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Lumbar spine localisation method based on feature fusion

CAAI Transactions on Intelligence Technology 2023年第3期8卷 931-945页

作者： Yonghong Zhang Ning Hu Zhuofu Li Xuquan Ji Shanshan Liu Youyang Sha Xiongkang Song Jian Zhang Lei Hu Weishi Li Robotics Institute School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina Beijing Zhuzheng Robot Co. LTDBeijingChina Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKnoxvilleTennesseeUSA Department of Orthopaedics Peking University Third HospitalBeijingChina Engineering Research Center of Bone and Joint Precision Medicine Ministry of EducationBeijingChina Beijing Key Laboratory of Spinal Disease Research BeijingChina School of Biological Science and Medical Engineering Beihang UniversityBeijingChina Department of Computer Science University of WarwickCoventryUK

To eliminate unnecessary background information,such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning,a two‐stage approach for localising lumbar segments is ***,based on the multi‐scale feature fusion technology,a non‐linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine,effectively eliminating useless background information,such as soft *** the second stage,we directly realised the precise positioning of each segment in the lumbar spine space region based on the non‐linear regression method,thus effectively eliminating the interference caused by the adjacent *** 3D Intersection over Union(3D_IOU)is used as the main evaluation indicator for the positioning *** an open dataset,3D_IOU values of 0.8339�0.0990 and 0.8559�0.0332 in the first and second stages,respectively is *** addition,the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s ***,the proposed method performs very well in terms of both pre-cision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.

关键词： CT image lumbar spatial orientation multi‐scale information fusion

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Incentive Mechanism for Throughput Enhancement in Blockchain-based Energy Trading System

Incentive Mechanism for Throughput Enhancement in Blockchain...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Liu, Yunshu Cheung, Man Hon Huang, Jianwei The Chinese University of Hong Kong Department of Information Engineering Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China City University of Hong Kong Department of Computer Science Hong Kong The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

ISBN: (纸本)9798350333336

In a blockchain-based energy trading system, prosumers engage in energy trading and simultaneously participate in blockchain mining to process trading transactions. However, as blockchain mining consumes energy, prosumers may lack incentives to participate, resulting in a low blockchain throughput and negatively affecting the energy trading market. To address this issue, we propose a Two-Piece Linear Compensation (TPLC) mechanism to incentivize sufficient participation for blockchain mining. We model the mechanism design as a two-stage game, wherein the system designer proposes the mechanism in Stage I, and prosumers decide on their energy allocation strategies in Stage II. We analyze the complex and coupled interactions of prosumers and uncover the reason for the low throughput issue: a self-interested prosumer neglects the positive externality of energy allocation to the blockchain, as it does not benefit him. Despite the diverse objectives of prosumers and the large search space of the mechanism, we demonstrate that the distributed TPLC mechanism achieves the maximum social welfare of a centralized decision benchmark. The numerical analysis compares the TPLC mechanism with linear compensation and no compensation benchmarks. Our results show that the TPLC mechanism achieves the highest social welfare and average payoff for prosumers. Additionally, the TPLC mechanism supports a large energy market without sacrificing the user experience. © 2023 IEEE.

关键词： Blockchain

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Thermal Ablation Therapy Control with Tissue Necrosis-driven Temperature Feedback Enabled by Neural State Space Model with Extended Kalman Filter

Thermal Ablation Therapy Control with Tissue Necrosis-driven...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ryo Murakami Satoshi Mori Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA The United States Department of Biomedical Engineering Robotics Engineering and Computer Science Engineering Worcester Polytechnic Institute Worcester MA The United States

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Thermal ablation therapy is a major minimally invasive treatment. One of the challenges is that the targeted region and therapeutic progression are often invisible to clinicians, requiring feedback provided in numerical information or imaging. Several emerging imaging modalities offer visualization of the ablation-induced necrosis formation; however, relying solely on necrosis monitoring can result in tissue overheating and endangering patients. Some of the necrosis monitoring modalities are known for their capabilities in temperature sensing, but the principles on which they are based have several limitations, such as sensitivity to the tissue motion and their environment. In this study, we propose a necrosis progression-based temperature estimation technique as an added safety feature for avoiding overheating. This model-based method does not require additional sensing hardware. It is designed to work as an independent estimator or a complimentary estimation component with other thermometers for improved robustness. For this objective, the Neural State Space model is used to approximate the ablation therapy, whose theoretical models involve nonlinear partial differential equations. Then, the Extended Kalman Filter is designed based on the model. The simulation study shows the estimation module robustly estimates the tissue temperature under several types of noise. The maximum estimation error observed before terminating ablation was around 1 °C, and the desired safety feature was successfully demonstrated. The estimator is expected to be used in a variety of necrosis monitoring modalities to guarantee more precise and safer treatment. More ambitiously, the architecture with the Neural State Space model and Extended Kalman Filter is generalizable to other medical/biological procedures involving nonlinear and patient/environment-specific physics and even to procedures having no reliable theoretical models.

