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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Ananya Tandri Keshuai Xu May W. Chen Emad Boctor Jeeun Kang Dept. Biomedical Engineering Johns Hopkins University Baltimore MD USA Dept. Computer Science Johns Hopkins University Baltimore MD USA Dept. Pediatrics Johns Hopkins University Baltimore MD USA Lab. for Computational Sensing & Robotics Johns Hopkins University Baltimore MD USA Dept. Anesthesiology and Critical Care Medicine Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Perinatal arterial ischemic stroke (PAIS) is a form of focal vascular neurological injury that occurs during or shortly after birth. We recently proposed a modular PA helmet (PAH) system, in which multiple modules constitutes a flexible helmet to accommodate any neonatal head shape and size to provide volumetric monitoring of neonatal brain oxygen saturation. Here, optical tracking of the PAH configuration enables spatial image reconstruction. Given the large freedom in PAH configuration, it is critical to understand the implications of various design parameters in imaging quality. In this paper, we present our simulation-aided design approach using acoustic and optical simulation tools. Our design criteria are to optimize 1) signal contrast and 2) spatial resolution with different number of modules and their mean distances, and is further challenged with modular position error inevitable in modular position recognition. Results of the simulation established an acceptable error threshold for optical tracking and informed module prototype configuration and shape. The prototype based on the optimal design specifications is also demonstrated.

关键词： Pediatrics Head Shape Prototypes Optical saturation Optical variables control Optical imaging Adaptive optics Acoustics Safety

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Qudit Dynamical Decoupling on a Superconducting Quantum Processor

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Physical Review Letters 2025年第5期134卷 050601-050601页

作者： Vinay Tripathi Noah Goss Arian Vezvaee Long B. Nguyen Irfan Siddiqi Daniel A. Lidar Department of Physics and Astronomy University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science and Technology University of Southern California Los Angeles California 90089 USA Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Math and Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA Department of Chemistry University of Southern California Los Angeles California 90089 USA

Multilevel qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decoherence than qubits due to increased loss channels, noise sensitivity, and crosstalk. To address these challenges, we develop protocols for dynamical decoupling (DD) of qudit systems based on the Heisenberg-Weyl group. We implement and experimentally verify these DD protocols on a superconducting transmon processor that supports qudit operation based on qutrits (d=3) and ququarts (d=4). Specifically, we demonstrate single-qudit DD sequences to decouple qutrits and ququarts from system-bath-induced decoherence. We also introduce two-qudit DD sequences designed to suppress the detrimental cross-Kerr couplings between coupled qudits. This allows us to demonstrate a significant improvement in the fidelity of time-evolved qutrit Bell states. Our results highlight the utility of leveraging DD to enable scalable qudit-based quantum computing.

关键词： Qubits

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Electrochemical impedance behavior of implantable neural interface following the change of equivalent circuit element 12

Electrochemical impedance behavior of implantable neural int...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Zhang, Jing Liu, Lusheng Wang, Aiping Wang, Junkongshuai Fang, Tao Niu, Lan Bin, Jianxiong Wang, Pengchao Mu, Wei Jia, Jie Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The simulation of two basic equivalent circuit models of the implantable neural interface is described in this paper. The simulated equivalent circuits are created based on the physicochemical mechanism of charge transfer on the surface of the implantable neural interface. Each parameter of the equivalent circuit model was varied to investigate the regular pattern of individual element affecting the performance of the implantable neural interface. In this paper, one circuit model is based on the charge transfer process, and another circuit model is based on the mixed charge transfer and diffusion process. The equivalent circuits are split into two parts. The neural interface of the implantable neural interface device is simulated in one part, and the rest of that is simulated in another part. The electrochemical impedance data is acquired firstly by simulating the equivalent circuit with Z-view software. Then, the simulated data was analyzed to investigate the change tendency of a single parameter, providing a theoretical method for developing customized implantable neural interface with specific electrochemical impedance. © 2022 IEEE.

