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2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond, DroneCom 2020

作者： Nagarajan, Deepan Jayakody, Dushantha Nalin K. Balakrishnan, Rebekka Department of ECE National Institute of Technology Tiruchirappalli India School of Computer Science and Robotics National Research Tomsk Polytechnic University Russia Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Sri Lanka

ISBN: (纸本)9781450381055

Unmanned aerial vehicle (UAV) has been considered as a widespread technical solution in recent years to meet the explosive data and massive device connections demands. On the other hand, Backscatter communication (BackComm) also equally evolved as a potential candidate to realize future Internet-of-Things (IoT) networks. By deploying BackComm in UAV-enabled IoT system allows an efficient utilization of the network resources, especially, in remote areas and smart cities. In this paper, we investigate the performance of a UAVassisted multi-node BackComm network over generalized - shadowed fading channel. Closed-form expressions are derived to study the system performance through outage probability and average BER. Additionally, we obtain a simplified asymptotic expression in a high SNR regime, from which we gain insight into how the channel and system parameters affect the overall performance. Finally, simulation results are provided to validate the derived theoretical results. © 2020 Association for Computing Machinery.

关键词： Internet of things

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Grasping by Spiraling: Reproducing Elephant Movements with Rigid-Soft Robot Synergy

arXiv

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arXiv 2025年

作者： Huang, Huishi Wang, Haozhe Fang, Chongyu Yan, Mingge Xu, Ruochen Zhang, Yiyuan Wang, Zhanchi Ying, Fengkang Liu, Jun Laschi, Cecilia Ang, Marcelo H. Advanced Robotics Centre National University of Singapore 117608 Singapore Integrative Sciences and Engineering Programme National University of Singapore Graduate School 119077 Singapore School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China 138632 Singapore

—The logarithmic spiral is observed as a common pattern in several living beings across kingdoms and species. Some examples include fern shoots, prehensile tails, and soft limbs like octopus arms and elephant trunks. In the latter cases, spiraling is also used for grasping. Motivated by how this strategy simplifies behavior into kinematic primitives and combines them to develop smart grasping movements, this work focuses on the elephant trunk, which is more deeply investigated in the literature. We present a soft arm combined with a rigid robotic system to replicate elephant grasping capabilities based on the combination of a soft trunk with a solid body. In our system, the rigid arm ensures positioning and orientation, mimicking the role of the elephant’s head, while the soft manipulator reproduces trunk motion primitives of bending and twisting under proper actuation patterns. This synergy replicates 9 distinct elephant grasping strategies reported in the literature, accommodating objects of varying shapes and sizes. The synergistic interaction between the rigid and soft components of the system minimizes the control complexity while maintaining a high degree of adaptability. Copyright © 2025, The Authors. All rights reserved.

关键词： Robotic arms

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Removing EOG Artifacts from the Resting State EEG Signal of Methamphetamine Addicts by ICA Algorithms

Removing EOG Artifacts from the Resting State EEG Signal of ...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Gege Zhan Haolong Su Pengchao Wang Lan Niu Jianxiong Bin Wei Mu Xueze Zhang Haifeng Jiang Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Zhejiang Lab Research Center for Intelligent Sensing Hangzhou China

EEG signal contains a wealth of information about brain activity, but the recording process is inevitably contaminated by EOG artifacts. An effective method to remove EOG artifacts can provide a guarantee for subsequent EEG analysis. In this paper, we compare the performance of four ICA algorithms in removing EOG artifacts from EEG signals of methamphetamine addicts. From the perspective of time domain and power spectral density, all the four algorithms can effectively remove the EOG artifacts without obvious difference. In terms of PSNR, MI and processing speed, FastICA algorithm can achieve higher processing speed and reconstruct signals better than the other three algorithms.

关键词： Electrooculography Brain Electroencephalography Brain-computer interfaces Recording Time-domain analysis

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Effects of Input Neuron Mapping Coordinates in Spiking Neural Network on the Motor Imagery EEG Signals Classification

Effects of Input Neuron Mapping Coordinates in Spiking Neura...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Gege Zhan Haolong Su Pengchao Wang Lan Niu Jianxiong Bin Wei Mu Xueze Zhang Haifeng Jiang Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Chinese Academy of Sciences Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

Spiking neural network (SNN), the third generation of the artificial neural network, uses the same computational principles as Spatio-temporal brain data (STBD) generation, so it can be used as an effective tool to learn and understand STBD. When using SNN to process STBD, mapping of input neurons is a problem to be considered. This paper investigates the influence of changing the mapping coordinates of input neurons on motor imagery EEG signal classification. The results show that the coordinate change affects the interaction between the input neuron and its neighboring neurons, and causes the classification accuracy to change, but the impact mechanism is unknown.

