2022 IEEE Sensors Applications Symposium (SAS)最新文献_第3页

Water Stress Detection in Pearl Millet Canopy with Selected Wavebands using UAV Based Hyperspectral Imaging and Machine Learning 基于无人机的高光谱成像与机器学习的珍珠谷子冠层水分胁迫选择波段检测

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881337

Adduru U. G. Sankararao, P. Rajalakshmi, Sivasakthi Kaliamoorthy, Sunitha Choudhary

{"title":"Water Stress Detection in Pearl Millet Canopy with Selected Wavebands using UAV Based Hyperspectral Imaging and Machine Learning","authors":"Adduru U. G. Sankararao, P. Rajalakshmi, Sivasakthi Kaliamoorthy, Sunitha Choudhary","doi":"10.1109/SAS54819.2022.9881337","DOIUrl":"https://doi.org/10.1109/SAS54819.2022.9881337","url":null,"abstract":"The major bottleneck in plant phenotyping is the assessment of thousands of genotypes under field conditions, which can be accelerated through Unmanned Aerial Vehicle (UAV) based sensing. Phenotyping for complex traits such as abiotic stress (drought) adaptation can be explored more precisely through the rich spectral information acquired by Hyperspectral Imaging (HSI) sensors. HSI sensors can identify plant water stress early by observing the changes in canopy reflectance due to drought. This study used a UAV-based HSI sensor in the 400-1000 nm range to identify canopy water stress in the pearl millet crop. Five Machine learning-based Feature Selection (FS) methods were used to identify the top-ranked ten wavebands sensitive to canopy water stress. Wavelengths around 692, 714-716, 763-769, 774-882, 870, and 949 nm were repeatedly selected by two or more FS methods. The Recursive feature elimination method with the Support vector machine (SVM) classifier outperformed the other FS methods in selecting the best bands subset. SVM classifier with linear kernel on the selected bands could classify two water stress levels with 95.38% accuracy and early detect stress with 80.76% accuracy in the pearl millet canopy. This study will benefit the agriculture sector by accelerating crop phenotyping using UAV-based HSI.","PeriodicalId":129732,"journal":{"name":"2022 IEEE Sensors Applications Symposium (SAS)","volume":"20 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132192224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Cough Classification Using Audio Spectrogram Transformer 利用声谱图变压器进行咳嗽分类

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881344

K. Habashy, J. J. Valdés, Madison Cohen-McFarlane, Pengcheng Xi, Bruce Wallace, R. Goubran, F. Knoefel

引用次数: 2

Development of a neural network to identify plastics using Fluorescence Lifetime Imaging Microscopy 利用荧光寿命成像显微镜识别塑料的神经网络的发展

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881372

Georgekutty Jose Maniyattu, Eldho Geegy, N. Leiter, Maximilian Wohlschlager, M. Versen, C. Laforsch

引用次数: 2

Feasibility of Measuring Shot Group Using LoRa Technology and YOLO V5 利用LoRa技术和YOLO V5测量枪弹组的可行性

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881356

Sanghyun Park, Dongheon Lee, Jisoo Choi, Dohyeon Ko, Minji Lee, Zack Murphy, Nowf Binhowidy, Anthony H. Smith

引用次数: 0

Live Migration of a 3D Flash LiDAR System between two Independent Data Processing Systems with Redundant Design 基于冗余设计的三维闪光激光雷达系统在两个独立数据处理系统之间的实时迁移

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881255

Philipp Stelzer, Sebastian Reicher, Georg Macher, C. Steger, Raphael Schermann

{"title":"Live Migration of a 3D Flash LiDAR System between two Independent Data Processing Systems with Redundant Design","authors":"Philipp Stelzer, Sebastian Reicher, Georg Macher, C. Steger, Raphael Schermann","doi":"10.1109/SAS54819.2022.9881255","DOIUrl":"https://doi.org/10.1109/SAS54819.2022.9881255","url":null,"abstract":"Self-driving and self-flying vehicles have the ability to drive respectively fly independently without the intervention of an operator. For this purpose, these vehicles need sensors for environment perception and data processing systems, which are safety-critical, to process the obtained raw data from these sensors. However, if such safety-critical systems fail, this can have fatal consequences and can affect human lives and/or the environment, especially in the case of highly automated vehicles. A total failure of these systems is one of the worst scenarios in an automated vehicle. Therefore, such safety-critical systems are often designed redundantly in order to prevent a total failure of environment perception. In order to ensure that the operation of the vehicle can continue safely, however, the live migration from one system to the other must be carried out with as little downtime as possible. In our publication, we present a concept for a 3D Flash LiDAR live migration between two independent data processing systems with redundant design. This concept provides a solution for highly automated vehicles to remain fail-operational in case one of the redundant data processing systems fails. The results obtained from the implemented concept, without specifically addressing performance, are also provided to demonstrate feasibility.","PeriodicalId":129732,"journal":{"name":"2022 IEEE Sensors Applications Symposium (SAS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122721611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forestry Crane Automation using Learning-based Visual Grasping Point Prediction 基于学习的林业起重机视觉抓取点预测自动化

