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arXiv 2019年

作者： Hao, Yufei Biswas, Shantonu Hawkes, Elliot Wang, Tianmiao Zhu, Mengjia Wen, Li Visell, Yon School of Mechanical Engineering and Automation Beihang University Beijing China California NanoSystems Institute University of California Santa BarbaraCA United States Department of Mechanical Engineering University of California Santa BarbaraCA United States Media Arts and Technology Program Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States

—A key challenge in robotics is to create efficient methods for grasping objects with diverse shapes, sizes, poses, and properties. Grasping with hand-like end effectors often requires careful selection of hand orientation and finger placement. Here, we present a soft, fingerless gripper capable of efficiently generating multiple grasping modes. It is based on a soft, cylindrical accordion structure containing coupled, parallel fluidic channels. It is controlled via pressure supplied from a single fluidic port. Inflation opens the gripper orifice for enveloping an object, while deflation allows it to produce grasping forces. The interior is patterned with a gecko-like skin that increases friction, enabling the gripper to lift objects weighing up to 20 N. Our design ensures that fragile objects, such as eggs, can be safely handled, by virtue of a wall buckling mechanism. The gripper can integrate a lip that enables it to form a seal and, upon inflating, to generate suction for lifting objects with flat surfaces. The gripper may also be inflated to expand into an opening or orifice for grasping objects with handles or openings. We describe the design and fabrication of this device and present an analytical model of its behavior when operated from a single fluidic port. In experiments, we demonstrate its ability to grasp diverse objects, and show that its performance is well described by our model. Our findings show how a fingerless soft gripper can efficiently perform a variety of grasping operations. Such devices could improve the ability of robotic systems to meet applications in areas of great economic and societal importance. Copyright © 2019, The Authors. All rights reserved.

关键词： Adhesion

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EEG dataset classification using CNN method

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Journal of Physics: Conference Series 2020年第1期1456卷

作者： W-L Mao H I K Fathurrahman Y Lee T W Chang Department of Electrical Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu 64002 Taiwan Bachelor Program in Interdisciplinary Studies College of Future National Yunlin University of Science and Technology Department of Digital Media Design National Yunlin University of Science and Technology

This paper proposes a simple Convolutional Neural Network (CNN) program to classify epileptic seizure. The diagnosis of epileptic seizure involves the identification and different characteristic of the Electroencephalography (EEG) signal. As such, it needs a method for identifying and classifying epileptic seizure. Deep learning is part of a neural network that has the ability and pattern to identify and classify epileptic seizure. CNN has been demonstrated high performance on image classification and pattern detection. In this paper, we combine the continuous wavelet transform (CWT) and CNN to classify epileptic seizure. This experiment uses the wavelet transform to convert signal data of EEG to time-frequency domain images. The output of the wavelet transform is an image that will classify into five attributes. In this experiment, we develop a simple program that will compare with other CNN approach (AlexNet and GoogleNet). The results of this experiment are two kinds of data, accuracy, and loss. The resulting accuracy is 72.49%, and the loss is 0.576. This result has a better learning time than GoogleNet and smaller loss result than AlexNet.

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Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation

Coupling Rendering and Generative Adversarial Networks for A...

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OCEANS

作者： Albert Reed Isaac D. Gerg John D. McKay Daniel C. Brown David P. Williamsk Suren Jayasuriya School of Electrical Computer & Energy Engineering Arizona State University Tempe AZ USA School of Electrical Engineering and Computer Science Pennsylvania State University State College PA USA Applied Research Laboratory Pennsylvania State University State College PA USA Graduate Program in Acoustics Pennsylvania State University State College PA USA NATO STO Centre for Maritime Research & Experimentation La Spezia Italy School of Arts Media & Engineering Arizona State University Tempe AZ USA

There is a growing demand for large-scale Synthetic Aperture Sonar (SAS) datasets. This demand stems from data-driven applications such as Automatic Target Recognition (ATR) [1]-[3], segmentation [4] and oceanographic research of the seafloor, simulation for sensor prototype development and calibration [5], and even potential higher level tasks such as motion estimation [6] and micronavigation [7]. Unfortunately, the acquisition of SAS data is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest. This skew introduces a data imbalance problem wherein a dataset can have as much as a 1000-to-1 ratio of seafloor background to object-of-interest SAS image chips.

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Transfer properties of touch elicited waves: Effect of posture and contact conditions 7

Transfer properties of touch elicited waves: Effect of postu...

