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IEEE Conference on Electron Devices and Solid-State Circuits

作者： Wen-Kuan Yeh Wenqi Zhang Chia-Hung Shih Yi-Lin Yang Department of Electrical Engineering National University of Kaohsiung Kaohsiung Taiwan Bachelor Program in Robotics National Pingtung University Taiwan Department of Electronic Engineering National Kaohsiung Normal University Kaohsiung Taiwan

ISBN: (纸本)9781509018314

In this work, the effect of fin width on device performance and reliability for high-k/metal tri-gate n/p-type FinFETs was investigated including electrical characteristic clarification and simulation. Carrier conduction in the trapezoidal shape Si-fin body of FinFETs is different for devices with different fin bottom widths (W Fin _ bottom ), which will affect device properties. For n-type FinFETs, the experimental results show that thinner W Fin_bottom device performs better reliability under HCI stress due to higher inversion carrier density at the center of Si-fin channel. For p-type FinFETs under negative bias stressing, thinner W Fin_bottom device shows more serious degradation with the increasing of stressing voltage due to larger electric field within the Si-fin and higher energy of inversion holes, while the thicker W Fin_bottom device shows almost insensitively degradation with the variation of stressing voltage.

关键词： FinFETs Reliability Degradation Logic gates Negative bias temperature instability Thermal variables control

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Propositional Temporal Logic for planning in an embedded Concurrent Autonomous Agent

Propositional Temporal Logic for planning in an embedded Con...

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2015 IEEE International Conference on robotics and Automation, ICRA 2015

作者： Stéfano, Diego Ferreira, F. Martins, Priscila S. Conceição, André Gustavo S. Da Costa, Augusto Loureiro Robotics Laboratory Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Bahia40210-630 Brazil

ISBN: (纸本)9781479969234

In this paper, a Propositional Temporal Logic (PTL) inference engine is implemented in the cognitive level of a Concurrent Autonomous Agent (CAA). The hardware architecture of the system is an embedded microcontrollers network designed to fit the concurrency requirements of the cognitive model of the aforementioned agent. The architecture of the agent comprises three levels, namely, the reactive level, instinctive level and the cognitive level, which runs concurrently. The reactive level is embedded in a PSoC 5LP, consisting of behaviours created over a embedded kinematic controller. The instinctive level runs in an ARM mbed, which receives perceptions from and sends the active behaviour to the reactive level through a CAN bus. This level uses a Knowledge Based System (KBS) with First Order Logic (FOL) as automatic reasoning mechanism. The cognitive level runs an implementation of the METATEM algorithm on a DNP/2486 which, in turn, receives symbolic information from the instinctive level to update its logical world model. It also sends new local goals to instinctive level through an Ethernet network. Finally, the architecture used in the experiments is presented, followed by the results. © 2015 IEEE.

关键词： Temporal logic

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Real-time contamination modeling for robotic health care support

Real-time contamination modeling for robotic health care sup...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Kory Kraft Tiffany Chu Patrick Hansen William D. Smart Robotics Program Oregon State University Corvallis 97331 USA Department of Computer Science at California State University Stanislaus Turlock 95382 USA Department of Computer Science at the University of Notre Dame IN 46556 USA

ISBN: (纸本)9781509037636

Real-time contamination monitoring in health care facilities would allow medical teams to take appropriate actions to carefully enter, avoid, or decontaminate contaminated areas, reducing infection risk for themselves and their patients. In this paper, we demonstrate and evaluate the first end-to-end, real-time contamination tracking system for robotic health care support. The system models contamination of the environment and people, directs decontamination efforts of a simulated scrubber robot, and alerts users when nearing contaminated areas. We outline our transmission model design choices, as based on Ebola virus disease, and evaluate the system against the spread of a physical substance.

关键词： Contamination Robots Diseases Real-time systems Atmospheric modeling Adaptation models Atmospheric models Pollutant real-time systems Robot Health Care maladjustment Adaptation models health care facility infection risk Public Health Environmental pollution

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A nonparametric belief solution to the Bayes tree

A nonparametric belief solution to the Bayes tree

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Dehann Fourie John Leonard Michael Kaess MIT/WHOI Joint Program Cambridge 02139 USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge 02139 USA Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 USA

ISBN: (纸本)9781509037636

We relax parametric inference to a nonparametric representation towards more general solutions on factor graphs. We use the Bayes tree factorization to maximally exploit structure in the joint posterior thereby minimizing computation. We use kernel density estimation to represent a wider class of constraint beliefs, which naturally encapsulates multi-hypothesis and non-Gaussian inference. A variety of new uncertainty models can now be directly applied in the factor graph, and have the solver recover a potentially multi-modal posterior. For example, data association for loop closure proposals can be incorporated at inference time without further modifications to the factor graph. Our implementation of the presented algorithm is written entirely in the Julia language, exploiting high performance parallel computing. We show a larger scale use case with the well known Victoria park mapping and localization data set inferring over uncertain loop closures.

关键词： Belief propagation Kernel Simultaneous localization and mapping Bayes methods Optimization Estimation

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Influence of slot geometry on air gap magnetic flux density of rotating machines

Influence of slot geometry on air gap magnetic flux density ...

