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International Multiconference of Engineers and computer Scientists 2016, IMECS 2016

作者： Rao, Kovvali Manasa Ravichandran, Durga Mahesh, Kavi Department of Environmental Engineering Arizona State University TempeAS United States Data Science Program University of Southern California Los AngelesCA United States KAnOE Department of Computer Science PES University India

ISBN: (纸本)9789881925381

Non-intrusive load monitoring (NILM) is a critical problem in creating consumer awareness about electrical power consumption in households with the objective of eliminating wastage and reducing overall energy consumption in a society. Many approaches have been explored in non-intrusive load monitoring in recent times with a focus on machine learning of appliance-level details and user activity from analysis of aggregate power consumption. Several technologies and devices have also been developed to collect power consumption data and perform analysis on voltage and current waveforms. However, not much work has been done in analyzing NILM data against demographic parameters such as time of day or day of week as well as occupancy data of households. This article presents a novel approach to identifying active devices and predicting future power usage using demographic data in addition to aggregate power usage over time. We find that SVM (Support Vector Machine) with Edge Analysis works best for device identification and ARMA (Autoregressive Moving Average Model)works best for predicting future power consumption using time series forecasting. We have also collected real aggregate power consumption data from a typical household in India and produced perhaps the first such dataset.

关键词： Forecasting

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Publisher's Note: "Chemical vapor deposition of monolayer MoS2directly on ultrathin Al2O3for low-power electronics" [Appl. Phys. Lett. 110, 053101 (2016)]

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Applied Physics Letters 2017年第9期110卷

作者： Bergeron, Hadallia Sangwan, Vinod K. McMorrow, Julian J. Campbell, Gavin P. Balla, Itamar Liu, Xiaolong Bedzyk, Michael J. Marks, Tobin J. Hersam, Mark C. Department of Materials Science and Engineering Northwestern University EvanstonIllinois60208 United States Applied Physics Graduate Program Northwestern University EvanstonIllinois60208 United States Department of Physics and Astronomy Northwestern University EvanstonIllinois60208 United States Department of Chemistry Northwestern University EvanstonIllinois60208 United States Department of Electrical Engineering and Computer Science Northwestern University EvanstonIllinois60208 United States

This article was originally published online on 30 January 2017. Due to a production error, outdated versions of Figures 1 and 4 appeared in the article as orig

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Automated Incline Detection for Assistive Powered Wheelchairs

Automated Incline Detection for Assistive Powered Wheelchair...

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IEEE International Symposium on Robot and Human Interactive Communication

作者： Mahdieh Nejati Brenna D. Argall McCormick School of Engineering Master of Science in Robotics Program Northwestern University Evanston IL USA Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA

ISBN: (纸本)9781509039302

This work presents an algorithm for automated real-time ramp detection using 3D point cloud data in the context of shared-control powered wheelchairs. Limitations in the interfaces available to those with severe motor impairments can make basic maneuvering tasks with powered wheelchairs difficult. Although a significant amount of work has been done on obstacle detection and avoidance, much less attention has been given to algorithms for the safe and reliable detection of ramps and inclines;even though navigating these structures is an important part of urban life. We provide an algorithmic solution for accurately detecting traversable inclines for applications with powered wheelchairs using the Point Cloud Library (PCL) within the robotics Operating System (ROS) framework. All algorithms are implemented first in simulation and later evaluated on data obtained from indoor and outdoor urban environments. We measure the performance of our algorithm with systematic testing on several different ramp structures, observed from varied viewpoints. Results show that our algorithm is successful in detecting the orientation, slope, and width of traversable ramps with up to 100% accuracy and an average detection accuracy of 88%.

