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Highly compliant silicone 3D-printed dual mode soft robotic gripper

smart Materials & Methods 2024年第4期1卷

作者： Ovy S. M. Al Islam Gianni Stano Abhishek P. Singh Pawandeep S. Matharu Yuyang Song Umesh Gandhi Ronald Poropatich Balakrishnan Prabhakaran Yonas Tadesse a Humanoids Biorobotics and Smart Systems (HBS) Laboratory Mechanical Engineering Department The University of Texas at Dallas Richardson TX USA b Toyota Research Institute of North America 11 Ann Arbor USA c Center for Military Medicine Research University of Pittsburgh Medical Center Pittsburgh PA USA d Multimedia Lab Department of Computer Science The University of Texas at Dallas Richardson TX USA

ABSTRACTSoft robotic grippers are increasingly preferred for object manipulation due to their enhanced-grasping capabilities, safe interaction, and high flexibility. This research presents a modular approach to 3D printing of a soft gripper with three silicone fingers, shape memory alloy springs as actuators, and spring steel elements for returning the fingers to their original position. The novelty lies in the one-shot fabrication of the soft fingers using a custom-made material extrusion machine, eliminating the need for support structures. The 3D printing setup, combined with a cartesian pick and place robot, automates the fabrication process. Two gripper configurations, horizontal and vertical, were achieved by assembling the soft fingers in different holders. The silicone fingers (100 × 8.5 × 30 mm) were used to grasp small medical equipment, showing high flexibility and a bending angle of ~60° at 4 A and 6.5 V, mimicking human finger motion. Finite Element Method analysis was used to study the dynamic behaviour, deformation, and stress of the fingers. The average stiffness and elastic modulus were 276 N/m and 0.014 MPa, respectively. The grippers were attached to a 3D printed child-sized humanoid robot to demonstrate their capabilities, highlighting their potential in medical robotics.

关键词： Soft Gripper soft robot additive manufacturing modular robot Grasping SMA actuation/artificial muscle Silicone medical assistive robots

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Improving the Performance of Ambulance Emergency Service Using smart Health systems

Improving the Performance of Ambulance Emergency Service Usi...

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IEEE International Conference on Connected Health: Applications, systems and engineering Technologies (CHASE)

作者： Mohammad Abdeen Mohamed Hossam Ahmed Hafez Seliem Mustafa El-Nainay Tarek Rahil Sheltami Faculty of Computer Science and Information Systems Islamic University of Madinah Madinah Saudi Arabia School of Electrical Engineering & Computer Science University of Ottawa Ottawa Canada Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt AlAlamein International University Matrouh Egypt Interdisciplinary Research center of Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (纸本)9781665439664

smart health is a new paradigm that can significantly improve the healthcare systems. In smart health, novel sensing, computing and communication technologies are integrated in healthcare to improve the quality of service. In this paper, we use the smart health to improve the performance of ambulance service. In particular, we use the real-time traffic information and hospital waiting time to minimize the ambulance response time, ambulance travel time to hospitals, and waiting time at hospitals. Results indicate that the use of smart health improves the performance significantly especially with non-uniform hospital capacity and non-uniform traffic conditions.

关键词： Hospitals Shape Roads smart healthcare Urban areas Quality of service Emergency services

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PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

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Efficient and accurate neural field reconstruction using resistive memory

arXiv

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arXiv 2024年

作者： Yu, Yifei Wang, Shaocong Zhang, Woyu Zhang, Xinyuan Wu, Xiuzhe He, Yangu Yang, Jichang Zhang, Yue Lin, Ning Wang, Bo Chen, Xi Wang, Songqi Zhang, Xumeng Qi, Xiaojuan Wang, Zhongrui Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Hong Kong ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Hong Kong Institute of Mind The University of Hong Kong Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Replicating this capability in AI finds wide applications in medical imaging, AR/VR, and embodied AI, where input data is often sparse and computing resources are limited. However, traditional signal reconstruction methods on digital computers face both software and hardware challenges. On the software front, difficulties arise from storage inefficiencies in conventional explicit signal representation. Hardware obstacles include the von Neumann bottleneck, which limits data transfer between the CPU and memory, and the limitations of CMOS circuits in supporting parallel processing. We propose a systematic approach with software-hardware co-optimizations for signal reconstruction from sparse inputs. Software-wise, we employ neural field to implicitly represent signals via neural networks, which is further compressed using low-rank decomposition and structured pruning. Hardware-wise, we design a resistive memory-based computing-in-memory (CIM) platform, featuring a Gaussian Encoder (GE) and an MLP Processing Engine (PE). The GE harnesses the intrinsic stochasticity of resistive memory for efficient input encoding, while the PE achieves precise weight mapping through a Hardware-Aware Quantization (HAQ) circuit. We demonstrate the system’s efficacy on a 40nm 256Kb resistive memory-based in-memory computing macro, achieving 31.5×, 35.5×, and 47.2× energy efficiency improvements and 10.8×, 38.8×, and 6.2× parallelism improvements without compromising reconstruction quality in tasks like 3D CT sparse reconstruction, novel view synthesis, and novel view synthesis for dynamic scenes. This work advances the AI-driven signal restoration technology and paves the way for future efficient and robust medical AI and 3D vision applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal reconstruction

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Supporting a “Glocal” Energy Transition: from Local Energy Communities to Global Simulation Networks

Supporting a “Glocal” Energy Transition: from Local Energy...

