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arXiv 2024年

作者： Bravo-Frank, Nicholas Mesyngier, Nicolas Feng, Lei Hong, Jiarong Department of Electrical and Computer Engineering University of Minnesota United States Saint Anthony Falls Laboratory University of Minnesota United States Minnesota Robotics Institute University of Minnesota United States Department of Mechanical Engineering University of Minnesota United States

We developed a digital inline holography (DIH) system integrated with deep learning algorithms for real-time detection of particulate matter (PM) and bacterial contamination in peritoneal dialysis (PD) fluids. The system comprises a microfluidic sample delivery module and a DIH imaging module that captures holograms using a pulsed laser and a digital camera with a 40× objective. Our data processing pipeline enhances holograms, reconstructs images, and employs a YOLOv8nbased deep learning model for particle identification and classification, trained on labeled holograms of generic PD particles, Escherichia coli (E. coli), and Pseudomonas aeruginosa (P. aeruginosa). The system effectively detected and classified generic particles in sterile PD fluids, revealing diverse morphologies predominantly sized 1–5 µm with an average concentration of 61 particles/µL. In PD fluid samples spiked with high concentrations of E. coli and P. aeruginosa, our system achieved high sensitivity (>90%) in detecting and classifying these bacteria at clinically relevant low false positive rates (~0.5%). Further validation against standard colony-forming unit (CFU) methods using PD fluid spiked with bacterial concentrations from approximately 100 to 10,000 bacteria/mL demonstrated a clear one-to-one correspondence between our measurements and CFU counts. Our DIH system provides a rapid, accurate alternative to traditional culture-based methods for assessing bacterial contamination in PD fluids. By enabling real-time sterility monitoring, it can significantly improve patient outcomes in PD treatment, facilitate point-of-care fluid production, reduce logistical challenges, and be extended to quality control in pharmaceutical production. © 2024, CC BY.

关键词： Escherichia coli

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Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks

arXiv

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arXiv 2023年

作者： Xiong, Ziliang Jonnarth, Arvi Eldesokey, Abdelrahman Johnander, Joakim Wandt, Bastian Forssén, Per-Erik Computer Vision Laboratory Department of Electrical Engineering LiU Sweden Visual Computing Center KAUST Saudi Arabia Husqvarna Group Huskvarna Sweden Zenseact Sweden

computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by predicting probability distributions. In this context, we investigate the regression-by-classification paradigm which can represent multimodal distributions, without a prior assumption on the number of modes. Through experiments on a specifically designed synthetic dataset, we demonstrate that traditional loss functions lead to poor probability distribution estimates and severe overconfidence, in the absence of full ground truth distributions. In order to alleviate these issues, we propose hinge-Wasserstein – a simple improvement of the Wasserstein loss that reduces the penalty for weak secondary modes during training. This enables prediction of complex distributions with multiple modes, and allows training on datasets where full ground truth distributions are not available. In extensive experiments, we show that the proposed loss leads to substantially better uncertainty estimation on two challenging computer vision tasks: horizon line detection and stereo disparity estimation. The code is available at:https://***/XZLeo/ hinge-Wasserstein Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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A Stable Lithium-ion Battery SOH Estimation Framework for Suppressing Measurement Noise with Unknown Distribution

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IEEE Transactions on Transportation Electrification 2025年

作者： Ma, Wentao Xue, Jingsong Li, Yang Guo, Peng Liu, Xinghua Wei, Zhongbao Wang, Yiwen Chen, Badong Xi’an University of Technology School of Electrical Engineering 710048 China Chalmers University of Technology Department of Electrical Engineering Gothenburg41296 Sweden Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Institute of artificial intelligence and robotics Xi’an Jiaotong University Xi’an710048 China

Existing methods for estimating the state of health (SOH) of lithium-ion battery (LIB) typically rely on the assumption that the distribution of noise (or outliers) in the measurement data is known. However, this assumption rarely holds true for LIB operating under real-word conditions. This article proposes a stable framework for accurate SOH estimation that accommodates noises with unknown distribution in both measurement data and label values. The framework combines generalized correntropy loss (GCL) with Savitzky-Golay (SG) filter and extreme learning machine (ELM) to obtain measurement data filter named SG-GCL and SOH estimator named generalized ELM (GELM), respectively. The SG-GCL filtering of the measurement data keeps the root mean square error (RMSE) within 0.0365%, and Pearson correlation between extracted feature and SOH improves by 0.4963, which in turn leads to the reduction of the RMSE metrics of the ELM for the estimation of the SOH by 43.69%. From the filtering results, feature extraction and estimation results proved its necessity and effectiveness. GELM effectively suppresses the influence of label value noise on the model in the training process, which reduces the SOH estimation RMSE index by more than 0.66%. The results from experiments with different distributional noise conditions show that the proposed SOH estimation framework has excellent and stable performance. © 2015 IEEE.

