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Frontiers in Optics, FiO_2019

作者： Li, Weijian Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Electrical and Computer Engineering Rice University TX77005 United States

We demonstrate a large photo-refractive effect in 1T-TaS2 in the visible at low intensity white light excitation. By using this optical tunability, we demonstrate tunable meta-gratings for nanophotonics applications. ... 详细信息

ISBN: (纸本)9781943580675

关键词： Finite difference time domain Light intensity Nanophotonics Photorefractive materials Thin film optical properties X ray diffraction

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Hybrid Beam Forming with Data Logging Scheduling(DLS) for wireless sensor network(WSN)

Hybrid Beam Forming with Data Logging Scheduling(DLS) for wi...

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2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Renu, Sakthidasan Yen, Lei Belay, Abebe Lin, Hsin-Piao Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taiwan Department of Electronic Engineering National Taipei University of Technology Taiwan Department of Electrical Engineering National Dong Hwa University Taipei Taiwan

ISBN: (纸本)9781509048977

Improve of the overall network efficiency between source and destination relay selection and interference free communication is important criteria in WSN. In this paper we are proposing SON based algorithm capable of simultaneous Scheduling and Routing in WSN. The proposes scheme contains the three stages, in first stage the sensor nodes are classified into different clusters and each nodes having different weight based on distance from base station. In second stage the IRSS (Improvised Received Signal Strength) based scheme used select Routing path based on the Node weight, Residual Energy and Probability of Link Loss values of Relay Sensors. On the other side the channel estimation algorithm executed simultaneously with previous two stages. In final we are implementing the Data Logging based Scheduling and Routing scheme (DLSR) for WSN with Energy and Maximized Throughput. We are applying path recovery algorithm in presence of Void path or node failures. The proposed scheme additionally will form the virtual relay path to make communication with nearly zero loss. © 2017 IEEE.

关键词： Scheduling

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Geometric Methods for Robust Data Analysis in High Dimension

arXiv

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arXiv 2017年

作者： Anderson, Joseph Timothy Graduate Program in Department of Computer Science and Engineering The Ohio State University

Data-driven applications are growing. Machine learning and data analysis now finds both scientific and industrial application in biology, chemistry, geology, medicine, and physics. These applications rely on large quantities of data gathered from automated sensors and user input. Furthermore, the dimensionality of many datasets is extreme: more details are being gathered about single user interactions or sensor readings. All of these applications encounter problems with a common theme: use observed data to make inferences about the world. Our work obtains the first provably efficient algorithms for Independent Component Analysis (ICA) in the presence of heavy-tailed data. The main tool in this result is the centroid body (a well-known topic in convex geometry), along with optimization and random walks for sampling from a convex body. This is the first algorithmic use of the centroid body and it is of independent theoretical interest, since it effectively replaces the estimation of covariance from samples, and is more generally accessible. We demonstrate that ICA is itself a powerful geometric primitive. That is, having access to an efficient algorithm for ICA enables us to efficiently solve other important problems in machine learning. The first such reduction is a solution to the open problem of efficiently learning the intersection of n + 1 halfspaces in Rn, posed in [43]. This reduction relies on a non-linear transformation of samples from such an intersection of halfspaces (i.e. a simplex) to samples which are approximately from a linearly transformed product distribution. Through this transformation of samples, which can be done efficiently, one can then use an ICA algorithm to recover the vertices of the intersection of halfspaces. Finally, we again use ICA as an algorithmic primitive to construct an efficient solution to the widely-studied problem of learning the parameters of a Gaussian mixture model. Our algorithm again transforms samples from a Gaussian mi

关键词： Independent component analysis

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Spectral Learning of Shared Dynamics Between Generalized-Linear Processes 38

Spectral Learning of Shared Dynamics Between Generalized-Lin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Oganesian, Lucine L. Sani, Omid G. Shanechi, Maryam M. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los AngelesCA United States

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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Fast HEVC Transrating using Random Forests

Fast HEVC Transrating using Random Forests

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IEEE Visual Communications and Image Processing (VCIP)

作者： Mateus Grellert Tiago Oliveira Carlos Rafael Duarte Luis A. da Silva Cruz Graduate Program in Electronic and Computer Engineering Catholic University of Pelotas Brazil

ISBN: (纸本)9781538644591;9781538644584

This article describes a fast transrating solution for HEVC based on classification and machine learning techniques. Two classifiers are trained to predict the range of CTU quadtree depths that will be searched to find the best CTU partitioning. Three approaches are proposed for reducing the number of features used by the classifiers, two based on feature selection, and one based on feature transformation using autoencoders. A full transrating framework based on x265 is built for model training and evaluation. Experimental results using the x265 encoder show that an average 41.81% computational complexity reduction can be achieved at the cost of a tolerable 0.29% Bjontegaard-Delta bitrate, outperforming competing methods.

关键词： Feature extraction Training Bit rate Streaming media Transcoding Upper bound

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Effective Loss Function for Unbalanced Breast Thermal Image Segmentation

Effective Loss Function for Unbalanced Breast Thermal Image ...