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International Conference on smart Energy systems and Technologies (SEST)

作者： G. Benedetto A. Mazza E. Pons E. Bompard A. De Paola D. Thomas E. Kotsakis G. Fulli S. Vogel A. Monti S. Bruno G. Giannoccaro M. La Scala A. Bonfiglio M. Invernizzi M. Rossi F. De Caro D. Villacci Department of Energy “Galileo Ferraris” Politecnico di Torino & G-RTS Lab Turin Italy Smart Grid Interoperability Laboratory Joint Research Centre - European Commission Ispra Italy OPAL-RT Germany Aachen Germany Institute for Automation of Complex Power Systems RWTH Aachen University Aachen Germany Department of Electrical & Information Engineering Politecnico di Bari & LabZERO Bari Italy Naval Architecture Dep. Electrical Electronics & Telecom. Eng. Università di Genova Genoa Italy Department of Engineering Università del Sannio Benevento Italy Department of Industrial Engineering Università di Napoli “Federico II” Naples Italy

Managing a large share of non-dispatchable renewable energy sources requires new approaches, which have to be integrated with existing and well-established control systems currently used to guarantee power system operation. In this context, the number of energy communities is expected to increase, and hence their effective integration is fundamental. This paper aims at assessing the role and impact of energy communities on the operation of a transition power system, still having a share of active traditional power plants. Simulation models and real hardware have been included in the experiment, based on a live real-time co-simulation. The results highlight both the significant contribution that the methodology (i.e., geographically-distributed real-time co-simulation) can give in informing and driving energy transition policies and the important role that energy communities may have in supporting the transition towards completely de-carbonized power systems.

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Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data

arXiv

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arXiv 2022年

作者： Hu, Sheng Gao, Song Luo, Wei Wu, Liang Li, Tianqi Xu, Yongyang Zhang, Ziwei School of Geography and Information Engineering China University of Geosciences Wuhan430074 China Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities Shenzhen518061 China GeoDS Lab Department of Geography University of Wisconsin‐Madison MadisonWI United States Geography Department National University of Singapore Singapore Singapore School of Computer Science China University of Geosciences Wuhan430074 China National Engineering Research Center of Geographic Information System Wuhan430074 China

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management. Copyright © 2022, The Authors. All rights reserved.

关键词： Abstracting

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Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering 15th

Discovering Spatio-Temporal Patterns in Precision Agricultur...

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15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020

作者： Melgar-García, Laura Godinho, Maria Teresa Espada, Rita Gutiérrez-Avilés, David Brito, Isabel Sofia Martínez-Álvarez, Francisco Troncoso, Alicia Rubio-Escudero, Cristina Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Mathematical and Physical Sciences Polytechnic Institute of Beja Beja Portugal Center for Mathematics Fundamental Applications and Operations Research University of Lisboa Lisbon Portugal Associação dos Agricultores do Baixo Alentejo Beja Portugal Department of Engineering Polytechnic Institute of Beja Beja Portugal Instituto de Desenvolvimiento de Novas Tecnologias - Centre of Technology and Systems Lisbon Portugal Department of Computer Languages and Systems University of Seville Seville Spain

ISBN: (纸本)9783030578015

Agriculture has undergone some very important changes over the last few decades. The emergence and evolution of precision agriculture has allowed to move from the uniform site management to the site-specific management, with both economic and environmental advantages. However, to be implemented effectively, site-specific management requires within-field spatial variability to be well-known and characterized. In this paper, an algorithm that delineates within-field management zones in a maize plantation is introduced. The algorithm, based on triclustering, mines clusters from temporal remote sensing data. Data from maize crops in Alentejo, Portugal, have been used to assess the suitability of applying triclustering to discover patterns over time, that may eventually help farmers to improve their harvests. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Precision agriculture

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Towards an Interaction Architecture for the iCub Robot: Social Gaze Space Model Adaptation for Social Interaction 23