关键词： Neurons Pattern classification Artificial neural networks Electroencephalography Brain-computer interfaces Biological neural networks

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When Vision Meets Touch: A Contemporary Review for Visuotactile Sensors from the Signal Processing Perspective

arXiv

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arXiv 2024年

作者： Li, Shoujie Wang, Zihan Wu, Changsheng Li, Xiang Luo, Shan Fang, Bin Sun, Fuchun Zhang, Xiao-Ping Ding, Wenbo Tsinghua Shenzhen International Graduate School Shenzhen518055 China RISC-V International Open Source Laboratory Shenzhen518055 China Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore Centre for Robotics Research King’s College London LondonWC2R 2LS United Kingdom Department of Automation Tsinghua University Beijing100084 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Tactile sensors, which provide information about the physical properties of objects, are an essential component of robotic systems. The visuotactile sensing technology with the merits of high resolution and low cost has facilitated the development of robotics from environment exploration to dexterous operation. Over the years, several reviews on visuotactile sensors for robots have been presented, but few of them discussed the significance of signal processing methods to visuotactile sensors. Apart from ingenious hardware design, the full potential of the sensory system toward designated tasks can only be released with the appropriate signal processing methods. Therefore, this paper provides a comprehensive review of visuotactile sensors from the perspective of signal processing methods and outlooks possible future research directions for visuotactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal processing

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Using Reinforcement Learning to Create Control Barrier Functions for Explicit Risk Mitigation in Adversarial Environments

Using Reinforcement Learning to Create Control Barrier Funct...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Edvards Scukins Petter Ögren Aeronautics Division SAAB Linköping Sweden Robotics Perception and Learning Lab. School of Electrical Engineering and Computer Science Royal Institute of Technology (KTH) Stockholm Sweden

Air Combat is a high-risk activity carried out by trained professionals operating sophisticated equipment. During this activity, a number of trade-offs have to be made, such as the balance between risk and efficiency. A policy that minimizes risk could have very low efficiency, and one that maximizes efficiency may involve very high *** this study, we use Reinforcement Learning (RL) to create Control Barrier Functions (CBF) that captures the current risk, in terms of worst-case future separation between the aircraft and an enemy missile. CBFs are usually designed manually as closed-form expressions, but for a complex system such as a guided missile, this is not possible. Instead, we solve an RL problem using high fidelity simulation models to find value functions with CBF properties, that can then be used to guarantee safety in real air combat situations. We also provide a theoretical analysis of what family of RL problems result in value functions that can be used as CBFs in this *** proposed approach allows the pilot in an air combat scenario to set the exposure level deemed acceptable and continuously monitor the risk related to his/her own safety. Given input regarding acceptable risk, the system limits the choices of the pilot to those that guarantee future satisfaction of the provided bound.

关键词： Missiles Atmospheric modeling Reinforcement learning Tools Mathematical models Safety Risk management

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Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction

Unlimited Neighborhood Interaction for Heterogeneous Traject...

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International Conference on computer Vision (ICCV)

作者： Fang Zheng Le Wang Sanping Zhou Wei Tang Zhenxing Niu Nanning Zheng Gang Hua School of Software Engineering Xi’an Jiaotong University Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University University of Illinois at Chicago School of Computer Science and Technology Xidian University Wormpex AI Research

ISBN: (纸本)9781665428132

Understanding complex social interactions among agents is a key challenge for trajectory prediction. Most existing methods consider the interactions between pairwise traffic agents or in a local area, while the nature of interactions is unlimited, involving an uncertain number of agents and non-local areas simultaneously. Besides, they treat heterogeneous traffic agents the same, namely those among agents of different categories, while neglecting people’s diverse reaction patterns toward traffic agents in different categories. To address these problems, we propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN), which predicts trajectories of heterogeneous agents in multiple categories. Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously, which is adaptive to any number of agents and any range of interaction area. Meanwhile, a hierarchical graph attention module is proposed to obtain category-to-category interaction and agent-to-agent interaction. Finally, parameters of a Gaussian Mixture Model are estimated for generating the future trajectories. Extensive experimental results on benchmark datasets demonstrate a significant performance improvement of our method over the state-of-the-art methods.

关键词： computer vision Benchmark testing Trajectory Gaussian mixture model

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Efficient distributed key agreement for edge devices in the internet of things with information-theoretic security 7

Efficient distributed key agreement for edge devices in the ...

