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Spring Simulation Conference (SpringSim)

作者： Luis García-Temza José L. Risco-Martín José L. Ayala Gemma Reig Roselló Juan M. Camarasaltas Department of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Hospital Universitario de La Princesa Madrid Spain

The application of computer-aided techniques regarding stroke diagnosis is especially important in non-urban areas, because of the lack of adequate resources. The purpose of this research consists of testing the hypothesis that state-of-the-art machine learning-based modeling techniques, when integrated with non-invasive monitoring technologies, can help on the diagnosis of the type of stroke only few minutes after the crisis. This method has been also tested to predict future risks, like the eventual death of the *** collected dataset comprised the medical records of 119 patients with 7 predictors and two target variables: diagnosis of stroke type and prediction of death. 7 different algorithms have been employed, evaluated over 6 different metrics. 10-fold cross validation resampling method was utilized. Random Forest models produced the best performance in stroke diagnosis and death prediction compared to the other algorithms, with average values of 0.93± 0.03 and 0.97± 0.01, respectively.

关键词： Stroke (medical condition) Machine learning algorithms Prediction algorithms Training Artificial intelligence Decision trees Logistics

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FS-Net: Medical image denoising via local receptive field smoothing network 4

FS-Net: Medical image denoising via local receptive field sm...

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4th IEEE International Conference on Data Science in Cyberspace, DSC 2019

作者： Xiaowei, Wang Xiong, Jiaxin Wang, Rui Sori, Worku J. Wang, Jingtian Shaohui, Liu Jiang, Feng Command Automation College PLA Army Engineering University Nanjing210007 China School of Computer Beijing Institute of Technology Beijing China School Of Architecture Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Harbin China Mary Star of the Sea High School CA90731 United States School of Computer Science Jinling Hospital Nanjing China Harbin210002 China

ISBN: (纸本)9781728145280

Noise is one of the most challenging tasks in medical images which is happening during transmission and acquisition, and as a result, it complicates a subsequent medical image analysis task. This paper proposes a novel local receptive field smoothing network (FS-Net) for medical image denoising by introducing smoothing priors of receptive field of intermediate CNN layers. First, for a receptive field of a CNN, each of its values smoothing properties are retained by weighting their local neighborhoods (i.e., after smoothing, each new neurons are the weighted average of its neighborhoods). The weights are calculated using the similarity of their corresponding receptive field (i.e., the similarity between the receptive field used to calculate a receptive fields value and the receptive field used to calculate their corresponding neighborhoods) and this keeps the smoothing properties. Second, by applying this smoothing prior on receptive fields of intermediate CNN layers, an end to end FSNet which directly approximate the latent clean images from its noisy version is designed. The receptive fields smoothing helps to train deeper architecture and improves the reconstructed image quality. Experimental results on Breast Mammography images reveal that the proposed method outperforms other states of the art denoising methods. © 2019 IEEE.

关键词： Image denoising

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States-Based Energy Management System for an EV Hybrid Charging Station with Z-Source Converters

States-Based Energy Management System for an EV Hybrid Charg...

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Power Tech Conference

作者： Enrique González-Rivera Pablo García-Triviño Luis M. Fernández-Ramirez Raúl Sarrias-Mena Juan P. Torreglosa Francisco Jurado Department of Electrical Engineering Research Group in Sustainable and Renewable Electrical Technologies (PAIDI-TEP-023) University of Cadiz Algeciras Spain Department of Engineering in Automation Research Group in Sustainable and Renewable Electrical Technologies (PAIDI-TEP-023) Electronics and Computer Architecture & Networks University of Cadiz Algeciras Spain Electrical Engineering Department University of Huelva Palos de la Frontera Spain Department of Electrical Engineering Research Group in Research and Electrical Technology (PAIDI- TEP-152) University of Jaen Linares Spain

ISBN: (数字)9781665435970

ISBN: (纸本)9781665411738

This work presents a new energy management system (EMS) based on states for a hybrid charging station of electric vehicles (EV). The hybrid charging station is composed of a photovoltaic (PV) system, a complete hydrogen system based on fuel cell (FC), electrolyzer (EZ) and tank as energy storage system (ESS), a battery energy storage system (BES), a grid connection, and six fast charging units, all of them connected to a common MVDC bus through Z-sources converters (ZSC). The states-based EMS is designed to control the power flow among the energy sources of the hybrid charging station. The viability of the EMS is proved under a long-term simulation of 25 years in Simulink by using an EV load profile taken from the Strategy Technology Energy Plan of the EU and real data for the sun irradiance.