Towards an Interaction Architecture for the iCub Robot: Soci...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Shaghaghi, Sahand Aliasghari, Pourya Tripp, Bryan Anderson, Britt Dautenhahn, Kerstin Nehaniv, Chrystopher L. Social & Intelligent Robotics Research Lab University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Dept. of Systems Design Enginering University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Dept. of Electrical & Computer Engineering University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Dept. of Psychology University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Centre for Theoretical Neuroscience University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada

ISBN: (纸本)9798350373578

Gaze behaviour plays a crucial role in social interactions extending to Human-Robot Interactions. Humanoid robots equipped with anthropomorphic vision systems may be expected to employ natural gaze behaviour in interactions with humans. Here, we implemented a novel first-person human gaze state detection tool on the iCub robot and assessed its gaze detection accuracy. Using this tool, we then developed two social gaze interaction architectures for the iCub robot. The Social Gaze Space theory (SGS) of Jording and colleagues was the theoretical framework for building our first architecture, SGS Base (SGS-B). Our second architecture, SGS Interaction Architecture (SGS-IA), is the extended version of SGS Base that lets the robot take on different higher-level goals in an interaction, including being attentive to the interaction partner or attempting to direct their attention to an object. These architectures support dyadic social interactions involving initiating and responding to joint attention towards objects of interest, and are expected to allow the robot to interact in a more human-like manner in social interactions. The gaze detection accuracy of the iCub robot equipped with our choice of algorithms exceeded that of previously implemented methods. System validation trials confirmed that the robot detects the social gaze states correctly in the majority of the instances, allowing for behavioural control according to the Social Gaze Space theory. Validation tests presented in this article demonstrate the functionality of the SGS Interaction Architecture and also highlight the differences in personal interaction dynamics when the humanoid robot has no particular goals as opposed to when it has interactional goals appropriate to a teaching scenario. © 2024 IEEE.

关键词： Anthropomorphic robots

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Self-Adjusting Network Slicing for Dynamic Heterogeneous Task Offloading in UAV-Enabled Mobile Edge Computing

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Li, Xulong Mao, Wencan Xu, Xinyi Liu, Yaxi Zhang, Huifeng Huangfu, Wei Long, Keping University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan Zhejiang Lab Hangzhou311500 China

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has received wide attention for its ability to significantly reduce latency with flexible mobile aerial nodes. However, the existing works merely consider a fixed periodic network slicing and usually neglect the characterization of computing tasks in practical applications. Motivated by this, we envision a novel scenario where the dynamics and heterogeneity of users and tasks are considered in UAV-enabled MEC networks. We propose a novel self-adjusting two-timescale network slicing scheme based on the internal mechanism in deep reinforcement learning (DRL) to automatically determine whether and when network slicing needs to be reconstructed. Such a novel scheme balances reconstruction cost and system performance compared with traditional fixed schemes. We build an optimization to minimize total system cost for joint UAV trajectory planning and resource allocation at both network and user slice levels. To tackle this problem, we design a novel self-adjusting two-timescale proximal policy optimization (PPO) algorithm that utilizes the property of the critic network, referred to as SaTPPO. Experiment results manifest the effectiveness of the proposed SaTPPO, and it has a lower system cost than the traditional fixed schemes. Moreover, the proposed SaTPPO outperforms the state-of-the-art baselines concerning effectiveness and efficiency. © 2015 IEEE.

关键词： Micro air vehicle (MAV)

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Twist-angle and thickness-ratio tuning of plasmon polaritons in twisted bilayer van der Waals films

arXiv

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arXiv 2023年

作者： Wang, Chong Xie, Yuangang Ma, Junwei Hu, Guangwei Xing, Qiaoxia Huang, Shenyang Song, Chaoyu Wang, Fanjie Lei, Yuchen Zhang, Jiasheng Mu, Lei Zhang, Tan Huang, Yuan Qiu, Cheng-Wei Yao, Yugui Yan, Hugen School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Department of Physics Fudan University Shanghai200433 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China

Stacking bilayer structures is an efficient way to tune the topology of polaritons in in-plane anisotropic films, e.g., by leveraging the twist angle (TA). However, the effect of another geometric parameter, film thickness ratio (TR), on manipulating the plasmon topology in bilayers is elusive. Here, we fabricate bilayer structures of WTe2 films, which naturally host in-plane hyperbolic plasmons in the terahertz range. Plasmon topology is successfully modified by changing the TR and TA synergistically, manifested by the extinction spectra of unpatterned films and the polarization dependence of the plasmon intensity measured in skew ribbon arrays. Such TR- and TA-tunable topological transitions can be well explained based on the effective sheet optical conductivity by adding up those of the two films. Our study demonstrates TR as another degree of freedom for the manipulation of plasmonic topology in nanophotonics, exhibiting promising applications in bio-sensing, heat transfer and the enhancement of spontaneous emission. © 2023, CC BY.

关键词： Polariton

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