关键词： Mean square error

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A tethered quadrotor UAV-buoy system for marine locomotion

arXiv

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arXiv 2021年

作者： Kourani, Ahmad Daher, Naseem Vision and Robotics Lab Department of Mechanical Engineering American University of Beirut Beirut Lebanon Vision and Robotics Lab Department of Electrical and Computer Engineering

Unmanned aerial vehicles (UAVs) are finding their way into offshore applications. In this work, we postulate an original system that entails a marine locomotive quadrotor UAV that manipulates the velocity of a floating buoy by means of a cable. By leveraging the advantages of UAVs relative to high speed, maneuverability, ease of deployment, and wide field of vision, the proposed UAV-buoy system paves the way in front of a variety of novel applications. The dynamic model that couples the buoy, UAV, cable, and water environment is presented using the Euler-Lagrange method. A stable control system design is proposed to manipulate the forward-surge speed of the buoy under two constraints: maintaining the cable in a taut state, and keeping the buoy in contact with the water surface. Polar coordinates are used in the controller design process to attain correlated effects on the tracking performance, whereby each control channel independently affects one control parameter. This results in improved performance over traditional Cartesian-based velocity controllers, as demonstrated via numerical simulations in wave-free and wavy seas. Copyright © 2021, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Autonomous Multiple-Trolley Collection System with Nonholonomic Robots: Design, Control, and Implementation

arXiv

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arXiv 2024年

作者： Xie, Peijia Xia, Bingyi Hu, Anjun Zhao, Ziqi Meng, Lingxiao Sun, Zhirui Gao, Xuheng Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Jiaxing Research Institute Southern University of Science and Technology Jiaxing China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

The intricate and multi-stage task in dynamic public spaces like luggage trolley collection in airports presents both a promising opportunity and an ongoing challenge for automated service robots. Previous research has primarily focused on handling a single trolley or individual functional components, creating a gap in providing cost-effective and efficient solutions for practical scenarios. In this paper, we propose a mobile manipulation robot incorporated with an autonomy framework for the collection and transportation of multiple trolleys that can significantly enhance operational efficiency. We address the key challenges in the trolley collection problem through the novel design of the mechanical system and the vision-based control strategy. We design a lightweight manipulator and docking mechanism, optimized for the sequential stacking and transportation of multiple trolleys. Additionally, based on the Control Lyapunov Function and Control Barrier Function, we propose a novel vision-based control with the online Quadratic Programming which significantly improves the accuracy and efficiency of the collection process. The practical application of our system is demonstrated in real-world scenarios, where it successfully executes multiple-trolley collection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Quadratic programming

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Leveraging PID gain selection towards adaptive backstepping control for a class of second-order systems

arXiv

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arXiv 2021年

—In this work, we establish a convenient similarity between an adaptive backstepping control law and a standard proportional-integral-derivative (PID) controller for a class of second-order systems. The extracted similarity provides a deeper understanding of the adaptive backstepping design from a performance perspective via an intuitive method to select its otherwise abstract controller gains, on top of its traditional stability perspective. Such a similarity analysis opens the door for researchers to use well-established PID tuning methods to predict the performance of Lyapunov stability-based controllers. At the same time, the obtained formulation reveals how the corresponding PID control law can be linked to Lyapunov stability theory. The proposed scheme is applied to a quadrotor unmanned aerial vehicle (UAV) carrying a payload with the presence of a wind gust as an external disturbance, in simulation and experimentally. Copyright © 2021, The Authors. All rights reserved.

关键词： Proportional control systems

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Tunable Rectification of Diffusion-Wave Fields by Spatiotemporal Metamaterials

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Physical Review Letters 2022年第25期129卷 256601-256601页

作者： Jiaxin Li Zhanxiang Zhang Guoqiang Xu Haoran Sun Lizhou Dai Tianlong Li Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China

The diffusion process is the basis of many branches of science and engineering, and generally obeys reciprocity between two ports of a linear time-invariant medium. Recent research on classical wave dynamics has explored the spatiotemporal modulation to exhibit preferred directions in photons and plasmons. Here we report a distinct rectification effect on diffusion-wave fields by modulating the conductivity and observe nonreciprocal transport of charges. We experimentally create a spatiotemporal diffusion metamaterial, in which a mode transition to zero frequency is realized. A direct current component thereby emerges, showcasing a biased effect on the charge diffusion when the incident fundamental frequency is a multiple of the system modulation frequency. These results may find applications spanning a plethora of diffusive fields in general.

