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Automatic Root Length Estimation from Images Acquired In Situ without Segmentation

Plant Phenomics 2024年第1期6卷 79-93页

作者： Faina Khoroshevsky Kaining Zhou Sharon Chemweno Yael Edan Aharon Bar-Hillel Ofer Hadar Boris Rewald Pavel Baykalov Jhonathan EEphrath Naftali Lazarovitch Department of Industrial Engineering and Management Ben-Gurion University of the NegevBeer ShevaIsrael The Jacob Blaustein Center for Scientific Cooperation The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael The Albert Katz International School for Desert Studies The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael Department of Communication Systems Engineering School of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer ShevaIsrael Institute of Forest Ecology Department of Forest and Soil SciencesUniversity of Natural Resources and Life SciencesVienna(BOKU)ViennaAustria Faculty of Forestry and Wood Technology Mendel University in BrnoBrnoCzech Republic Vienna Scientific Instruments GmbH AllandAustria French Associates Institute for Agriculture and Biotechnology of Drylands The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael

Image-based root phenotyping technologies,including the minirhizotron(MR),have expanded our understanding of the in situ root responses to changing environmental *** conventional manual methods used to analyze MR images are time-consuming,limiting their *** study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total(cumulative)root length(TRL)per MR image without requiring *** data were derived from manual annotations in Rootfly,commonly used software for MR image *** compared TRL estimation with 2 models,a regression-based model and a detection-based model that detects the annotated points along the ***,the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR *** models were trained and tested with 4,015 images acquired using 2 MR system types(manual and automated)and from 4 crop species(corn,pepper,melon,and tomato)grown under various abiotic *** datasets are made publicly available as part of this *** coefficients of determination(R2),between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets,demonstrating that this tool is accurate and *** analyses were conducted to examine the effects of(a)the data acquisition system and thus the image quality on the models'performance,(b)automated differentiation between images with and without roots,and(c)the use of the transfer learning *** approaches can support precision agriculture by providing real-time root growth information.

关键词： Image Root image

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

CITR: A Coordinate-Invariant Task Representation for Robotic...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Peter So Rafael I. Cabral Muchacho Robin Jeanne Kirschner Abdalla Swikir Luis Figueredo Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence TUM Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Libya School of Computer Science University of Nottingham UK Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned *** this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

关键词： Learning systems Coordinate measuring machines Robot kinematics Taxonomy Fingerprint recognition Mathematical models Libraries

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Industrial Scenario of Renewable Energy-Based Electromobility 3rd

Industrial Scenario of Renewable Energy-Based Electromobilit...

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3rd International Conference on Energy systems, Drives and Automations, ESDA 2020

作者： Szymanski, Jerzy Zurek-Mortka, Marta Faculty of Transport Electrical Engineering and Computer Science Kazimierz Pulaski University of Technology and Humanities Radom Poland Department of Control Systems Lukasiewicz Research Network Institute for Sustainable Technology Radom Poland

ISBN: (纸本)9789811672736

The article presents the concept of a hybrid network topology in the enterprises with the use of a solar power plant and energy storage as well as a drive frequency converter for charging of transportation battery of wheels vehicles and powering of electric caterpillar mining machines. Two important functionalities of the drive frequency converter as a unidirectional EV battery charger and for powering electric motors are presented. The obtained results of the simulation and experimental tests of the presented model confirm the possibility of controlling the DC current value of connected to drive frequency converter of the diode rectifier unit charging the EV battery. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Place recognition survey: An update on deep learning approaches

arXiv

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arXiv 2021年

作者： Barros, Tiago Pereira, Ricardo Garrote, Luís Premebida, Cristiano Nunes, Urbano J. The University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal

Autonomous Vehicles (AV) are becoming more capable of navigating in complex environments with dynamic and changing conditions. A key component that enables these intelligent vehicles to overcome such conditions and become more autonomous is the sophistication of the perception and localization systems. As part of the localization system, place recognition has benefited from recent developments in other perception tasks such as place categorization or object recognition, namely with the emergence of deep learning (DL) frameworks. This paper surveys recent approaches and methods used in place recognition, particularly those based on deep learning. The contributions of this work are twofold: surveying recent sensors such as 3D LiDARs and RADARs, applied in place recognition;and categorizing the various DL-based place recognition works into supervised, unsupervised, semi-supervised, parallel, and hierarchical categories. First, this survey introduces key place recognition concepts to contextualize the reader. Then, sensor characteristics are addressed. This survey proceeds by elaborating on the various DL-based works, presenting summaries for each framework. Some lessons learned from this survey include: the importance of NetVLAD for supervised end-to-end learning;the advantages of unsupervised approaches in place recognition, namely for cross-domain applications;or the increasing tendency of recent works to seek, not only for higher performance but also for higher efficiency. © 2021, CC BY.