关键词： Equivalent circuits

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Experimental Validation of Sensitivity-Aware Trajectory Planning for a Quadrotor UAV Under Parametric Uncertainty

Experimental Validation of Sensitivity-Aware Trajectory Plan...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Ali Srour Salvatore Marcellini Tommaso Belvedere Marco Cognetti Antonio Franchi Paolo Robuffo Giordano CNRS Univ Rennes Inria IRISA Rennes Cedex France Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy LAAS-CNRS Université de Toulouse CNRS UPS Toulouse France LAAS-CNRS Robotics and Mechatronics Department Electrical Engineering Mathematics Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

In this work, we provide an experimental vali-dation of the recent concepts of closed-loop state and input sensitivity in the context of robust flight control for a quadrotor (UAV) equipped with the popular PX4 1 1 https://***/ controller. Our objective is to experimentally assess how the optimization of the reference trajectory w.r.t. these sensitivity metrics can improve the closed-loop system performance against model uncertainties commonly affecting the quadrotor systems. To accomplish this, we present a series of experiments designed to validate our optimization approach on two distinct trajectories, with the primary aim of assessing its precision in guiding the quadrotor through the center of a window at relatively high speeds. This approach provides some interesting insights for increasing the closed-loop robustness of the robot state and inputs against physical parametric uncertainties that may degrade the system's performance.

关键词： Uncertainty Sensitivity Target tracking Trajectory planning Perturbation methods Autonomous aerial vehicles Robustness

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Multi-Exposure Image Fusion Using Edge-Aware Network

Multi-Exposure Image Fusion Using Edge-Aware Network

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International Conference on Emerging Technologies, ICET

作者： Ghazala Aslam Muhammad Imran Bushra Haq Anayat Ullah Ersin Elbasi Department of Computer Science BUITEMS Quetta Pakistan Department of Electrical Engineering FICT BUITEMS Quetta Pakistan Control Automotive and Robotics Lab NCRA Rawalpindi Pakistan Department of Computer Science FICT BUITEMS Quetta Pakistan Department of Electronic Engineering FICT BUITEMS Quetta Pakistan College of Engineering & Technology American University of Middle East Egaila Kuwait

ISBN: (纸本)9781665459938

The natural dynamic range is much broader than the dynamic range a digital camera can capture. As a result, the images captured by ordinary cameras suffer from visibility issues; some images look overly bright, while others look overly dark. Therefore, in the research article, we propose a solution for image fusion, where multiple images can be combined to produce a single comprehensive image with better perceptibly. To achieve that, we designed a feature extraction block, which can extract features of an image at different levels and combines them to get more refined and detailed feature maps. In addition to that, we also designed a custom layer to extract edges, and those edges later are combined with the feature maps to give a fused image. We evaluated and compared our proposed architecture with some of the latest architecture, designed for the same reason, in terms of Image Structure Similarity and Human Perception-Based metrics. Experimental results corroborate that the proposed scheme outperforms the contemporary techniques for image fusion.

关键词： Image edge detection Lighting Deep architecture Dynamic range Feature extraction Digital cameras Image fusion

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Exploring Humanoid Robot Face Preference Using Brain Functional Connectivity and Graph Neural Network 12

Exploring Humanoid Robot Face Preference Using Brain Functio...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Moe Frontiers Center for Brain Science Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Huashan Hospital Fudan University Department of Rehabilitation Medicine Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Graph neural networks (GNN) have been applied in EEG signal analysis. However, it is not clear how to describe the connection relationships between electrodes, and a reasonable representation of the adjacency matrix cannot be neglected for the study of GNN based on EEG signals. In this paper, we use brain functional connectivity to analyze the face preferences of a humanoid robot and explore the feasibility of using the matrix obtained based on brain functional connectivity measurements as a GNN connectivity matrix. The results show that it is feasible and an average accuracy of 73.47% is achieved. In addition, the results of the brain functional connectivity analysis also show that there is a difference between preferences and non-preferences. © 2022 IEEE.