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881370

Harald Gietler, Christoph Böhm, Stefan Ainetter, Christian Schöffmann, F. Fraundorfer, S. Weiss, H. Zangl

{"title":"Forestry Crane Automation using Learning-based Visual Grasping Point Prediction","authors":"Harald Gietler, Christoph Böhm, Stefan Ainetter, Christian Schöffmann, F. Fraundorfer, S. Weiss, H. Zangl","doi":"10.1109/SAS54819.2022.9881370","DOIUrl":"https://doi.org/10.1109/SAS54819.2022.9881370","url":null,"abstract":"This paper presents an approach to automate the log-grasping of a forestry crane. A common hydraulic actuated log-crane is converted into a robotic device by retrofitting it with various sensors yielding perception of internal and environmental states. The approach uses a learning-based visual grasp detection. Once a suitable grasping candidate is determined, the crane starts its kinematic controlled operation. The system’s design process is based on a real-sim-real transfer to avoid possibly harmful, to humans and itself, crane behavior. Firstly, the grasping position prediction network is trained with real-world images. Secondly, an accurate simulation model of the crane, including photo-realistic synthetic images, is established. Note that in simulation, the prediction network trained on real-world data can be used without re-training. The simulation is used to design and verify the crane’s control- and the path planning scheme. In this stage, potentially dangerous maneuvers or insufficient quality of sensory information become visible. Thirdly, the elaborated closed-loop system configuration is transferred to the real-world forestry crane. The pick and place capabilities are verified in simulation as well as experimentally. A comparison shows that simulation and real-world scenarios perform equally well, validating the proposed real-sim-real design procedure.1","PeriodicalId":129732,"journal":{"name":"2022 IEEE Sensors Applications Symposium (SAS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128605776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluation of the quality of LiDAR data in the varying ambient light 在不同环境光下评估激光雷达数据的质量

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881373

Bhaskar Anand, Harshal Verma, A. Thakur, Parvez Alam, P. Rajalakshmi

引用次数: 5

Improvement of Door Recognition Algorithm using Lidar and RGB-D camera for Mobile Manipulator 基于激光雷达和RGB-D摄像头的移动机械手门识别算法改进

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881249

Taehyeon Kim, Minwoo Kang, Sumin Kang, D. Kim

{"title":"Improvement of Door Recognition Algorithm using Lidar and RGB-D camera for Mobile Manipulator","authors":"Taehyeon Kim, Minwoo Kang, Sumin Kang, D. Kim","doi":"10.1109/SAS54819.2022.9881249","DOIUrl":"https://doi.org/10.1109/SAS54819.2022.9881249","url":null,"abstract":"The characteristics of mobile manipulator, which can perform various tasks in dynamic environments, have the advantage of driving and doing diverse tasks in large indoor environments with complex structures such as high-rise buildings. However, in order to efficiently navigate in such environments, mapping process containing information about diverse objects that robot can interact with is essential. Among these objects, door is of great importance, but door recognition is challenge because there are doors of various structures and sizes even in single indoor environment. This paper proposes an improvement of door recognition algorithm for mobile manipulator robot using RGB-D camera attached to end effector of manipulator and Lidar of mobile platform. Basically, laser scan data from Lidar is processed by line fitting algorithm and vision data from RGB-D camera is processed by YOLOv3. The process by laser scan data enables the first door recognition. And Additional recognition through vision data is possible by controlling the manipulator according to the weights given by the first recognition. The proposed algorithm has been verified in a simulation environment based on real-world, and we confirmed that it has a higher recognition success rate compared to traditional algorithms.","PeriodicalId":129732,"journal":{"name":"2022 IEEE Sensors Applications Symposium (SAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115541372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Hydrogen Induced Dipole Layer in Pd-SiO2 Based Gas Sensors Pd-SiO2基气体传感器中的氢致偶极子层

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/SAS54819.2022.9881358

Idan Shem Tov, B. Mukherjee, J. Hayon, Laura Hargreaves, A. Shluger, Y. Rosenwaks

引用次数: 0

The Material Imaging Analyzer MIA 材料成像分析仪MIA

2022 IEEE Sensors Applications Symposium (SAS) Pub Date : 2022-08-01 DOI: 10.1109/sas54819.2022.9881374

B. Thornberg

引用次数: 1