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Control and Notification Automatic Water Pump with Arduino and SMS Gateway

IOP Conference Series: Materials science and engineering 2018年第1期407卷

作者： R Ratnadewi H Nurdiyanto A Najmurrokhman C Prabowo R Idmayanti H Eteruddin C A Sugianto N Kurniasih H K Siburian D Nababan R Rahim Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Department of Informatics STMIK Dharma Wacana Metro Lampung Indonesia Department of Electrical Engineering Universitas Jenderal Achmad Yani Cimahi Indonesia Department of Information Technology Politeknik Negeri Padang Padang Indonesia Department of Electrical Universitas Lancang Kuning Pekanbaru Indonesia Departement of Informatics Politeknik TEDC Bandung Bandung Indonesia Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Informatics Sekolah Tinggi Manajemen Informatika dan Komputer Medan Indonesia Department of Information System Universitas Pelita Harapan Medan Medan Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia

This paper aims to facilitate the owner to control water pump machine from the vacuum, and the condition of the water is it full or not in house or apartment. The current control system still has many limitations in controlling the water pump machine at home or apartment. The water machine control system was designed with device that has a smart surveillance system that can be accessed with Android smartphone and all information from or to machine control system will command by using SMS, this device create a development of Arduino UNO microcontroller technology that allows the reading of the GSM Module and connected with SMS Gateway. Experiment results facilitate real-time water control and much easy for the owner to control water tank.

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Laser annealing heals radiation damage in avalanche photodiodes

arXiv

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arXiv 2017年

作者： Lim, Jin Gyu Anisimova, Elena Higgins, Brendon L. Bourgoin, Jean-Philippe Jennewein, Thomas Makarov, Vadim Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Quantum Information Science Program Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada

Avalanche photodiodes (APDs) are a practical option for space-based quantum communications requiring single-photon detection. However, radiation damage to APDs significantly increases their dark count rates and reduces their useful lifetimes in orbit. We show that high-power laser annealing of irradiated APDs of three different models (Excelitas C30902SH, Excelitas SLiK, and Laser Components SAP500S2) heals the radiation damage and substantially restores low dark count rates. Of nine samples, the maximum dark count rate reduction factor varies between 5.3 and 758 when operating at −80 ◦C. The illumination power to reach these reduction factors ranges from 0.8 to 1.6 W. Other photon detection characteristics, such as photon detection efficiency, timing jitter, and afterpulsing probability, remain mostly unaffected. These results herald a promising method to extend the lifetime of a quantum satellite equipped with APDs. Copyright © 2017, The Authors. All rights reserved.

关键词： Photons

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Brains as Naturally Emerging Turing Machines

Brains as Naturally Emerging Turing Machines

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International Joint Conference on Neural Networks

作者： Juyang Weng Department of Computer Science and Engineering Cognitive Science Program and Neuroscience Program Michigan State University East Lansing Michigan 48824 USA

ISBN: (纸本)9781479919611

It has been shown that a Developmental Network (DN) can learn any Finite Automaton (FA) [29] but FA is not a general purpose automaton by itself. This theoretical paper presents that the controller of any Turing Machine (TM) is equivalent to an FA. It further models a motivation-free brain--excluding motivation e.g., emotions -- as a TM inside a grounded DN -- DN with the real world. Unlike a traditional TM, the TM-in-DN uses natural encoding of input and output and uses emergent internal representations. In Artificial Intelligence (AI) there are two major schools, symbolism and connectionism. The theoretical result here implies that the connectionist school is at least as powerful as the symbolic school also in terms of the general-purpose nature of TM. Furthermore, any TM simulated by the DN is grounded and uses natural encoding so that the DN autonomously learns any TM directly from natural world without a need for a human to encode its input and output. This opens the door for the DN to fully autonomously learn any TM, from a human teacher, reading a book, or real world events. The motivated version of DN [31] further enables a DN to go beyond action-supervised learning -- so as to learn based on pain-avoidance, pleasure seeking, and novelty seeking [31].

关键词： FA TM DN dwarf novae thulium Automata theory-Turing machines FAS gene Cerebrum

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Cluster adaptive training for deep neural network 40

Cluster adaptive training for deep neural network

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Tan, Tian Qian, Yanmin Yin, Maofan Zhuang, Yimeng Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781467369978

Although context-dependent DNN-HMM systems have achieved significant improvements over GMM-HMM systems, there still exists big performance degradation if the acoustic condition of the test data mismatches that of the training data. Hence, adaptation and adaptive training of DNN are of great research interest. Previous works mainly focus on adapting the parameters of a single DNN by regularized or selective fine-tuning, applying linear transforms to feature or hidden-layer output, or introducing vector representation of non-speech variability into the input. These methods all require relatively large number of parameters to be estimated during adaptation. In contrast, this paper employs the cluster adaptive training (CAT) framework for DNN adaptation. Here, multiple DNNs are constructed to form the bases of a canonical parametric space. During adaptation, an interpolation vector, specific to a particular acoustic condition, is used to combine the multiple DNN bases into a single adapted DNN. The DNN bases can also be constructed at layer level for more flexibility. The CAT-DNN approach was evaluated on an English switchboard task in unsupervised adaptation mode. It achieved significant WER reductions over the unadapted DNN-HMM, relative 6% to 8.5%, with only 10 parameters. © 2015 IEEE.