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7th IEEE World Haptics Conference, WHC 2017

作者： Schafer, Harald Wells, Zachary Shao, Yitian Visell, Yon Department of Electrical and Computer Engineering Media Arts and Technology Program California NanoSystems Institute University of California Santa BarbaraCA93106 United States

ISBN: (纸本)9781509014255

During haptic interaction with touched objects, contact with the skin elicits mechanical signals that propagate rapidly to distances removed from the location of contact. Prior research has shown that these touch elicited mechanical signals travel predominantly as waves, and during manual touch, reach distances spanning the entire hand. While the structure in these signals is well preserved across distance, it is unknown how the propagation of these signals from the contact locus to other areas of the hand or finger depend on posture or contact conditions. To address this, we measured empirical transfer functions from a contacting plate that stimulated the fingertip to other sites on the finger, and analyzed the dependence of the corresponding frequency domain transfer functions on location, posture, and contact forces. The results suggest that the frequency and spatial dependence of these transfer functions varied consistently, and encoded contact conditions and relative contact location. Greatest variation was observed in the low or high frequency ranges of vibrotactile sensation, and at large distances from the contact surface. In contrast, mechanical information in the frequency range from 100 to 200 Hz was well preserved at large distances, and across conditions, indicating that tactile information in this frequency range is transmitted to other finger locations in a manner that is relatively invariant to the conditions and location of touch contact. The results may refine current understanding of the biomechanics of cutaneous wave propagation in the hand, may hold implications for understanding information content in touch-related cutaneous waves, and could hold inform the design of tactile displays worn on the skin. © 2017 IEEE.

关键词： Location

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Author Correction: Disrupting cellular memory to overcome drug resistance

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Nature communications 2024年第1期15卷 4264页

作者： Guillaume Harmange Raúl A Reyes Hueros Dylan L Schaff Benjamin Emert Michael Saint-Antoine Laura C Kim Zijian Niu Shivani Nellore Mitchell E Fane Gretchen M Alicea Ashani T Weeraratna M Celeste Simon Abhyudai Singh Sydney M Shaffer Cellular and Molecular Biology Graduate Group Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Biochemistry and Molecular Biophysics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Bioengineering School of Engineering and Applied Sciences University of Pennsylvania Philadelphia PA USA. Division of Biology and Biological Engineering California Institute of Technology Pasadena CA 91125 USA. Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA. Abramson Family Cancer Research Institute Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Chemistry College of the Arts and Sciences University of Pennsylvania Philadelphia PA USA. Department of Physics College of the Arts and Sciences University of Pennsylvania Philadelphia PA USA. Department of Biology College of the Arts and Sciences University of Pennsylvania Philadelphia PA USA. The Wharton School University of Pennsylvania Philadelphia PA USA. Cancer Signaling and Microenvironment Research Program Fox Chase Cancer Center Philadelphia PA USA. Department of Biochemistry and Molecular Biology Johns Hopkins School of Public Health Baltimore MD USA. Sidney Kimmel Cancer Center Johns Hopkins School of Medicine Baltimore MD USA. Department of Cell and Developmental Biology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Bioengineering School of Engineering and Applied Sciences University of Pennsylvania Philadelphia PA USA. sydshaffer@***. Department of Pathology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. sydshaffer@***.

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Coupling rendering and generative adversarial networks for artificial SAS image generation

arXiv

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arXiv 2019年

作者： Reed, Albert Gerg, Isaac D. McKay, John D. Brown, Daniel C. Williams, David P. Jayasuriya, Suren School of Electrical Computer & Energy Engineering Arizona State University TempeAZ United States School of Arts Media & Engineering Arizona State University TempeAZ United States Applied Research Laboratory Pennsylvania State University State College PA United States Graduate Program in Acoustics Pennsylvania State University State College PA United States School of Electrical Engineering and Computer Science Pennsylvania State University State College PA United States NATO STO Centre for Maritime Research & Experimentation La Spezia Italy

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Hive: An interactive sculpture for musical expression

Hive: An interactive sculpture for musical expression

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International conference on New Interfaces for Musical Expression, NIME 2017

作者： Kıratlı, Şölen Cadambi, Akshay Visell, Yon Media Arts and Technology University of California Santa Barbara Santa BarbaraCA United States Media Arts and Technology Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA United States

In this paper we present HIVE (2016), a parametrically designed interactive sound sculpture with embedded multichannel digital audio which explores the intersection of sculptural form and musical instrument design. We examine sculpture as an integral part of music composition and performance, expanding the definition of musical instrument to include the gestalt of loudspeakers, architectural spaces, and material form. After examining some related works, we frame HIVE as an interactive sculpture for musical expression. We then describe our design and production process, which hinges on the relationship between sound, space, and sculptural form. Finally, we discuss the installation and its implications. © 2017 Steering Committee of the International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Musical instruments

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Telecommunication-wavelength lasing in er-doped gan multiple quantum wells at room temperature

Telecommunication-wavelength lasing in er-doped gan multiple...

