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arXiv 2017年

作者： Pablo-Pedro, Ricardo Lopez-Rios, Hector Mendoza-Cortes, Jose-L Kong, Jing Fomine, Serguei van Voorhis, Troy Dresselhaus, Mildred S. Department of Chemistry Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Instituto de Investigaciones en Materiales Universidad Nacional Autónoma de México Apartado Postal 70-360 CU Coyoacán Ciudad de México04510 Mexico Department of Chemical & Biomedical Engineering Florida A&M University Florida State University Joint College of Engineering Tallahassee FL32310 United States Scientific Computing Department Materials Science and Engineering Program High Performance Material Institute Condensed Matter Theory National High Magnetic Field Laboratory Florida State University TallahasseeFL32310 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

High-performance materials rely on small reorganization energies to facilitate both charge separation and charge transport. Here, we performed DFT calculations to predict small reorganization energies of rectangular silicene nanoclusters with hydrogen-passivated edges denoted by H-SiNC. We observe that across all geometries, H-SiNCs feature large electron affinities and highly stabilized anionic states, indicating their potential as n-type materials. Our findings suggest that fine-tuning the size of H-SiNCs along the "zigzag" and "armchair" directions may permit the design of novel n-type electronic materials and spinctronics devices that incorporate both high electron affinities and very low internal reorganization energies. Copyright © 2017, The Authors. All rights reserved.

关键词： Silicene

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Minimum variance beamformer applied to frequency components of the RF signal of the eye: Enhancing the image quality of curved interfaces

Minimum variance beamformer applied to frequency components ...

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IEEE Symposium (IUS) Ultrasonics

作者： Hirofumi Taki Alfred C. H. Yu Billy Y. S. Yiu Hiroshi Kanai Graduate School of Biomedical Engineering Tohoku University Sendai Japan Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Electrical and Electronic Engineering University of Hong Kong Pokfulam Hong Kong

ISBN: (纸本)9781467398985

Minimum variance beamformer has high potential in improving image quality through an adaptive apodization weight optimization approach, but its impact is conventionally limited to lateral resolution enhancements. Recently, we reported a high range resolution technique that applies minimum variance beamformer to the frequency components of the RF signal after a delay-and-sum process. The technique has high performance in the improvement of the range resolution when a target has flat interfaces, such as a longitudinal section of an artery. In the present study, we report further advance of this technique to enhance image quality of a normal swine eye whose anatomy is marked by multiple layers of curved interfaces. The reference signal is acquired by the coherent integration of the echoes from isolated interfaces to take account of the attenuation and the interface shape. The proposed technique succeeded to offer a sharp rendering of the cornea boundaries of the swine eye, where the center frequency of the transmit ultrasound pulse was 12 MHz. Computationally, it required 5 seconds using a laptop PC with a single CPU for the depiction of an image in the ROI that consists of 422,528 pixels. We reckon that, if the proposed technique is implemented using GPUs, high-quality images of the eye (with both lateral and range resolution enhancements) can be readily achieved in real time.

关键词： RF signals Ultrasonic imaging biomedical imaging Image resolution Attenuation Image quality

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Publisher Correction: Brain charts for the human lifespan