关键词： Diffusion Metamaterials Bloch-Floquet theorem

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Augmenting Efficient Real-time Surgical Instrument Segmentation in Video with Point Tracking and Segment Anything

arXiv

引用

arXiv 2024年

作者： Wu, Zijian Schmidt, Adam Kazanzides, Peter Salcudean, Septimiu E. Robotics and Control Laboratory Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States

The Segment Anything Model (SAM) is a powerful vision foundation model that is revolutionizing the traditional paradigm of segmentation. Despite this, a reliance on prompting each frame and large computational cost limit its usage in robotically assisted surgery. Applications, such as augmented reality guidance, require little user intervention along with efficient inference to be usable clinically. In this study, we address these limitations by adopting lightweight SAM variants to meet the efficiency requirement and employing fine-tuning techniques to enhance their generalization in surgical scenes. Recent advancements in Tracking Any Point (TAP) have shown promising results in both accuracy and efficiency, particularly when points are occluded or leave the field of view. Inspired by this progress, we present a novel framework that combines an online point tracker with a lightweight SAM model that is fine-tuned for surgical instrument segmentation. Sparse points within the region of interest are tracked and used to prompt SAM throughout the video sequence, providing temporal consistency. The quantitative results surpass the state-of-the-art semi-supervised video object segmentation method XMem on the EndoVis 2015 dataset with 84.8 IoU and 91.0 Dice. Our method achieves promising performance that is comparable to XMem and transformer-based fully supervised segmentation methods on ex vivo UCL dVRK and in vivo CholecSeg8k datasets. In addition, the proposed method shows promising zero-shot generalization ability on the label-free STIR dataset. In terms of efficiency, we tested our method on a single GeForce RTX 4060/4090 GPU respectively, achieving an over 25/90 FPS inference speed. Code is available at: https://***/wuzijian1997/SIS-PT-SAM © 2024, CC BY.

关键词： Surgical equipment

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Graph-Based Visual-Kinematic Fusion and Monte Carlo Initialization for Fast-Deployable Cable-Driven Robots

Graph-Based Visual-Kinematic Fusion and Monte Carlo Initiali...

引用

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

作者： R. Khorrambakht H. Damirchi M.R. Dindarloo A. Saki S.A. Khalilpour Hamid D. Taghirad Stephan Weiss Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY School of Computer Science University of Adelaide Australia Faculty of Electrical Engineering University of Technology Tehran Iran Department of Smart Systems Technologies University of Klagenfurt Austria

Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework that presents a unified localization and calibration system which requires no initial values for the kinematic parameters. This is achieved by formulating a Monte-Carlo algorithm that initializes a factor-graph representation of the calibration and localization problem. With this, we are able to jointly identify both the kinematic parameters and the visual odometry scale alongside their corresponding uncertainties. We demonstrate the practical applicability of the framework using our state-estimation dataset recorded with the ARAS-CAM suspended cable driven parallel robot, and published as part of this manuscript.

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Dependency-aware Task Offloading and Resource Pricing in Vehicular Edge Computing: A Stackelberg Game Approach

Dependency-aware Task Offloading and Resource Pricing in Veh...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Liang Zhao Shuai Huang Yuxiang Cao Huan Zhou Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Vehicular Edge Computing (VEC) allows vehicles to offload their delay-sensitive tasks to nearby Road Side Units (RSUs) for processing, which improves network quality of service (QoS). However, the self-interested SDN controller is unwilling to ask RSUs to provide free computing resources for vehicles. At the same time, complicated dependencies between vehicular subtasks may cause non-ideal task delay and energy consumption. In order to solve these problems, this paper proposes a Stackelberg game-based Dependency-aware task Offloading and resource Pricing framework (SDOP). Specifically, we first model a vehicular edge network that partially offloads dependency-aware tasks. Then, we depict the interaction between the SDN controller and vehicles as a Stackelberg game, with the goal of maximizing the utility of both parties. Next, we present a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the problem. Finally, numerous simulations are performed, and the results show that compared with other baseline schemes, the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios.

关键词： Energy consumption Computational modeling Road side unit Process control Games Pricing Quality of service Delays Edge computing Genetic algorithms

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