关键词： Thermometers Estimation Medical treatment Mathematical models Robustness Sensors Safety Temperature control Kalman filters Monitoring

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RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment

RiskMap: A Unified Driving Context Representation for Autono...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Xin, Ren Wang, Sheng Chen, Yingbing Cheng, Jie Liu, Ming Ma, Jun The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Hong Kong Robotics and Autonomous Systems Thrust Guangzhou511453 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665481090

Motion planning is a complicated task that requires the combination of perception, map information integration and prediction, particularly when driving in heavy traffic. Developing an extensible and efficient representation that visualizes sensor noise and provides basis to real-time planning tasks is desirable. We aim to develop an interpretable map representation, which offers prior of driving cost in planning tasks. In this way, we can simplify the planning process for dealing with complex driving scenarios and visualize sensor noise. Specifically, we propose a unified context representation empowered by deep neural networks. The unified representation is a differentiable risk field, which is an analytical representation of statistical cognition regarding traffic participants for downstream planning tasks. This representation method is nominated as RiskMap. A sampling-based planner is adopted to train and compare RiskMap generation methods. In this paper, the RiskMap generation tools and model structures are explored, the results illustrate that our method can improve driving safety and smoothness, and the limitation of our method is also discussed. © 2024 IEEE.

关键词： Motion planning

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Detection of Gender and Age Category from Speech

Detection of Gender and Age Category from Speech

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2023 World Symposium on Digital Intelligence for Systems and Machines, DISA 2023

作者： Haluška, Renát Popovič, Michal Pleva, Matus Frohman, Matthew Department of Electronics and Multimedia Communications Faculty of Electrical Engineering and Informatics Technical University Kosice Kosice Slovakia Perception and Robotics Group Department of computer science University of Maryland College ParkMD United States

ISBN: (纸本)9798350343533

The main goal of this paper was to find out how the gender and age group acoustical models behave on audio data that is in no way related to the data corpora used to train and evaluate the models. These models could be used for early predator detection in minors’ audio chats. The datasets used for training were mainly composed of English speakers. One part was made up of speakers whose native language is English and the rest use English as a secondary language. © 2023 IEEE.

关键词： Electronics industry

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Efficient-Net ASPP Deep Network for Malignant Ultrasound Breast Cancer Segmentation

Efficient-Net ASPP Deep Network for Malignant Ultrasound Bre...

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2023 IEEE International Conference on Emerging Trends in engineering, sciences and Technology, ICES and T 2023

作者： Azam, Komal M.A., Azam M.A., Qureshi K.B., Khan M.A., Azam College Women University The Government Sadiq Department of Computer Science Bahawalpur Pakistan The Islamia University of Bahawalpur The Faculty of Engineering Bahawalpur Pakistan Istituto Italiano di Tecnologia Department of Advanced Robotics Genoa Italy

ISBN: (纸本)9781665455602

Worldwide, breast cancer is one of the main causes of mortality among women. Only through early recognition of symptoms is it possible to limit the incidence of premature deaths. Utilizing standard deep-learning semantic segmentation, tumors are automatically segmented. It requires minimal human contact. The standard methods are less efficient and precise in accuracy. In this paper, we propose an Efficient-Net based Atrous Spatial Pyramid Pooling (Efficient-Net ASPP) model for efficient and improved segmentation. The proposed model is comprised of an EfficientNetV2B3 backbone with dilated convolutional, skip connection, max pooling, and an ASSP block. A publicly available Malignant Breast Cancer Ultrasound (MBCU) subset image dataset was investigated in our study. Several baseline segmentation methods were compared with our proposed method. The quantitative benchmark analysis shows the proposed model for segmenting MBCU images achieved 70% intersection over union (IOU) and a 62% score on the Dice Similarity Coefficient (DSC) metrics. The proposed network improves MBCU image segmentation by 14% on DSC and 18% on IOU when compared to baseline models. © 2023 IEEE.

关键词： Ultrasonics

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Deployment of Large Language Models to Control Mobile Robots at the Edge

arXiv

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arXiv 2024年

作者： Sikorski, Pascal Schrader, Leendert Yu, Kaleb Billadeau, Lucy Meenakshi, Jinka Mutharasan, Naveena Esposito, Flavio AliAkbarpour, Hadi Babaiasl, Madi Computer Science Department Aerospace & Mechanical Engineering Department Science Department Robotics Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis United States