关键词： Wheelchairs Mobile robots Three-dimensional displays Robot sensing systems Navigation Image edge detection three-dimensional displays Manipulator sensing systems Edge detection Wheelchair Mobile robots navigation RAMP Inclined Ramps physical impairment algorithms Town life Obstacle detection Traversable urban environment

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Valley-selective optical Stark effect probed by Kerr rotation

arXiv

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arXiv 2017年

作者： LaMountain, Trevor Bergeron, Hadallia Balla, Itamar Stanev, Teodor K. Hersam, Mark C. Stern, Nathaniel P. Applied Physics Program Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL60208 United States

The ability to monitor and control distinct states is at the heart of emerging quantum technologies. The valley pseudospin in transition metal dichalcogenide (TMDC) monolayers is a promising degree of freedom for such control, with the optical Stark effect allowing for valley-selective manipulation of energy levels in WS2 and WSe2 using ultrafast optical pulses. Despite these advances, understanding of valley-sensitive optical Stark shifts in TMDCs has been limited by reflectance-based detection methods where the signal is small and prone to background effects. More sensitive polarization-based spectroscopy is required to better probe ultrafast Stark shifts for all-optical manipulation of valley energy levels. Here, we show time-resolved Kerr rotation to be a more sensitive probe of the valley-selective optical Stark effect in monolayer TMDCs. Compared to the established time-resolved reflectance methods, Kerr rotation is less sensitive to background effects. Kerr rotation provides a five-fold improvement in the signal-to-noise ratio of the Stark effect optical signal and a more precise estimate of the energy shift. This increased sensitivity allows for observation of an optical Stark shift in monolayer MoS2 that exhibits both valley- and energy-selectivity, demonstrating the promise of this method for investigating this effect in other layered materials and heterostructures. Copyright © 2017, The Authors. All rights reserved.

关键词： Landforms

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Composite skyrmion bags in two-dimensional materials

arXiv

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arXiv 2018年

作者： Foster, David Kind, Charles Ackerman, Paul J. Tai, Jung-Shen B. Dennis, Mark R. Smalyukh, Ivan I. H. H. Wills Physics Laboratory University of Bristol BristolBS8 1TL United Kingdom School of Mathematics University of Bristol BristolBS8 1TW United Kingdom Department of Physics and Soft Materials Research Center University of Colorado BoulderCO80309 United States School of Physics and Astronomy University of Birmingham BirminghamB12 2TT United Kingdom Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program University of Colorado BoulderCO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory and University of Colorado BoulderCO80309 United States

Skyrmions are particle-like topological excitations, studied in various condensed matter systems and models of high-energy physics (HEP). They occur as stable spin textures in certain planar magnetic materials [1-4] and as configurations in chiral nematic liquid crystals [5], having been originally proposed as model of atomic nuclei [10]. Since magnetic Skyrmions can be accelerated with a current [6-8], they have the potential to encode bits in low-power magnetic storage devices [9]. Drawing on techniques from HEP, we demonstrate that magnetic and liquid crystal Skyrmions interact like orientation dependent, localised particles, explaining previously observed Skyrmion behaviour. This interaction motivates the construction of Skyrmion bags: Textures of high topological degree which we realise experimentally in liquid crystals, and in magnetic materials by computer simulations. These Skyrmion bags configurations are nested multiple Skyrmions, which act like single Skyrmions in pairwise interaction, and under the inuence of a current in magnetic materials. These results emphasize equivalent Skyrmion behaviour in different physical systems, and suggest new, high-density magnetic memory storage devices based on Skyrmion bags. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic storage

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Multi-modality in gene regulatory networks with slow promoter kinetics

arXiv

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arXiv 2017年

作者： Al-Radhawi, M. Ali Vecchio, Domitilla Del Sontag, Eduardo D. Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139-4307 United States Departments of Bioengineering and of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Harvard Program in Therapeutic Science Harvard Medical School BostonMA02115 United States

Phenotypical variability in the absence of genetic variation often reflects complex energetic landscapes associated with underlying gene regulatory networks (GRNs). In this view, different phenotypes are associated with alternative states of complex nonlinear systems: stable attractors in deterministic models or modes of stationary distributions in stochastic descriptions. We provide theoretical and practical characterizations of these landscapes, specifically focusing on stochastic slow promoter kinetics, a time scale relevant when transcription factor binding and unbinding are affected by epigenetic processes like DNA methylation and chromatin remodeling. In this case, largely unexplored except for numerical simulations, adiabatic approximations of promoter kinetics are not appropriate. In contrast to the existing literature, we provide rigorous analytic characterizations of multiple modes. A general formal approach gives insight into the influence of parameters and the prediction of how changes in GRN wiring, for example through mutations or artificial interventions, impact the possible number, location, and likelihood of alternative states. We adapt tools from the mathematical field of singular perturbation theory to represent stationary distributions of Chemical Master Equations for GRNs as mixtures of Poisson distributions and obtain explicit formulas for the locations and probabilities of metastable states as a function of the parameters describing the system. As illustrations, the theory is used to tease out the role of cooperative binding in stochastic models in comparison to deterministic models, and applications are given to various model systems, such as toggle switches in isolation or in communicating populations, a synthetic oscillator and a trans-differentiation network. Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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Hybrid cryptosystem RSA – CRT optimization and VMPC

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Journal of Physics: Conference Series 2018年第1期978卷

作者： R Rahmadani H Mawengkang Sutarman Master Program (S2) of Informatics Engineering Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia. Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia.