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Nature 2022年第7931期610卷 E6页

作者： R A I Bethlehem J Seidlitz S R White J W Vogel K M Anderson C Adamson S Adler G S Alexopoulos E Anagnostou A Areces-Gonzalez D E Astle B Auyeung M Ayub J Bae G Ball S Baron-Cohen R Beare S A Bedford V Benegal F Beyer J Blangero M Blesa Cábez J P Boardman M Borzage J F Bosch-Bayard N Bourke V D Calhoun M M Chakravarty C Chen C Chertavian G Chetelat Y S Chong J H Cole A Corvin M Costantino E Courchesne F Crivello V L Cropley J Crosbie N Crossley M Delarue R Delorme S Desrivieres G A Devenyi M A Di Biase R Dolan K A Donald G Donohoe K Dunlop A D Edwards J T Elison C T Ellis J A Elman L Eyler D A Fair E Feczko P C Fletcher P Fonagy C E Franz L Galan-Garcia A Gholipour J Giedd J H Gilmore D C Glahn I M Goodyer P E Grant N A Groenewold F M Gunning R E Gur R C Gur C F Hammill O Hansson T Hedden A Heinz R N Henson K Heuer J Hoare B Holla A J Holmes R Holt H Huang K Im J Ipser C R Jack A P Jackowski T Jia K A Johnson P B Jones D T Jones R S Kahn H Karlsson L Karlsson R Kawashima E A Kelley S Kern K W Kim M G Kitzbichler W S Kremen F Lalonde B Landeau S Lee J Lerch J D Lewis J Li W Liao C Liston M V Lombardo J Lv C Lynch T T Mallard M Marcelis R D Markello S R Mathias B Mazoyer P McGuire M J Meaney A Mechelli N Medic B Misic S E Morgan D Mothersill J Nigg M Q W Ong C Ortinau R Ossenkoppele M Ouyang L Palaniyappan L Paly P M Pan C Pantelis M M Park T Paus Z Pausova D Paz-Linares A Pichet Binette K Pierce X Qian J Qiu A Qiu A Raznahan T Rittman A Rodrigue C K Rollins R Romero-Garcia L Ronan M D Rosenberg D H Rowitch G A Salum T D Satterthwaite H L Schaare R J Schachar A P Schultz G Schumann M Schöll D Sharp R T Shinohara I Skoog C D Smyser R A Sperling D J Stein A Stolicyn J Suckling G Sullivan Y Taki B Thyreau R Toro N Traut K A Tsvetanov N B Turk-Browne J J Tuulari C Tzourio É Vachon-Presseau M J Valdes-Sosa P A Valdes-Sosa S L Valk T van Amelsvoort S N Vandekar L Vasung L W Victoria S Villeneuve A Villringer P E Vértes K Wagstyl Y S Wang S K Warfield V Warrier E Westman M L Westwater H C Whalley A V Witte N Yang B Yeo H Y Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Brain Mapping Unit Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Child and Adolescent Psychiatry and Behavioral Science The Children's Hospital of Philadelphia Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Lifespan Brain Institute The Children's Hospital of Philadelphia and Penn Medicine Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Psychiatry University of Cambridge Cambridge UK. MRC Biostatistics Unit University of Cambridge Cambridge UK. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. Lifespan Informatics & Neuroimaging Center University of Pennsylvania Philadelphia PA USA. Department of Psychology Yale University New Haven CT USA. Developmental Imaging Murdoch Children's Research Institute Melbourne Victoria Australia. Department of Medicine Monash University Melbourne Victoria Australia. UCL Great Ormond Street Institute for Child Health London UK. Weill Cornell Institute of Geriatric Psychiatry Department of Psychiatry Weill Cornell Medicine New York USA. Department of Pediatrics University of Toronto Toronto Canada. Holland Bloorview Kids Rehabilitation Hospital Toronto Canada. The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformation University of Electronic Science and Technology of China Chengdu China. University of Pinar del Río "Hermanos Saiz Montes de Oca" Pinar del Río Cuba. MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge UK. Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. Department of Psychology School of Philosophy Psychology and Language Sciences University of Edinburgh Edinburgh UK. Queen's

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Soft haptics using soft actuator and soft sensor

Soft haptics using soft actuator and soft sensor

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IEEE/RAS-EMBS International Conference on biomedical Robotics and Biomechatronics (BioRob)

作者： P.M. Khin J.H. Low W.W. Lee S.L. Kukreja H.L. Ren N.V. Thakor C.H. Yeow Department of Biomedical Engineering National University of Singapore Singapore Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Singapore Institute for Neurotechnology National University of Singapore Singapore Department of Biomedical Engineering Electrical and Computer Engineering and Neurology Johns Hopkins University United States Department of Electrical & Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9781509032884

In this paper, we presented fabric-based soft tactile actuator and soft sensor. The force characterization result indicates that the actuator is able to produce force up to about 2.20(±0.017)N, when it is supplied with 80kPa of pressurized air. Hence it is capable of producing sufficient amount of force, which surpasses the human's haptic perception threshold. The thin, sheet nature of the material creates lightweight actuator, which improves the payload-to-weight ratio. The pneumatic-based operation principle creates a safer human-machine interface. Thus, it eliminates possible occurrence of safety issues such as the danger of applying high voltages to user's skin in case of malfunction. Direct force coupling of the soft actuator with the sensor is established to enable transmission of force information from the sensor to the actuator. The test profile indicates that the actuator is able to produce similar force profile as that of the sensor. This opens up possibility of developing soft tactile sensors and actuators based gloves, which can be paired and applied in virtual-reality based training and rehabilitation programs. Superimposition of multiple soft actuators would create an array that provides shape and size specific haptic feedback.

关键词： Actuators Force Robot sensing systems Haptic interfaces Fabrics Skin

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A compliant modular robotic hand with fabric force sensor for multiple versatile grasping modes

A compliant modular robotic hand with fabric force sensor fo...

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IEEE/RAS-EMBS International Conference on biomedical Robotics and Biomechatronics (BioRob)

作者： J.H. Low W.W. Lee P.M. Khin S.L. Kukreja H.L. Ren N.V. Thakor C.H. Yeow Department of Biomedical Engineering National University of Singapore Singapore Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Singapore Institute for Neurotechnology National University of Singapore Singapore Department of Biomedical Engineering Electrical and Computer Engineering and Neurology Johns Hopkins University United States Department of Electrical & Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9781509032884