关键词： Adaptation Cluster Adaptive Training Deep Neural Network

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Colloidal Surfaces with Boundaries, Apex Boojums, and Nested Elastic Self-Assembly of Nematic Colloids

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Physical Review Letters 2016年第27期117卷 277801-277801页

作者： Sungoh Park Qingkun Liu Ivan I. Smalyukh Department of Physics University of Colorado Boulder Colorado 80309 USA Department of Electrical Computer and Energy Engineering Soft Materials Research Center and Materials Science and Engineering Program University of Colorado Boulder Colorado 80309 USA Renewable and Sustainable Energy Institute University of Colorado and National Renewable Energy Laboratory Boulder Colorado 80309 USA

Self-assembly of colloidal particles is poised to become a powerful composite material fabrication technique, but remains challenged by a limited control over the ensuing structures. We develop a new breed of nematic colloids that are physical analogs of a mathematical surface with boundary, interacting with the molecular alignment field without inducing defects when flat. However, made from a thin nanofoil, they can be shaped to prompt formation of self-compensating defects that drive preprogramed elastic interactions mediated by the nematic host. To show this, we wrap the nanofoil on all triangular side faces of a pyramid, except its square base. The ensuing pyramidal cones induce point defects with fractional hedgehog charges of opposite signs, spontaneously align with respect to the far-field director to form elastic dipoles and nested assemblies with tunable spacing. Nanofoils shaped into octahedrons interact as elastic quadrupoles. Our findings may drive realization of low-symmetry colloidal phases.

关键词： Defects Point defects Self-assembly Colloids Liquid crystals Nematic liquid crystals

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Recurrent neural network language model with structured word embeddings for speech recognition 40

Recurrent neural network language model with structured word...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： He, Tianxing Xiang, Xu Qian, Yanmin Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781467369978

Due to effective word context encoding and long-term context preserving, recurrent neural network language model (RNNLM) has attracted great interest by showing better performance over back-off n-gram models and feed-forward neural network language models (FNNLM). However, it still has the difficulty of modelling words of very low frequency in training data. To address this issue, a new framework of structured word embedding is introduced to RNNLM, where both input and target word embeddings are factorized into weighted sum of the corresponding sub-word embeddings. The framework is instantiated for Chinese, where characters can be naturally used as the sub-word units. Experiments on a Chinese twitter LVCSR task showed that the proposed approach effectively outperformed the standard RNNLM, yielding a relative PPL improvement of 8:8% and an absolute 0:59% CER improvement in N-Best re-scoring. © 2015 IEEE.

关键词： Language Model Recurrent Neural Network Speech Recognition Word Embeddings

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A study on fitness function with parameter-free genetic algorithm for non-destructive estimation of permittivity distribution based on capacitance and electric potential

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IEEJ Transactions on Fundamentals and Materials 2016年第4期136卷 164-173页

作者： Ohchi, Masashi Tomiyama, Kazuyoshi Furukawa, Tatsuya Itoh, Hideaki Fukumoto, Hisao Department of Electrical Electronics and Computer Engineering Faculty of Engineering Chiba Institute of Technology 2-17-1 Tudanuma Narashino275-0016 Japan Master's Program in Electrical Electronics and Computer Engineering Graduate School of Engineering Chiba Institute of Technology 2-17-1 Tudanuma Narashino275-0016 Japan Graduate School of Science and Engineering Saga University 1 Honjo-machi Saga840-8502 Japan

We have carried out experimental and numerical analyses for reconstruction of permittivity distribution using a pair of long distance small size electrodes and additional arc electrodes. A reconstruction method for permittivity distribution that uses a genetic algorithm and numerical analysis (finite element method) based on measured capacitance has been proposed and investigated. However, the conventional method had a problem in accuracy. In this paper, the reconstruction method has been improved by using a Parameter-free Genetic Algorithm(PfGA) based on measured capacitance and electric potential. In addition, we have evaluated the fitness function with PfGA. © 2016 The Institute of Electrical Engineers of Japan.

关键词： Finite element method

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A CMOS-compatible Large-Scale Monolithic Integration of Heterogeneous Multi-Sensors on Flexible Silicon for IoT Applications

A CMOS-compatible Large-Scale Monolithic Integration of Hete...

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IEEE International Electron Devices Meeting

作者： Joanna M. Nassar Galo A. Torres Sevilla Seneca J. Velling Marlon D. Cordero Muhammad Mustafa Hussain Integrated Nanotechnology Lab and Integrated Disruptive Electronic Applications (IDEA) Lab Computer Electrical Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Material & Nanoscience Program Faculty of Science University of Waterloo Waterloo ON N2L-3G1 Canada

ISBN: (纸本)9781509039036

We report CMOS technology enabled fabrication and system level integration of flexible bulk silicon (100) based multi-sensors platform which can simultaneously sense pressure, temperature, strain and humidity under various physical deformations. We also show an advanced wearable version for body vital monitoring which can enable advanced healthcare for IoT applications.

关键词： CMOS-compatible Large-Scale Flexible Silicon

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Information-theoretic segmentation of natural language 3

Information-theoretic segmentation of natural language

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3rd International Workshop on Artificial Intelligence and Cognition, AIC 2015

作者： Griffiths, Sascha S. McGinity, Mariano Mora Forth, Jamie Purver, Matthew Wiggins, Geraint A. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London 10 Godward Square LondonE1 4FZ United Kingdom

We present computational experiments on language segmentation using a general information-theoretic cognitive model. We present a method which uses the statistical regularities of language to segment a continuous stream of symbols into "meaningful units" at a range of levels. Given a string of symbols-in the present approach, textual representations of phonemes-we attempt to find the syllables such as grea and sy (in the word greasy);words such as in, greasy, wash, and water;and phrases such as in greasy wash water. The approach is entirely information-theoretic, and requires no knowledge of the units themselves;it is thus assumed to require only general cognitive abilities, and has previously been applied to music. We tested our approach on two spoken language corpora, and we discuss our results in the context of learning as a statistical processes.

关键词： Information theory

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