Hybrid cryptosystem combines symmetric algorithms and asymmetric algorithms. This combination utilizes speeds on encryption/decryption processes of symmetric algorithms and asymmetric algorithms to secure symmetric keys. In this paper we propose hybrid cryptosystem that combine symmetric algorithms VMPC and asymmetric algorithms RSA – CRT optimization. RSA – CRT optimization speeds up the decryption process by obtaining plaintext with dp and p key only, so there is no need to perform CRT processes. The VMPC algorithm is more efficient in software implementation and reduces known weaknesses in RC4 key generation. The results show hybrid cryptosystem RSA – CRT optimization and VMPC is faster than hybrid cryptosystem RSA – VMPC and hybrid cryptosystem RSA – CRT – VMPC. Keyword : Cryptography, RSA, RSA – CRT, VMPC, Hybrid Cryptosystem.

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Hierarchical Graph-Based Discovery of Non-Primitive-Shaped Objects in Unstructured Environments

Hierarchical Graph-Based Discovery of Non-Primitive-Shaped O...

引用

IEEE International Conference on robotics and Automation

作者： Christian A. Mueller Andreas Birk Robotics Group of the Computer Science & Electrical Engineering Department Jacobs University Bremen Germany

ISBN: (纸本)9781467380270

We present a hierarchical graph-based approach for unknown object discovery in RGB-D point clouds captured with a Kinect-like sensor from unstructured scenes. A two-step approach is proposed which first extracts meaningful regions from an input scene through over-segmentation. Secondly, a procedure is introduced to detect compositions of such regions that can represent primitive-shaped object candidates like boxes or cylinders. Complex-shaped objects are interpreted as a composition of primitive-shaped objects, for instance, a teddy bear can consist of two convex-shaped arms, legs, a convex-shaped head and torso. An ensemble of classifiers is trained to learn patterns from the appearances of such neighboring primitive shapes that constitute complex-shaped objects. Therein the appearance is described by a set of features from the texture and geometry domain. For the experiments, a dataset was prepared which is publicly available, containing a set of scenes which consists of 296 human-annotated object instances in total. Experiments show that the proposed hierarchical approach is capable to extract meaningful regions: an under-segmentation rate of 2.6% has been achieved. Furthermore, objects are segmented with a segmentation rate of 92.9% which reflects the capability of our approach to detect potential object candidates within unstructured scenes.

关键词： Discovery Non-Primitive-Shaped Objects

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Optofluidic Systems: Miniaturized, Battery-Free Optofluidic Systems with Potential for Wireless Pharmacology and Optogenetics (Small 4/2018)

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Small 2018年第4期14卷

作者： Kyung Nim Noh Sung Il Park Raza Qazi Zhanan Zou Aaron D. Mickle Jose G. Grajales-Reyes Kyung-In Jang Robert W. Gereau IV Jianliang Xiao John A. Rogers Jae-Woong Jeong Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Center for Remote Health Science Technologies Texas A&M University College Station TX 77843 USA Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder CO 80309 USA Department of Mechanical Engineering University of Colorado Boulder Boulder CO 80309 USA Department of Anesthesiology Washington University Pain Center Washington University School of Medicine St. Louis MO 63130 USA Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Republic of Korea Department of Neuroscience Washington University Pain Center Washington University School of Medicine St. Louis MO 63130 USA Department of Materials Science and Engineering Biomedical Engineering Neurological Surgery Chemistry Mechanical Engineering Electrical and Computer Science Northwestern University Evanston IL 60208 USA Center for Bio-Integrated Electronics Simpson Querrey Institute for BioNanotechnology Northwestern University Evanston IL 60208 USA School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea

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