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EI-Drive: A Platform for Cooperative Perception With Realistic Communication Models

IEEE Internet of Things Journal 2025年

作者： Zhou, Hanchu Xie, Edward Shao, Wei Gao, Dechen Dong, Michelle Zhang, Junshan University of California Davis AI Robotics and Edge Laboratory DavisCA95616 United States Electrical and Computer Engineering Department of Johns Hopkins University BaltimoreMD21218 United States Csiro data61 ClaytonVIC Australia Monta Vista High School CupertinoCA95014 United States

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce EI-Drive (Edge Intelligent Drive), an Edge-AI based autonomous driving simulation platform that integrates advanced cooperative perception with more realistic communication models. Built on the CARLA framework, EI-Drive features new modules for cooperative perception while taking into account transmission latency and errors, providing a more realistic platform for evaluating cooperative perception algorithms. In particular, the platform enables vehicles to fuse data from multiple sources, improving situational awareness and safety in complex environments. With its modular design, EI-Drive allows for detailed exploration of sensing, perception, planning, and control in various cooperative driving scenarios. Experiments using EI-Drive demonstrate significant improvements in vehicle safety and performance, particularly in scenarios with complex traffic flow and network conditions. All code and documents are accessible on our GitHub page: https://***/***/. © 2014 IEEE.

关键词： Digital elevation model

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Arc-to-line frame registration method for ultrasound and photoacoustic image-guided intraoperative robot-assisted laparoscopic prostatectomy

arXiv

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arXiv 2023年

作者： Song, Hyunwoo Yang, Shuojue Wu, Zijian Moradi, Hamid Taylor, Russell H. Kang, Jin U. Salcudean, Septimiu E. Boctor, Emad M. Department of Computer Science Whiting School of Engineering The Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering Whiting School of Engineering The Johns Hopkins University BaltimoreMD21218 United States Laboratory for Computational Sensing and Robotics The Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Purpose: To achieve effective robot-assisted laparoscopic prostatectomy, the integration of transrectal ultrasound (TRUS) imaging system which is the most widely used imaging modality in prostate imaging is essential. However, manual manipulation of the ultrasound transducer during the procedure will significantly interfere with the surgery. Therefore, we propose an image co-registration algorithm based on a photoacoustic marker (PM) method, where the ultrasound/photoacoustic (US/PA) images can be registered to the endoscopic camera images to ultimately enable the TRUS transducer to automatically track the surgical instrument. Methods: An optimization-based algorithm is proposed to co-register the images from the two different imaging modalities. The principle of light propagation and an uncertainty in PM detection were assumed in this algorithm to improve the stability and accuracy of the algorithm. The algorithm is validated using the previously developed US/PA image-guided system with a da Vinci surgical robot. Results: The target-registration-error (TRE) is measured to evaluate the proposed algorithm. In both simulation and experimental demonstration, the proposed algorithm achieved a sub-centimeter accuracy which is acceptable in practical clinics (i.e., 1.15 ± 0.29 mm from the experimental evaluation). The result is also comparable with our previous approach (i.e., 1.05 ± 0.37 mm), and the proposed method can be implemented with a normal white light stereo camera and doesn’t require highly accurate localization of the PM. Conclusion: The proposed arc-to-line based frame registration algorithm that transform between the camera and the TRUS imaging frames can allow simple yet efficient integration of commercial US/PA imaging system into the laparoscopic surgical setting, and contribute to the image-guided surgical intervention applications. © 2023, CC BY.

关键词： Ultrasonic imaging

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A Novel Stiffness Programmable Method for Environment-adaptive Crawling Robot

A Novel Stiffness Programmable Method for Environment-adapti...