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Advanced Solid State Lasers, ASSL 2018

作者： Ho, V.X. Al Tahtamouni, T.M. Wang, Y. Jiang, H.X. Lin, J.Y. Zavada, J.M. Vinh, N.Q. Department of Physics Center for Soft Matter and Biological Physics Virginia Tech BlacksburgVA24061 United States Materials Science and Technology Program College of Arts and Sciences Qatar University Doha2713 Qatar Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

We report the realization of room-temperature, stimulated-emission in Er-doped-GaN multiple-quantum-wells at the 1.5-µm. Structures were grown by MOCVD and lasing was confirmed by threshold-behaviors of emission-... 详细信息

ISBN: (纸本)9781943580484

关键词： Semiconductor quantum wells

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Pre-training of deep bidirectional protein sequence representations with structural information

arXiv

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arXiv 2019年

作者： Min, Seonwoo Park, Seunghyun Kim, Siwon Choi, Hyun-Soo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Clova AI Research NAVER Corp. Seongnam13561 Korea Republic of Department of Computer Science and Engineering Kangwon National University Chuncheon24341 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence ASRI INMC Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount of unlabeled data, and the representations were transferred to various downstream tasks. Most pre-training methods solely rely on language modeling and often exhibit limited performance. In this paper, we introduce a novel pre-training scheme called PLUS, which stands for Protein sequence representations Learned Using Structural information. PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same-family prediction. PLUS can be used to pre-train various model architectures. In this work, we use PLUS to pre-train a bidirectional recurrent neural network and refer to the resulting model as PLUS-RNN. Our experiment results demonstrate that PLUS-RNN outperforms other models of similar size solely pre-trained with the language modeling in six out of seven widely used protein biology tasks. Furthermore, we present the results from our qualitative interpretation analyses to illustrate the strengths of PLUS-RNN. PLUS provides a novel way to exploit evolutionary relationships among unlabeled proteins and is broadly applicable across a variety of protein biology tasks. We expect that the gap between the numbers of unlabeled and labeled proteins will continue to grow exponentially, and the proposed pre-training method will play a larger role. All the data and codes used in this study are available at https://***/mswzeus/PLUS. © 2019, CC BY-NC-ND.

关键词： Proteins

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Noninvasive assessment of jugular venous pressure via force-coupled single crystal ultrasound

Noninvasive assessment of jugular venous pressure via force-...

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作者： Singh, Robin Jaffe, Alex Frydman, Galit H. Najia, Mohamad Ali Toufic Wei, Zijun Yang, Jason Majmudar, Maulik D. Department of Mechanical Engineering Medical Electronic Device Realization Centre Institute of Medical Engineering and Science Massachusetts Institute of Technology United States Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Division of Comparative Medicine Department of Biological Engineering Massachusetts Institute of Technology United States Center for Surgery Innovation and Bioengineering Department of Surgery Massachusetts General Hospital United States Harvard-MIT Health Sciences and Technology United States Broad Institute of MIT and Harvard United States Department of Hematology/Oncology Boston Children's Hospital United States Department of Media Arts and Sciences Massachusetts Institute of Technology United States Healthcare Transformation Lab Division of Cardiology Department of Medicine Massachusetts General Hospital BostonMA02114-2696 United States

Objective: we have developed a handheld device for noninvasive quantitative assessment of jugular venous pressure (JVP). Methods: we used a single crystal ultrasound coupled to a force-sensing load cell to measure JVP based on the force necessary to collapse the internal jugular vein (IJV) walls. We used a gelatin-based model system of the IJV to test the ability of single crystal ultrasound to identify the IJV and verified the cross-sectional position and diameter of the vessels with conventional imaging ultrasound. We also tested our prototype device on healthy human volunteers. Results: experiments on model system demonstrated that vessel diameters determined with single crystal ultrasound were in close agreement with the diameters derived from conventional 2-D ultrasound. Proof-of-concept human experiments demonstrate that single crystal ultrasound can detect the IJV in basal and collapsed states, as compared to gold-standard sonography (insert stats). Assessment of JVP in human volunteers was physiologically consistent with and sensitive to postural changes (supine JVP 6.6 ± 2.4 mmHg;standing JVP 4.2 ± 1.9 mmHg (p © 1964-2012 IEEE.

关键词： Single crystals

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