This paper investigates the possibility of intuitive human-robot interaction through the application of Natural Language Processing (NLP) and Large Language Models (LLMs) in mobile robotics. This work aims to explore the feasibility of using these technologies for edge-based deployment, where traditional cloud dependencies are eliminated. The study specifically contrasts the performance of GPT-4-Turbo, which requires cloud connectivity, with an offline-capable, quantized version of LLaMA 2 (LLaMA 2-7B.Q5 K M). These results show that GPT-4-Turbo delivers superior performance in interpreting and executing complex commands accurately, whereas LLaMA 2 exhibits significant limitations in consistency and reliability of command execution. Communication between the control computer and the mobile robot is established via a Raspberry Pi Pico W, which wirelessly receives commands from the computer without internet dependency and transmits them through a wired connection to the robot’s Arduino controller. This study highlights the potential and challenges of implementing LLMs and NLP at the edge, providing groundwork for future research into fully autonomous and network-independent robotic systems. For video demonstrations and source code, please refer to: https://***/MobileRobotGPT4LLaMA2024. © 2024, CC BY.

关键词： Human robot interaction

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Preprocessing-based Kinodynamic Motion Planning Framework for Intercepting Projectiles using a Robot Manipulator

Preprocessing-based Kinodynamic Motion Planning Framework fo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ramkumar Natarajan Hanlan Yang Qintong Xie Yash Oza Manash Pratim Das Fahad Islam Muhammad Suhail Saleem Howie Choset Maxim Likhachev The Robotics Institute School of Computer Science Carnegie Mellon University USA Department of Engineering Science University of Oxford UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile’s future motion, (iii) plan a minimum-time rapid trajectory that can evade obstacles and intercept the projectile, and (iv) execute the planned trajectory. These four steps must be performed under the manipulator’s dynamic limits and extreme time constraints (≤ 350ms in our setting) to successfully intercept the projectile. In addition, we want these trajectories to be smooth to reduce the robot’s joint torques and the impulse on the platform on which it is mounted. To this end, we propose a kinodynamic motion planning framework that preprocesses smooth trajectories offline to allow real-time collision-free executions online. We present an end-to-end pipeline along with our planning framework, including perception, prediction, and execution modules. We evaluate our framework experimentally in simulation and show that it has a higher blocking success rate than the baselines. Further, we deploy our pipeline on a robotic system comprising an industrial arm (ABB IRB-1600) and an onboard stereo camera (ZED 2i), which achieves a 78% success rate in projectile interceptions.

关键词： Service robots Projectiles Pipelines Robot vision systems Trajectory Planning Time factors

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PFAS Incineration: Known Unknowns, Reaction Simulation, and ML to Predict Product Properties

PFAS Incineration: Known Unknowns, Reaction Simulation, and ...

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2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

作者： Soiffer, Duncan Pyorre, Tuomas Kenney, David Teixeira, Andrew Worcester Polytechnic Institute Department of Computer Science and Mathematical Sciences WorcesterMA United States Worcester Polytechnic Institute Department of Robotics Engineering WorcesterMA United States Worcester Polytechnic Institute Department of Chemical Engineering WorcesterMA United States

ISBN: (纸本)9798350308600

Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals used for their impressive stability, a property which has also led them to be found polluting virtually every sector of the environment-including our own bodies. Presently, our ability to destroy PFAS is limited largely to incineration, whose products and side effects are poorly understood. Research on PFAS incineration is constrained by high experimental costs and difficulty in accurately measuring reaction products. This presents an opportunity for computer simulation and machine learning to provide valuable assistance. In this study, three kinds of thermal PFAS decomposition are simulated. Further, to broaden physical understanding of PFAS incineration products, a recent in-house machine learning model for predicting the octanol-water partition coefficient (LogP) of chemicals is improved and applied to simulation results. A decrease in LogP is observed in all treatments, with the most dramatic effect (-2 average LogP) observed in oxidative pyrolysis. © 2023 IEEE.

关键词： Machine learning

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Transformers for Image-Goal Navigation

arXiv

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arXiv 2024年

作者： Pelluri, Nikhilanj V. Minnesota Robotics Institute Department of Computer Science and Engineering University of Minnesota Twin Cities United States

Visual perception and navigation have emerged as major focus areas in the field of embodied artificial intelligence. We consider the task of image-goal navigation, where an agent is tasked to navigate to a goal specified by an image, relying only on images from an onboard camera. This task is particularly challenging since it demands robust scene understanding, goal-oriented planning and long-horizon navigation. Most existing approaches typically learn navigation policies reliant on recurrent neural networks trained via online reinforcement learning. However, training such policies requires substantial computational resources and time, and performance of these models is not reliable on long-horizon navigation. In this work, we present a generative Transformer based model that jointly models image goals, camera observations and the robot’s past actions to predict future actions. We use state-of-the-art perception models and navigation policies to learn robust goal conditioned policies without the need for real-time interaction with the environment. Our model demonstrates capability in capturing and associating visual information across long time horizons, helping in effective navigation. © 2024, CC BY.

关键词： Navigation

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