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IEEE International Conference on robotics and Biomimetics

作者： Yudong Luo Na Zhao Yantao Shen Department of Computer Science and Technology Morphing Robotics Laboratory Dalian Maritime University Dalian Liaoning China Department of Electrical and Biomedical Engineering University of Nevada Reno Nevada

ISBN: (纸本)9781665481106

Benefitting from the adjustable body stiffness, crawling animals such as earthworms can crawl in soil with different hardnesses. To address one of the main challenges of variable stiffness in crawling robots, this paper presents a new concept of tuning stiffness characteristics based on the mechanical stiffening method, constructs the stiffness model of the robot, and performs a proof-of-concept based on a custom-built crawling robot prototype. The robot is composed of a group of specially designed scissor elements, which allows it to achieve bidirectional continuous deformation, thereby enabling continuous stiffness regulation. The stiffness can be adjusted simply by programming the input to the actuators or modi-fying the number of scissor elements, making it easy control, fast response, and high energy efficiency. Then, the stiffness mathematical model of the novel crawling robot segment is established, and the physical parameters are excavated on the basis of numerous numerical simulations. Finally, the 3D-printed prototype of the robot is constructed for experiments, and the results demonstrate the feasibility of tuning the stiffness characteristics based on the proposed scissor element enabled mechanism, which provides a new perspective on the way to change the stiffness of the crawling robot.

关键词： Young's modulus Actuators Prototypes Programming Soil Mathematical models Regulation

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Modelling the Dependence of Chlorophyll Leaf-Clip Measures on Vegetation Indices Derived from Multispectral UAS Images in Vineyards Parcels

Modelling the Dependence of Chlorophyll Leaf-Clip Measures o...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gil Gonçalves Umberto Andriolo Lúcio Paiva Cristiano Premebida António Ferraz Department of Mathematics University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering INESC Coimbra Coimbra Portugal Research Centre for Natural Resources Environment and Society Polytechnic Institute of Coimbra Coimbra Agrarian Technical School Coimbra Portugal Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal NASA Jet Propulsion Laboratory Pasadena United States

Multispectral images and leaf-clip measurements were used for evaluating the Chlorophyll (Chl) content on grapevine leaves through the use of vegetation indices. The multispectral leaf images were taken by a sensor onboard an UAS, which was placed over a table at a height of 70 cm. Images were radiometrically and geometrically processed to obtain accurate coregistered five band image stacks. Using a Kmeans segmentation, each leaf imaged in a multispectral image was automatically detected and the mean leaf reflectance values were used to compute three vegetation indices: NDVI, NDRE, and GLI. When compared to leaf-clip measurements, the results indicated that the NDRE index was the best predictor of Chl leaf content (R2=0.81). The obtained NDRE regression model can be used in UAS-based multispectral orthomosaics to generate canopy Chl maps at vine-row scale, which can assist vine growers in monitoring the spatial and temporal variability of grapevine vigor.

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Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery

arXiv

引用

arXiv 2024年

作者： Kåreborn, Liv Ingerstad, Erica Berg, Amanda Karlsson, Justus Haglund, Leif Maxar International Sweden AB Ebbegatan 13 Linköping582 13 Sweden AI Sweden Folkungagatan 44 Stockholm118 26 Sweden Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping581 83 Sweden

In this work, the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images are investigated. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel approach in computer vision, performs in predicting seasonal variations across different months. Through comprehensive analysis and visualization, the study examines the model's ability to capture and predict seasonal changes, highlighting specific challenges and strengths. Results showcase the impact of the sun direction on predictions, revealing nuanced details in seasonal transitions, such as snow cover, color accuracy, and texture representation in different landscapes. Given these results, we propose Planet-NeRF, an extension to Sat-NeRF capable of incorporating seasonal variability through a set of month embedding vectors. Comparative evaluations reveal that Planet-NeRF outperforms prior models in the case where seasonal changes are present. The extensive evaluation combined with the proposed method offers promising avenues for future research in this domain. © 2024, CC BY.

关键词： 3D reconstruction

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Optimizing Transistor Selection Using AH-TOR for Low Noise Amplifier Design in Sensing Applications

Optimizing Transistor Selection Using AH-TOR for Low Noise A...