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IEEE International Conference on Environment and Electrical Engineering

作者： Rodrigo Alves de Lima A. C. Paulo Coimbra Tony Almeida Viviane Margarida Gomes Thiago Martins Pereira Aylton José Alves Wesley Pacheco Calixto Program in Electronic Systems and Automation PGEA Department of Electrical Engineering ENE University of Brasilia UnB Institute of Systems and Robotics ISR University of Coimbra UC Experimental & Technological Research and Study Group NExT Federal Institute of Goias IFG

ISBN: (纸本)9781509023219

The objective of this work is to investigate the influence of slotted air gap constructive parameters on magnetic flux density of rotating machines. For this purpose, different approaches were used to solve the air gap field diagram using finite element method and the magnetic field distribution uniformity was evaluated by Carter's factor calculation. Sensitivity analysis of slot constructive parameters was performed and results show that slot geometry modifies the magnetic flux on air gap and shifts the air gap magnetic equipotential midline of double slotted machines.

关键词： Carter's factor finite elements method rotating machines

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Optimization of fish-like locomotion using hierarchical reinforcement learning

Optimization of fish-like locomotion using hierarchical rein...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Jeonghyeon Wang Jinwhan Kim Robotics Program Ocean Robotics & Intelligence (ORIN) Laboratory Department of Mechanical Engineering KAIST Daejeon Korea

With an interest in advanced marine propulsion systems, much research has been done on mimicking fish-like locomotion using flapping fins. This study aims to optimize the swimming pattern of fish-like locomotion based on hierarchical reinforcement learning. A simplified carangiform fish model is employed and a segmented tail motion is learned by Q-learning to maximize the average forward velocity by flapping the tail fin. The performance of the self-learned swimming pattern is verified and analyzed in terms of the flapping efficiency. The results show that the flapping angle limit of approximately 35 degrees is best in maximizing the forward moving velocity and the hierarchical reinforcement learning approach is effective in providing a reasonable solution for a large-scale problem.

关键词： Learning (artificial intelligence) Learning systems Mathematical model Markov processes Robots Propulsion

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Reaction torque minimization method for keeping drilling direction of the hybrid rotary steerable system

Reaction torque minimization method for keeping drilling dir...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Jongheon Kim Sungwook Jung Suyoung Choi Hyun Myung Dept. of Civil and Environmental Engineering Urban Robotics Lab. Daejeon Korea Robotics Program Urban Robotics Lab. Daejeon Korea

The directional drilling system is one of the key technologies for developing unconventional resources such as shale gas and coal bed methane (CBM). There are several types of the directional drilling system such as the whipstock, bent-sub, and the rotary steerable system (RSS). Among these systems, the RSS is most advanced system. In this paper, the hybrid type RSS is developed and its experiment results of drilling cement blocks will be introduced. The experiment had a problem due to the reaction torque of the drilling system. A method of minimizing the effect of the reaction torque is proposed to solve this problem.

关键词： Torque Rotation measurement Bismuth

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Underwater Visual SLAM with Loop-Closure using Image-to-Image Link Recovery

Underwater Visual SLAM with Loop-Closure using Image-to-Imag...

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OCEANS

作者： Seonghun Hong Taeyun Kim Jinwhan Kim Robotics Program Department of Mechanical Engineering KAIST Daejeon Korea

ISBN: (纸本)9781479987375

This study develops an underwater visual simultaneous localization and mapping (SLAM) algorithm using a monocular vision as a major measurement sensor, focusing in particular on the loop-closure problem. Although most vision-based loop-closure approaches have been implemented by feature-based pairwise image matching, matching underwater images is generally a challenging task due to the limited number of feature correspondences or relatively small overlapping regions between the compared images. The lack of loop-closing events caused by these challenges can degrade the navigation and mapping performance of visual SLAM. For robust visual SLAM, a loop-closure algorithm that can recover image-to-image matching links is presented. The proposed loop-closure algorithm is more resilient to failures of pairwise matching and thus can maximize the use of image-to-image links, thereby improving the estimation performance in the context of visual SLAM. To validate the effectiveness of the proposed algorithm, a hover-capable unmanned underwater vehicle was used for in-water experiments, and the proposed algorithm was also evaluated through a series of comparative results drawn from an experimental dataset.

关键词： Underwater visual SLAM Loop-closure Link recovery

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BLISS: Using Robot in Learning Intervention to Promote Social Skills for Autism Therapy 2016

BLISS: Using Robot in Learning Intervention to Promote Socia...

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Proceedings of the international Convention on Rehabilitation Engineering & Assistive Technology

作者： Settapon Santatiwongchai Boonserm Kaewkamnerdpong Wisanu Jutharee Kajornvut Ounjai Department of Computer Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Biological Engineering Program Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Institute of Field Robotics King Mongkut's University of Technology Thonburi Bangkok Thailand

Robots have been introduced for autism therapy. However, most robotics studies focused on providing means of practice with dyadic interaction activities. To live in the society, autistic children need social interaction skills. In this study, we developed a mobile robot with non-threatening facial expression and investigated the potential of using this robot in triadic interaction activity for autism therapy to promote social interaction in children with autism. From the experiment that subjects play a picture matching game, it can be indicated that the robot can attract children with autism. Subjects can learn and understand the rule with their parents' participation. They could learn the name of the object in the picture that they do not know before as well. Some parents found that the robot could aid them in training their children. Autism specialists discovered some capabilities that they have never observed from the subjects in their therapeutic sessions. Therefore, this robot could be useful for autism specialists in finding aptitudes of children with autism in order to provide suitable intervention for effective therapy. This robot could be helpful for parents/caregivers in conducting therapeutic activities at home (continuing after therapeutic session with specialists) for even more effective therapy.

关键词： Mobile Robot Autism Non-Humanoid Robot Social Interaction

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