Measurement Under ResNet-Based Roughness Classification and Light-Section With Seam-Driven Image Stitching (RCLS) 基于 ResNet 的粗糙度分类和缝合驱动图像拼接（RCLS）的光剖面下的表面粗糙度测量研究

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-10-10 DOI: 10.1109/OJIM.2024.3477568

Huashen Guan;Qiushen Cai;Xiaobin Li;Guofu Sun

{"title":"Research on a Surface Roughness Measurement Under ResNet-Based Roughness Classification and Light-Section With Seam-Driven Image Stitching (RCLS)","authors":"Huashen Guan;Qiushen Cai;Xiaobin Li;Guofu Sun","doi":"10.1109/OJIM.2024.3477568","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3477568","url":null,"abstract":"With the development of optics, light section method has become a feasible measurement for surface roughness, while the short sampling length is negative to the accuracy. To overcome this defect, this article proposed a measurement under ResNet-based roughness classification and light section with seam-driven image stitching (RCLS). First, the images were classified with ResNet neural network, then stitched and enhanced by scale invariant feature transform (SIFT) and optimized random sample consensus (RANSAC) algorithm for the best visual effect. After this, images were processed by Nobuyuki Otsu method and Freeman chain code tracking algorithm. Least square was also adopted to calculate the optical band edge curve and contour midline. Finally, the roughness contour arithmetic mean deviation model was established to evaluate the surface roughness. The experiments were conducted with vertical milled, planned, and turned samples that self-machined. The light section method had a reduction of 2.75% on the mean relative error compared to stylus and RCLS could further reduce the mean relative error by 1.42%, especially in planned sample. The RCLS could achieve a more accurate surface roughness by overcoming the disadvantages of small sample length and low precision of the light section method, and is more convenient than stylus.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Positioning and Navigation Using IMUs and Low-Cost Sensors 使用 IMU 和低成本传感器进行定位和导航

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-10-10 DOI: 10.1109/OJIM.2024.3477574

Patrick Grates

引用次数: 0

Comparative Analysis of Internal Porosity in AM Ti64 Using X-Ray Computed Tomography and Mechanical Polishing Serial Sectioning 利用 X 射线计算机断层扫描和机械抛光序列切片对 AM Ti64 的内部孔隙率进行比较分析

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-10-10 DOI: 10.1109/OJIM.2024.3477569

Bryce Jolley;Christine Knott;Daniel Sparkman;Michael Uchic

{"title":"Comparative Analysis of Internal Porosity in AM Ti64 Using X-Ray Computed Tomography and Mechanical Polishing Serial Sectioning","authors":"Bryce Jolley;Christine Knott;Daniel Sparkman;Michael Uchic","doi":"10.1109/OJIM.2024.3477569","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3477569","url":null,"abstract":"X-ray computed tomography (XCT) is a widely adopted nondestructive technique for characterizing internal porosity in additive manufactured (AM) components. However, the accuracy and precision of porosity characterization using XCT can be affected by factors, such as XCT system configuration and post-processing methodologies. This study investigates the influence of these variables on porosity characterization by comparing results obtained from four different XCT systems and two distinct analysis workflows applied to a single metallic AM sample. A benchmark is also established for the XCT performance by using a high-resolution reference dataset generated through mechanical polishing serial sectioning (MPSS). Porosity metrics, including volume fraction, pore count, size distribution, and equivalent spherical diameter (ESD), were computed for large pores (\u0000<inline-formula> <tex-math>$ge 84~mu $ </tex-math></inline-formula>\u0000m) within the XCT and MPSS datasets. By comparing these metrics across XCT systems and workflows, this research aims to demonstrate the variability introduced by different XCT configurations and analysis procedures, providing insights into the potential limitations and uncertainty considerations needed while carrying out XCT-based porosity characterization of AM components.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dataguzzler-Python and SpatialNDE2: Crucial Software Infrastructure for Reconfigurable NDE Data Acquisition With Spatial Context Dataguzzler-Python 和 SpatialNDE2：利用空间上下文进行可重构无损检测数据采集的关键软件基础设施

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-09-13 DOI: 10.1109/OJIM.2024.3459989

Tyler J. Lesthaeghe;Stephen D. Holland

{"title":"Dataguzzler-Python and SpatialNDE2: Crucial Software Infrastructure for Reconfigurable NDE Data Acquisition With Spatial Context","authors":"Tyler J. Lesthaeghe;Stephen D. Holland","doi":"10.1109/OJIM.2024.3459989","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3459989","url":null,"abstract":"In the field of nondestructive evaluation (NDE), we sometimes need an intricate system of multiple actuators and sensors to measure and assess the material condition or structural integrity of a specimen. Complicated systems are especially necessary for more advanced techniques that involve multiple phenomena or modeling in a geometric context. In the research laboratory, we rarely understand the intricacies of the measurement up front, and we need the agility to reconfigure our measurement system as needs evolve. Software is the glue that ties our measurement systems together. The traditional approach of ad hoc software quickly becomes unsustainable in the modern environment. We propose an alternative approach that addresses the need for agility in the modern NDE laboratory: a reconfigurable, modular software architecture that is built from the ground up to accommodate conflicting requirements in the areas of data management, automation, parallelism, geometry and robotics, and version control. We describe a new pair of open-source tools, Dataguzzler-Python and SpatialNDE2, that facilitate instrumentation control, data acquisition, and processing for the NDE laboratory. The tools make up a framework that provides the following: multiplexed automatic and manual control of instrumentation, a versioned database to store the acquired data, parallel acquisition and live high performance/GPU computation, the ability to acquire and store data in geometric context, and the ability to visualize and interact with the acquired data. This article discusses their design, implementation, and initial experiences in using them in the NDE laboratory.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combining LiDAR and Time-Domain Frequency Analysis for Enhanced Spatial Understanding of Vibration Responses 结合激光雷达和时域频率分析，加强对振动响应的空间理解