This paper presents the development of a compliant, modular, reconfigurable, and sensorized robotic hand with multiple grasping capabilities. Each finger consists of a soft pneumatic actuator with embedded fabric force sensor and a detachable casing. The casing has a through hole for housing the actuator and special connectors for attachment to other casings. One casing each from the thumb and finger parts has a protrusion for connecting both parts together via a screw tightening system. The through-hole design allows different grasping length to be achieved and the inflated pneumatic channel of the actuator locks it in place. The modular robotic hand is capable of various versatile grasping tasks by simply changing the bending direction of the actuator, the distance between the thumb and finger parts, the grasping length of the actuator, or attaching/detaching additional fingers to the hand. (1) Hook grasping with single finger, (2) pinching with pad opposition, (3) reverse grasping for holding a pipe-like object, (4) wrap grasping with palm opposition, as well as (5) picking up an object through its handle with one thumb and two or more fingers were illustrated. These studies show the capability of the compliant modular robotic hand in performing various types of grasping by simply using different configurations of the casings. The excellent payload-to-weight ratio of the robotic hand was demonstrated. Also, the fabric force sensor that was embedded in the soft actuator indicated the difference in grasping forces that were applied to different objects during hook grasping. The modular robotic hand has the potential to broaden or substitute the usage of existing robotic hands, especially in applications where soft versatile configurable grasping is desired.

关键词： Grasping Actuators Thumb Robot sensing systems Fabrics

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Effect of Types and Gap on Spark Plugs on Exhaust Emissions on the 110CC Double Wheel Vehicles and Its Dissemination Using Website

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Tinus Ginting Hotler Manurung Astro Julida Harahap Erianto Ongko H Hartono Dahlan Abdullah Cut Ita Erliana Kundharu Saddhono S Sriadhi Supriyono Akbar Iskandar A A Saleh I Ketut Sudarsana Department of Mechanical Engineering Akademi Teknologi Industri Immanuel Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Geography Education Universitas Prof Dr Hazairin SH Bengkulu Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia

Based on the formulation of the problem to be discussed by the writer is to know: the influence of 0.4 mm spark gap variation, and standard spark plug type, to CO and HC, the influence of 0.5 mm spark plug gap variation, and variation of platinum spark plug on CO and HC, influence of spark gap variation 0,6mm, and variation of iridium spark plug type to CO and HC emission Method used to obtain data needed to support the writer that is research method with experiment, literature study method or literature study The result of this research concludes that there is spark gap influence 0.4 mm, with variations of standard plug, platinum and iridium spark plugs against CO and HC emissions, where the lowest CO value is 0.27% and HC 98 ppm, 0.5 mm spark plug with lowest CO 0.15% and HC 46 ppm, 0.6 mm spark plug gap the lowest CO value of 0.19% and HC 24 ppm. The results of this research will be disseminated using the website

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Retraction: Effect of Types and Gap on Spark Plugs on Exhaust Emissions on the 110CC Double Wheel Vehicles and Its Dissemination Using Website (J. Phys.: Conf. Ser. 1114 012097)

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Journal of Physics: Conference Series 2018年第1期1114卷

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Shunt active power filter based on cascaded transformers coupled with three-phase bridge converters

Shunt active power filter based on cascaded transformers cou...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Gregory A. de Almeida Carlos Cursino B. Jacobina João Paulo R. A. Méllo Euzeli C. dos Santos Post-Graduate Program in Electrical Engineering - PPgEE - Copele Campina Grande PB Brazil Department of Electrical and Computer Engineering Indiana University - Purdue University (IUPUI) Indianapolis IN USA

ISBN: (纸本)9781467395519

This paper proposes a multilevel shunt active power filter (SAPF) to deal with either harmonic currents compensation or reactive power compensation. Such a device can reduce the harmonic distortion at the grid currents provided by non-linear loads located in stiff systems. The proposed SAPF is based on three-phase bridge (TPB) converters connected to cascaded single-phase transformers. The transformers arrangement permits the compensator to use a single dc-link unit which simplifies the control strategy and number of sensors. The multilevel waveforms are generated by using a suitable PWM strategy associated with the transformers turns ratio. Modularity and simple maintenance make the proposed SAPF an attractive solution compared to some conventional configurations. The model, PWM technique and control strategy, are presented as well as studies considering harmonic distortion and semiconductor losses estimation. Simulation and experimental results are presented in order to validate theoretical approaches.

关键词： Pulse width modulation Estimation Capacitors Harmonic analysis Switches Power system harmonics Reactive power

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Fog computing in medical internet-of-things: Architecture, implementation, and applications

arXiv

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arXiv 2017年

作者： Dubey, Harishchandra Monteiro, Admir Constant, Nicholas Abtahi, Mohammadreza Borthakur, Debanjan Mahler, Leslie Sun, Yan Yang, Qing Akbar, Umer Mankodiya, Kunal Department of Electrical Computer and Biomedical Engineering University of Rhode Island RI-02881 United States Department of Communicative Disorders University of Rhode Island RI-02881 United States Wearable Biosensing Lab University of Rhode Island RI-02881 United States Movement Disorders Program Rhode Island Hospital RI-02903 United States Center for Robust Speech Systems University of Texas DallasTX75080 United States

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex;2) The data, when communicated, are vulnerable to security and privacy issues;3) The communication of the continuously collected data is not only costly but also energy hungry;4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a serviceoriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the ove

关键词： Internet of things

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