关键词： Anthropomorphic robots

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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A TB-CSP Motor Imagery EEG Recognition Method for Neural-controlled Manipulator System 12

A TB-CSP Motor Imagery EEG Recognition Method for Neural-con...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Mu, Wei Wang, Pengchao Wang, Junkongshuai Fang, Tao Wang, Aiping Niu, Lan Bin, Jianxiong Liu, Lusheng Zhang, Jing Jia, Jie Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

To develop a manipulator system based on spontaneous EEG signal control, this paper proposes a common spatial pattern method combined with the Bhattacharyya distance of time-frequency signal (TB-CSP) for EEG signal feature extraction. Firstly, for different subjects, the original EEG signal is divided by a specific time window and different frequency band;Secondly, the average Bhattacharyya distance of different frequency bands in a specific time window is calculated to select the optimal time-frequency signal and input it to CSP for feature extraction;Finally, the extracted features are input into SVM and LDA to classify and complete the control of the manipulator. The proposed method achieves 82.6% accuracy on the public dataset BCI IVa and 79.48% accuracy on the self-collected dataset. © 2022 IEEE.

关键词： Feature extraction

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Service Humanoid robotics: Review and Design of a Novel Bionic-Companionship Framework 8th

Service Humanoid Robotics: Review and Design of a Novel Bion...

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8th International Conference on Robot Intelligence Technology and Applications, RiTA 2020

作者： Yang, Jiaji Chew, Esyin Liu, Pengcheng EUREKA Robotics Lab Cardiff School of Technologies Cardiff Metropolitan University CardiffC5 2YB United Kingdom The Department of Computer Science University of York YorkYO10 5GH United Kingdom

ISBN: (纸本)9789811648021

At present, industrial robotics focused more on motion control and vision;whereas Humanoid Service robotics (HSRs) are increasingly being investigated among researchers’ and practitioners’ field of speech interactions. The problematic and quality of human-robot interaction (HRI) has become one of the hot potatoes concerned in academia. This paper proposes a novel interactive framework suitable for HSRs. The proposed framework is grounded on the novel integration of Trevarthen [24 Companionship Theory and neural image generation algorithm in computer vision. By integrating the image-to-natural interactivities generation, and communicate with the environment to better interact with the stakeholder, thereby changing from interaction to a bionic-companionship. In addition, the article also reviews the research of neural image generation algorithms and summarizes the application cases of the algorithm structure in the field of robotics from a critical perspective. We believe that the new interactive bionic-companionship framework can enable HSRs to further develop towards robot companions. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Human-robot interaction Humanoid robotics Social robotics

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Evaluating Return on Investment for Cyberinfrastructure Using the International Integrated Reporting Framework

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SN computer science 2024年第5期5卷 558页

作者： Snapp-Childs, Winona G. Hart, David L. Costa, Claudia M. Wernert, Julie A. Jankowski, Harmony E. Towns, John W. Stewart, Craig A. Pervasive Technology Institute Indiana University 2709 E. Tenth St. Bloomington 47408 IN United States Research Technologies University Information Technology Services Indiana University 2709 E. Tenth St. Bloomington 47408 IN United States Computational and Information Systems Lab National Center for Atmospheric Research Boulder 803807 CO United States National Center for Supercomputing Applications University of Illinois 1205 W. Clark St. Urbana 61801 IL United States Department of Computer Science Indiana University 700 N. Woodlawn Ave. Bloomington 47408 IN United States

This paper investigates the return on investment (ROI) in cyberinfrastructure (CI) facilities and services by comparing the value of end products created to the cost of operations. We assessed the cost of a US CI facility called XSEDE and the value of the end products created using this facility, categorizing end products according to the International Integrated Reporting Framework. The US federal government invested approximately $0.3B in operating the XSEDE ecosystem from 2016–2022. The estimated value of end products facilitated by XSEDE ranges from around $4.7B to $22.7B or more. Credit for the majority of these end products is shared among various contributors, including the XSEDE ecosystem. Granting the XSEDE ecosystem a seemingly reasonable percentage of credit for its contributions to end product creation suggests that the return on federal investment in the XSEDE ecosystem, in terms of value of end products created, was greater than one and possibly far greater than one. The Framework proved useful for addressing this question. Earlier work showed that the value of services provided by XSEDE was significantly greater than the cost of those services to the US federal government—a positive return on investment for delivery of services. Analyzing the financial efficiency of operations and the financial value of end products are two means for assessing the success of CI facilities in financial terms. Financial analyses should be used as one of many approaches for evaluating the success of CI facilities. © The Author(s) 2024.

关键词： Cyberinfrastructure IR framework Return on investment ROI science gateways XSEDE

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