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International Conference on Sensing Technology, ICST

作者： Dayarnab Baidya Karthick Thiyagarajan Mitradip Bhattacharjee Yang An Department of Electrical Engineering and Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith Australia

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

The design of a wireless sensor network receiver requires an efficient Low Noise Amplifier (LNA), with the transistor being a critical component that influences its performance. Selecting an optimal transistor for LNA design is a complex and time-consuming process due to the conflicting criteria involved. This study utilizes the Analytic Hierarchy Trade-off Ranking Method (AH-TOR), an integrated Multi-Criteria Decision-Making (MCDM) approach, to facilitate the selection of a suitable transistor for LNA design. Four types of transistors were evaluated as alternatives based on four criteria, with the Pseudomorphic High Electron Mobility Transistor (PHEMT) emerging as the optimal choice. The weight of PHEMT was calculated to be 1.0787, and its performance was benchmarked against other existing methods. Further analysis demonstrated the robustness of the AH-TOR method across various scenarios, consistently identifying PHEMT as the most suitable option. These findings highlight the reliability of the AH-TOR method for transistor selection in diverse applications.

关键词： Low-noise amplifiers Wireless sensor networks PHEMTs Decision making Receivers Electron mobility Robustness Sensors Transistors Integrated circuit reliability

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EYNet: Extended YOLO for Airport Detection in Remote Sensing Images

arXiv

引用

arXiv 2022年

作者： Mirhajianmoghadam, Hengameh Haghighi, Behrouz Bolourian Electrical Engineering Department Ferdowsi University of Mashhad Mashhad Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Machine Vision Laboratory Ferdowsi University of Mashhad Mashhad Iran

Nowadays, airport detection in remote sensing images has attracted considerable attention due to its strategic role in civilian and military scopes. In particular, uncrewed and operated aerial vehicles must immediately detect safe areas to land in emergencies. The previous schemes suffered from various aspects, including complicated backgrounds, scales, and shapes of the airport. Meanwhile, the rapid action and accuracy of the method are confronted with significant concerns. Hence, this study proposes an effective scheme by extending YOLOV3 and ShearLet transform. In this way, MobileNet and ResNet18, with fewer layers and parameters retrained on a similar dataset, are parallelly trained as base networks. According to airport geometrical characteristics, the ShearLet filters with different scales and directions are considered in the first convolution layers of ResNet18 as a visual attention mechanism. Besides, the major extended in YOLOV3 concerns the detection Sub-Networks with novel structures which boost object expression ability and training efficiency. In addition, novel augmentation and negative mining strategies are presented to significantly increase the localization phase’s performance. The experimental results on the DIOR dataset reveal that the framework reliably detects different types of airports in a varied area and acquires robust results in complex scenes compared to traditional YOLOV3 and state-of-the-art schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Classifying Cover Crop Residue from RGB Images: A Simple SVM versus a SVM Ensemble

Classifying Cover Crop Residue from RGB Images: A Simple SVM...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Parth C. Upadhyay Lokesh Karanam John A. Lory Guilherme N. DeSouza Vision-Guided and Intelligent Robotics (ViGIR) Laboratory Department of Electrical Engineering and Computer Science (EECS) Division of Plant Sciences University of Missouri Columbia MO

ISBN: (纸本)9781728190495

Plant-derived crop residue on soil surface provides many important advantages including preventing erosion and conserving soil moisture. In that sense, making accurate determination on the percent of crop residue cover using RGB images can be a fundamental tool in protecting the soil. In our research, we approach the determination of such percentages as a classification problem, and in this paper, we compare two of these approaches. Both approaches relied on support vector machines (SVM) as the classifier of choice, and the same set of features, which were selected in our previous studies on the same topic. In this paper we developed a SVM ensemble with a hierarchical structure and compared it against a single, multi-class SVM classifier. In the SVM ensemble framework, four two-class SVMs and one five-class SVM were combined in sequence to better separate adjacent levels of residue cover. The rationale of the ensemble was to allow each of the two-class SVMs to find the hyperplanes that maximize the margin between the corresponding two consecutive classes. Then, based on the distance of the samples to these hyperplanes, probabilistic estimates of the data-point belonging to the class were computed and added as extra inputs for the last SVM. In order to enhance the performance of the ensemble, other considerations such as the use of Grid Search method for optimizing the hyperparameters were employed in the tuning of the SVMs. Numerical experiments were conducted over a dataset of 4,400 images, which were collected from 88 locations in 40 row crop fields in five Missouri counties between mid-April and early July in 2018 and 2019. The images were collected using a camera mounted on a tripod, with a spatial resolution of 0.014 cm pixel- 1 GSD (Ground Sampling Distance). The experiments highlighted the better performance of the proposed hierarchical ensemble classifier, which achieved a cross-validation accuracy of 86.3 % vs an accuracy of 80.4 % for the single SVM, whil