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7th International Cryptology and Information Security Conference 2020, CRYPTOLOGY 2020

作者： Rauf, Abid Wang, Zhaohong Sajid, Hasan Ali Tahir, Muhammad Department of Computing School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan Department of Electrical and Computer Engineering California State University Chico United States Department of Robotics and Intelligent Machine Engineering National University of Sciences and Technology Islamabad Pakistan

The Internet of Things (IoT) has a broad application in various domains, from small scale smart homes to large scale smart grids and smart cities. As different devices equipped with the capability of connection to communication networks, the weakness of IoT edge devices becomes a vulnerability of the entire system. Adversaries try to infiltrate into the communication between IoT edge devices and then launch attacks on the bigger system. Besides, the IoT edge devices generally lack computational resources compared to traditional computers, making IoT edge devices inefficient in carrying out computational-intensive cryptographic tasks. Therefore, how to update keys efficiently becomes a challenge throughout the life span of those IoT edge devices. In this paper, we propose a secret key establishment scheme suitable for securing IoT comprised of heterogeneous edge devices. By forming closely located IoT edge devices into enclaves of devices, our proposed framework achieves distributed secret generation in an information-theoretic secure sense. Simulations validated our proposed design in terms of its effectiveness and run-time complexity. © 2020 ACM.

关键词： Internet of things

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Centrifugal Microfluidic Based Painless Blood Sample Collection for Hematocrit Measurement

Centrifugal Microfluidic Based Painless Blood Sample Collect...

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Biomedical Engineering International Conference (BMEiCON)

作者： Santi Rattanavarin Thanapat Sangkharat Ekachai Juntasaro Witsaroot Sripumkhai Pattaraluck Pattamang Norabadee Ranron Numfon Khemthongcharoen Chompunoot Sinthupibulyakit Chamras Promptmas Chayapitcha Saesue Jiraskul Udarwudhipong Wutthinan Jeamsaksiri Department of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok (KMUTNB) Bangkok Thailand Mechanical Engineering Simulation and Design Group (MESD) Sirindhorn International Thai-German Graduate School of Engineering (TGGS) King Mongkut’s University of Technology North Bangkok (KMUTNB) Bangkok Thailand Thai Microelectronics Center (TMEC) National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Chachoengsao Thailand Infrastructure Development Division (IDD) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand Faculty of Medical Technology Huachiew Chalermprakiet University Samut Prakan Thailand Department of Biomedical Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand

This work demonstrates a centrifugal microfluidic chip design for painless blood collection and hematocrit measurement. The fluid channel in the microfluidic chip was fabricated using a laser-cutting method. An optimal dimension of the negative pressure chamber was designed to control the maximum sample volume obtained for further hematocrit examination. The estimated blood sample of 20 µl could be obtained based on the negative pressure driven by finger press actuation on the negative pressure chamber of the microfluidic chip. The repeatability of blood volume collection evaluated by a laboratory technician was 21.126±0.001 µl with a coefficient of variation (CV) of 5.32%. The sample volume used in this centrifugal microfluidic chip was around three times lower than that used in the standard method. Pack red cell preparation for hematocrit examination in the microfluidic chip could be achieved using a centrifugation speed around 2.4 times lower than that used in the standard method. The hematocrit examination by our centrifugal microfluidic chip and the standard method was 41.75±0.77 (1.84 %CV), and 41.5±0.71 (1.7 %CV), respectively. The results showed that the hematocrit measurements in our centrifugal microfluidic chip were compatible with those measured by the standard method.

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Ridge polynomial neural network with error feedback for recursive multi-step forecast strategy: A case study of carbon dioxide emissions forecasting 2

Ridge polynomial neural network with error feedback for recu...

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2nd International Conference on computer and Information sciences, ICCIS 2020

作者： Waheeb, Waddah Shah, Habib Jabreel, Mohammed Puig, Domenec School of Computing Asia Pacific University of Technology Innovation Kuala Lumpur Bukit Jalil57000 Malaysia College of Computer Science King Khalid University Department of Computer Science Abha62529 Saudi Arabia IRCV - Intelligent Robotics and Computer Vision Group Universitat Rovira i Virgili Tarragona43007 Spain

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154671

Neural networks (NNs) have been used extensively for forecasting problems. NN with error feedbacks is a type of NNs that showed more accurate forecasts compared to feedforward NNs and NNs with output feedbacks with some forecasting problems. The main issue with NN s with error feedbacks appears when there is a need for recursive multi-step forecast strategy because the observed values must be known in order to calculate network errors. This paper proposes to use the last calculated error after finishing training NNs with error feedbacks because the observed values are unknown. This last calculated error is used as a fixed value when producing forecasts using recursive multi-step forecast strategy. For that, this paper investigated this simple solution with a NN with error feedback called the ridge polynomial neural network with error feedback (RPNN-EF). Carbon dioxide emissions for three countries in the organization of the petroleum exporting countries (OPEC) were used in this investigation. The forecasting accuracy of RPNN-EF was compared with seven forecasting methods. According to the obtained results, on average, the proposed solution produces reasonable forecasts compared to the seven forecasting methods. Therefore, this solution can be suggested for NNs with error feedbacks for recursive multi-step forecast strategy. © 2020 IEEE.

关键词： Polynomials

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