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2021 International Conference on computer System, Information Technology, and Electrical engineering, COSITE 2021

作者： Roslidar, Roslidar Saddami, Khairun Irhamsyah, Muhammad Arnia, Fitri Syukri, Maimun Munadi, Khairul Universitas Syiah Kuala Doctoral Program Post Graduate Program School of Engineering Banda Aceh23111 Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh23111 Indonesia Universitas Syiah Kuala Medical Faculty Banda Aceh23111 Indonesia

ISBN: (纸本)9781665425094

The convolutional neural network's ability to learn images has reigned in computer vision tasks of object detection, classification, and segmentation. In segmentation, the CNN architectures of U-Net and SegNet have shown a good performance;thus, we implemented these networks to take the region of interest (ROI) of the breast thermal images from database for mastology research (DMR). We fine-tuned the networks to find the optimal hyperparameter that can result in the best learning performance. The networks were trained using the stochastic gradient descent optimization algorithm, and weights were updated using the error backpropagation of the algorithm. To minimize the error, the loss function is applied to evaluate a candidate solution of the weights. Thus, we conducted a preliminary study on finding the optimal loss function for breast thermal image segmentation. We applied loss functions of cross-entropy, dice, and focal and figured out the one that provides the highest segmentation accuracy. Then, to evaluate the segmentation result, we calculated the pixel accuracy rate and the dice coefficient to obtain the overlapping index measuring the overlay area between ground truth and predicted output. The result shows that the SegNet model with cross-entropy loss function can take the ROI of breast thermal images at the highest pixel accuracy and optimum dice coefficient of 0.8845 and 0.7928, respectively. © 2021 IEEE.

关键词： Image segmentation

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Three-port converter based on the bidirectional Ćuk and the LC series-resonant half-bridge topologies for renewable energy applications

Three-port converter based on the bidirectional Ćuk and the...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Juliano Luiz dos Santos de Morais Roger Gules Graduate Program in Electrical and Computer Engineering — CPGEI UTFPR Curitiba Brazil

The development of a DC-DC three-port converter (TPC) is proposed in this paper. Through the combination of a bidirectional Ćuk converter and a high frequency transformer with a full-bridge (FB) rectifier circuit, two non-isolated energy ports and one isolated energy port are achieved. A LC series-resonant circuit is employed, resulting in soft-switching in the power switches. The bidirectional Cuk converter, controlled by pulse width modulation (PWM), and the tank resonant circuit, controlled by pulse frequency modulation (PFM) above the resonance frequency, allow two control variables for the topology. Furthermore, the power switches are shared for the operation of both circuits, reducing the number of active components. Voltage and current regulation are performed for applications with photovoltaic panel and bank of batteries. Operation modes, converter analysis and control strategy of the proposed TPC are presented. Experimental results are implemented to discuss the feasibility of the proposed TPC.

关键词： Inductors Capacitors Topology Batteries Resonant frequency RLC circuits Switches

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Back propagation neural network experiment on team matchmaking MOBA game 1

Back propagation neural network experiment on team matchmaki...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Tanuar, Evawaty Abbas, Bahtiar Saleh Trisetyarso, Agung Kang, Chul-Ho Gaol, Ford Lumban Suparta, Wayan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Sanata Dharma University Yogyakarta55282 Indonesia

ISBN: (纸本)9781538609545

Backpropagation is one of the famous method for learning, Implementing Backpropagation techniques in games is part of AI in game. This paper aim to analyzed the best team composition in a Moba Game type in learning using neural network. Mobile Legend: Bang Bang is one of MOBA game for mobile phone. In this experiment, the 2 hidden layers back-propagation neural network algorithm is used to analyze the Mobile Legend game. 5000 training steps was done to reach the stability. This also a way of learning to find the best composition in a team played. Research in area AI and game is very wide and open, a huge opportunity to be explore in future. The objective of this research is to produce the good combination of team which can be resulted in a greater winning chance. © 2018 IEEE.

关键词： Backpropagation

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A formal approach for component based embedded software modelling and analysis

A formal approach for component based embedded software mode...

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IEEE International Symposium on Industrial Electronics 2005, ISIE 2005

作者： Almeida, Hyggo Oliveira Dias Da Silva, Leandro Oliveira, Elthon Perkusich, Angelo Federal University of Campina Grande Department of Electrical Engineering Campina Grande Paraiba Brazil Federal University of Campina Grande Graduate Program in Computer Science Campina Grande Paraiba Brazil

ISBN: (纸本)0780387384

To develop software for embedded systems the designer must take into account different kinds of problems and complexities. The main issues are related to late integration with the target hardware and the separation of the development environments from target execution platforms. Due to the fact that in most situations they are developed using low-level programming languages, approaches to develop complex software systems, such as component based software development, cannot be used at all. In this work we introduce a component based development process that integrates colored Petri nets and a component model in order to introduce better abstraction mechanisms in the modelling, analysis and verification processes for embedded software systems. ©2005 IEEE.

关键词： Embedded systems

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on computer Science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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