关键词： Surveys

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Operating with Inaccurate Models by Integrating Control-Level Discrepancy Information into Planning

Operating with Inaccurate Models by Integrating Control-Leve...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ellis Ratner Claire J. Tomlin Maxim Likhachev Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA

Typical robotic systems rely on models for planning. Therefore, the quality of the robot's behavior is heavily dependent on how accurately the model can predict the outcome of the robot's actions in the environment. A challenge, however, is that no model is perfect; moreover, we often do not know where discrepancies between the model's prediction and the actual outcome occur prior to observing executions in the real-world. One way to address this is to bias the planner away from these discrepancies by inflating the cost of states and actions where we previously observed the model to be inaccurate. Making such decisions about where and how to bias purely at the planning-level, however, neglects valuable information from the control-level, which gives a more fine-grained understanding of where and how the model went wrong during execution. Based on this observation, our key idea is to first infer a statistical model over discrepancies in the control-level's model. Then, we translate this model to the planning-level, where we use it to more informatively bias the planner away from states and actions where the model's predicted outcome is likely to be inaccurate. We demonstrate that our framework enables a robot to complete tasks, despite an inaccurate planning model, with greater efficiency than existing approaches. We do so through an experimental evaluation in simulation and real-robot experiments on NASA's Astrobee free-flyer.

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From instantaneous schedulability to worst case schedulability: a significant moment approach

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Cyber-Physical systems 2024年

作者： Yao, Ningshi Zhang, Fumin Department of Electrical and Computer Engineering George Mason University FairfaxVA United States Cheng Kar-Shun Robotics Institute Hong Kong University of Science and Technology Hong Kong Hong Kong SAR Hong Kong

The method of significant moment analysis has been used to derive instantaneous schedulability tests for real-time tasks. However, the instantaneous schedulability can only be checked within a finite time window. On the other hand, worst-case schedulability guarantees schedulability of tasks for infinite time. This paper derives the classical worst-case schedulability conditions for pre-emptive periodic tasks starting from instantaneous schedulability, hence unifying the two notions of schedulability. The results provide a rigorous justification for the critical time instants being the worst case for scheduling pre-emptive periodic tasks. The paper also shows that the critical time instant is not the only worst-case moments. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： critical instant analysis instantaneous schedulability significant moment analysis Worst-case schedulability

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Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Re...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Hyeonmin Lee Dongchan Kim Donggyu Beom Gyurin Byun Van-Vi Vo Hyunseung Choo Department of AI Systems Engineering Sungkyunkwan University Suwon Republic of Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Republic of Korea Convergence Research Institute Sungkyunkwan University Suwon Republic of Korea Department of Computer Science and Engineering Sungkyunkwan University Suwon Republic of Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural Networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. The model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. The attack type of each timestamp were visualized using distinct colors to the grid images. These images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. This graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. This research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences.

关键词： Solid modeling Protocols Image color analysis Image edge detection Telecommunication traffic Traffic control Graph neural networks Spatiotemporal phenomena Information management computer crime

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Integration of SAP and Intelligent Robotic Process Automation

Integration of SAP and Intelligent Robotic Process Automatio...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Yendluri, Dileep Kumar Tatikonda, Reshmi Thatikonda, Ramya Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science WilliamsburgMO United States University of the Central Missouri Department of Computer Information Systems WarrensburgMO United States University of the Cumberlands Department of Information Technology WilliamsburgMO United States Jawaharlal Technological University Department of ECE Hyderbad India Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350319088

This article explores how implementing IRPA in enterprises could have a revolutionary effect. Businesses looking to maximize efficiency can find a partnership between IRPA and SAP systems to be a wise way to achieve their goals. This study investigates how several company operations including payroll processing, human administration, financial and accounting processes, and logistics are impacted by integration. This talk explores the benefits, challenges, and best practices for integrating IRPA with SAP, offering specific instances from real-world projects that highlight the effectiveness of this relationship. The future scope of the IRPA-SAP integration looks at enhanced analytics, hyper-automation, cutting-edge cognitive features, broader industry applications, and the growth of human-robot cooperation. A thorough analysis highlights tactics that businesses may use to use SAP's and IRPA's combined power to accelerate digital transformation and achieve peak performance. © 2023 IEEE.

关键词： Integration

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A Sparsity-driven tinyML Accelerator for Decoding Hand Kinematics in Brain-computer Interfaces

A Sparsity-driven tinyML Accelerator for Decoding Hand Kinem...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Adithya Krishna Vignesh Ramanathan Satyapreet Singh Yadav Sahil Shah André van Schaik Mahesh Mehendale Chetan Singh Thakur Department of Electronic Systems Engineering Indian Institute of Science Bangalore India International Centre for Neuromorphic Systems The MARCS Institute Western Sydney University Australia Department of Electrical and Computer Engineering University of Maryland USA

Despite advances in understanding the mechanisms of movement disorders, controlling voluntary movements remains challenging, with limited treatment options. However, the integration of machine learning (ML) accelerators into the braincomputer interface (BCI) pipeline offers promising solutions by harnessing the capabilities of ML algorithms to decode intended motor actions accurately. While there have been numerous efforts to classify intended hand movements into discrete classes, the challenging problem of decoding continuous hand kinematics has seen limited research efforts. Our work focuses on tackling this challenge using RAMAN, an energy-efficient tinyML accelerator. We demonstrate the successful decoding of macaque hand kinematics from the MC_Maze dataset on the Efinix Ti60 FPGA with RAMAN architecture. Our approach achieves an R2-score of 0.91 across 108 different maze configurations with a significantly low memory footprint of 230 KB, a latency of 86.7 ms while consuming 52.23 mW of power at a 5 MHz clock rate and 5.78 ms latency with the peak power efficiency of 102.34 GOp/s/W at 75 MHz and 75% pruning.

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