关键词： Support vector machines Measurement Training Search methods Soil moisture Crops Probabilistic logic

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Video Instance Segmentation with Recurrent Graph Neural Networks 43rd

Video Instance Segmentation with Recurrent Graph Neural Net...

引用

43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021

作者： Johnander, Joakim Brissman, Emil Danelljan, Martin Felsberg, Michael Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden Zenseact Gothenburg Sweden Saab Bröderna Ugglas gata Linköping582 54 Sweden Computer Vision Lab ETH Zürich Zürich Switzerland School of Engineering University of KwaZulu-Natal Durban South Africa

ISBN: (纸本)9783030926588

Video instance segmentation is one of the core problems in computer vision. Formulating a purely learning-based method, which models the generic track management required to solve the video instance segmentation task, is a highly challenging problem. In this work, we propose a novel learning framework where the entire video instance segmentation problem is modeled jointly. To this end, we design a graph neural network that in each frame jointly processes all detections and a memory of previously seen tracks. Past information is considered and processed via a recurrent connection. We demonstrate the effectiveness of the proposed approach in comprehensive experiments. Our approach, operating at over 25 FPS, outperforms previous video real-time methods. We further conduct detailed ablative experiments that validate the different aspects of our approach. © 2021, Springer Nature Switzerland AG.

关键词： computer vision

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Bioinspired Musculoskeletal Model-based Soft Wrist Exoskeleton for Stroke Rehabilitation

引用

Journal of Bionic engineering 2020年第6期17卷 1163-1174页

作者： Ning Li Tie Yang Yang Yang Peng Yu Xiujuan Xue Xingang Zhao Guoli Song Imad HElhajj Wenxue Wang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Itelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Bejing 100049China Rehabilitation Center for the Disabled Shenyang 110015China Vision and Robotics Lab Department of Electrical and Computer EngineeringAmerican University of BeirutBeirut 11072020Lebanon Emerging Technologies Instinute Department of Industrial&Manufacturing Systems EngineeringUniversity of Hong KongPokfulamHong Kong 999077China

Exoskeleton robots have demonstrated the potential to rehabilitate stroke ***,poor human-machine physiological coupling causes unexpected damage to human of muscles and ***,inferior humanoid kinematics control would restrict human natural *** to deal with these problems results in bottlenecks and hinders its *** this paper,the simplified muscle model and muscle-liked kinematics model were proposed,based on which a soft wrist exoskeleton was established to realize natural human ***,we simplified the redundant muscular system related to the wrist joint from ten muscles to four,so as to realize the human-robot physiological ***,according to the above human-like musculoskeletal model,the humanoid distributed kinematics control was established to achieve the two DOFs coupling kinematics of the *** results show that the wearer of an exoskeleton could reduce muscle activation and joint force by 43.3%and 35.6%,***,the humanoid motion trajectories similarity of the robot reached 91.5%.Stroke patients could recover 90.3%of natural motion ability to satisfy for most daily *** work provides a fundamental understanding on human-machine physiological coupling and humanoid kinematics control of the exoskeleton robots for reducing the post-stroke complications.

关键词： stroke rehabilitation simplified muscle model soft wrist exoskeleton robot bionic kinematics model

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