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-08-26 DOI: 10.1109/OJIM.2024.3449936

Oliver L. Geißendörfer;Christoph Holst

{"title":"Combining LiDAR and Time-Domain Frequency Analysis for Enhanced Spatial Understanding of Vibration Responses","authors":"Oliver L. Geißendörfer;Christoph Holst","doi":"10.1109/OJIM.2024.3449936","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3449936","url":null,"abstract":"Analyzing objects concerning their periodic behavior is mostly performed with inertial measurement units (IMUs) or global navigation satellite system (GNSS) sensors fixed to its surface. For connecting observations, sensors have to be assigned to the same reference frame in space and time as a prerequisite. Using light detection and ranging (LiDAR) observations enables contactless, time-synchronized, and spatially connected data points within a single sensor. Therefore, common signal properties are further analyzed in the spectrum to find connections and similarities between observations. Since observations are spatially continuous we can discretize them and traditionally process them. However, the time domain offers a diversity of ways to simultaneously estimate frequencies and continuously model properties at different spatial locations. Within this work, we exploit the potential of processing LiDAR data in the time domain to make use of the sensor’s contactless observations and its sampling rate in space and time. Consecutive points and their spatial neighborhoods are used to implement temporal as well as spatiotemporal connections to directly model oscillations in 2-D space. Moreover, we compute an uncertainty of estimated variables to qualify our solution. Consequently, our approach offers the opportunity to describe as well as evaluate movements and vibrations of spatially connected areas.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multigranularity Feature Automatic Marking-Based Deep Learning for Anomaly Detection of Industrial Control Systems 基于多粒度特征自动标记的深度学习用于工业控制系统异常检测

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-06-24 DOI: 10.1109/OJIM.2024.3418466

Xinyi Du;Chi Xu;Lin Li;Xinchun Li

{"title":"Multigranularity Feature Automatic Marking-Based Deep Learning for Anomaly Detection of Industrial Control Systems","authors":"Xinyi Du;Chi Xu;Lin Li;Xinchun Li","doi":"10.1109/OJIM.2024.3418466","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3418466","url":null,"abstract":"Industrial control systems are facing ever-increasing security challenges due to the large-scale access of heterogeneous devices in the open Internet environment. Existing anomaly detection methods are mainly based on the priori knowledge of industrial control protocols (ICPs) whose protocol specifications, communication mechanism, and data format are already known. However, when these knowledge are blank, namely, unknown ICPs, existing methods become powerless to detect the anomaly data. To tackle this challenge, we propose a multigranularity feature automatic marking-based deep learning method to classify unknown ICPs for anomaly detection. First, to obtain the feature sequences without priori knowledge assisting, we propose a multigranularity feature extraction algorithm to extract both byte and half-byte information by fully utilizing the intensive key information in the header field of the application layer. Then, to label the feature sequences for deep learning, we propose a feature automatic marking algorithm that utilizes the inconsistency feature sequences to dynamically update the feature sequence set. With the labeled feature sequences, we employ deep learning with 1-D convolutional neural network and gated recurrent unit to classify the unknown ICPs and realize anomaly detection. Extensive experiments on two public datasets show that both the accuracy and precision of the proposed method reach above 98.4%, which is better than the three benchmark methods.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570378","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Microwave NDT/NDE Through Differential Bayesian Compressive Sensing 通过差分贝叶斯压缩传感实现微波无损检测/无损探伤

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-06-11 DOI: 10.1109/OJIM.2024.3412205

Marco Salucci;Lorenzo Poli;Giorgio Gottardi;Giacomo Oliveri;Luca Tosi;Andrea Massa

引用次数: 0

LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation 基于激光雷达的三维地图优化正态分布变换定位，用于自主导航

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-06-11 DOI: 10.1109/OJIM.2024.3412219

Abhishek Thakur;P. Rajalakshmi

{"title":"LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation","authors":"Abhishek Thakur;P. Rajalakshmi","doi":"10.1109/OJIM.2024.3412219","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3412219","url":null,"abstract":"Autonomous navigation has become a topic of immense interest in robotics in recent years. Light detection and ranging (LiDAR) can perceive the environment in 3-D by creating the point cloud data that can be used in constructing a 3-D or high-definition (HD) map. Localization can be performed on the 3-D map created using a LiDAR sensor in real-time by matching the current point cloud data on the prebuilt map, which is useful in the GPS-denied areas. GPS data is inaccurate in indoor or obstructed environments, and achieving centimeter-level accuracy requires a costly real-time kinematic (RTK) connection in GPS. However, LiDAR produces bulky data with hundreds of thousands of points in a frame, making it computationally expensive to process. The localization algorithm must be very fast to ensure the smooth driving of autonomous vehicles. To make the localization faster, the point cloud is downsampled and filtered before matching, and subsequently, the Newton optimization is applied using the normal distribution transform to accelerate the convergence of the point cloud data on the map, achieving localization at 6 ms per frame, which is 16 times less than the data acquisition rate of LiDAR at 10 Hz (100ms per frame). The performance of optimized localization is also evaluated on the Kitti odometry benchmark dataset. With the same localization accuracy, the localization process is made five times faster. LiDAR map-based autonomous driving on an electric vehicle is tested in the TiHAN testbed at the IIT Hyderabad campus in real-time. The complete system runs on the robot operating system (ROS). The code will be released at \u0000<uri>https://github.com/abhishekt711/Localization-Nav</uri>\u0000.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OJIM 2023 Reviewer List OJIM 2023 审查员名单

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-06-07 DOI: 10.1109/OJIM.2024.3403319

引用次数: 0