关键词： Photovoltaic systems Renewable energy sources Costs Software packages Hydrogen Charging stations Hybrid power systems

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Advanced migraine prediction hardware system 50

Advanced migraine prediction hardware system

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50th Summer computer Simulation Conference, SCSC 2018, Part of the 2018 Summer Simulation Multi-Conference, SummerSim 2018

作者： Henares, Kevin Pagán, Josué Ayala, José L. Risco-Martín, José L. Dept. of Computer Architecture and Automation Complutense University of Madrid Madrid Spain Electronic Engineering Department Universidad Politécnica de Madrid Spain CCS-Center for Computational Simulation Madrid Spain Dept. of Computer Architecture and Automation Complutense University of Madrid CCS-Center for Computational Simulation Madrid Spain

ISBN: (纸本)9781510860209

Migraine is one of the most disabling neurological diseases. Its prevalence reaches 15% of the population in developed countries and lead to high economic costs for private and national health systems. There is no cure for the migraine yet. Objective and accurate migraine prediction turns necessary in order to increase the effectiveness of the current treatments. To tackle this problem authors demonstrated in previous works the feasibility of the migraine modeling and presented a DEVS-based model for migraine prediction in a real time framework. This work presents the first step to bring migraine predictive models and simulations to real devices for the first time in the literature. To do this, the DEVS-based model has been ported to a robust hardware description language (VHDL) to be implemented into a FPGA. This paper leads to the final stage: the physical implementation of a predictive migraine monitoring device. © 2018 Society for Modeling & Simulation International (SCS).

关键词： Field programmable gate arrays (FPGA)

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Latent source mining of fMRI data via deep belief network 15

Latent source mining of fMRI data via deep belief network

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15th IEEE International Symposium on Biomedical Imaging, ISBI 2018

作者： Li, Lei Hu, Xintao Huang, Heng He, Chunlin Wang, Liting Han, Junwei Quo, Lei Zhang, Wei Liu, Tianming School of Automation Northwestern Polytechnical University Xi'an China Cortical Architecture Imaging and Discovery Lab Department of Computer Science University of Georgia United States

ISBN: (纸本)9781538636367

Blind source separation (BSS) is one of the fundamental techniques for resolving meaningful features in functional magnetic resonance imaging (fMRI). BSS methods based on unsupervised shallow models (e.g., restricted Boltzmann machine, RBM) have improved fMRI BSS compared to conventional matrix factorization models (e.g., independent component analysis (ICA)). In machine learning field, it is widely accepted that deeper models (e.g., deep belief network, DBN) are more powerful in latent feature learning and data representation. Thus, in this paper we propose a BSS model based on DBN with two hidden layers of RBM. In addition, we apply the model to fMRI time series for BSS instead of fMRI volumes as proposed in previous studies, such that the parameter searching space is significantly pruned and large-scale training samples of fMRI time series are available. Our experimental results on an fMRI dataset acquired with a movie stimulus showed that the proposed model is capable of identifying not only latent components related to distinct brain networks, but also the ones related to functional interactions across different networks. © 2018 IEEE.

关键词： Blind source separation

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Efficient FPGA Implementation of Binary Field Multipliers Based on Irreducible Trinomials

Efficient FPGA Implementation of Binary Field Multipliers Ba...

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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： José L. Imaña Department of Computer Architecture and Automation Complutense University Madrid Spain

Binary extension (or Galois) fields GF(2 m ) have been widely studied due to their use in several important applications, such as cryptography, error control codes and digital signal processing. These applications require efficient hardware implementations of GF(2 m ) arithmetic operations, particularly multiplication, which is considered the most important and complex one. The complexity of GF(2 m ) multiplication depends on the representation basis and on the defining irreducible polynomial f(y) selected for the finite field. For efficient hardware implementations, polynomial basis and irreducible trinomials or pentanomials are normally used. Any element A ϵ GF(2 m ) can be represented in the polynomial basis {1,x,...,x m-1 } as A = Σ i-0 m-1 ,α i x i with α i ϵ GF(2), where x is a root of the irreducible polynomial f(y) = Σ i=0 m f i y i . Polynomial basis multiplication C = A · B requires a polynomial multiplication followed by a reduction modulo an irreducible polynomial. Mastrovito [1] proposed an efficient bit-parallel polynomial basis multiplier in which a product matrix was introduced to combine the above two steps together. A new polynomial basis multiplication method applied to irreducible trinomials was proposed in [2], where the functions S_i and T_i given by the addition of terms x k = (a k b k ) and z i j = (a i b j + a j b i ), with a i , b i ϵ GF(2), were obtained from the decomposition of a product matrix. The addition of these functions is used for the computation of the product of two GF(2 m ) elements. In [3], the above method was applied to type II irreducible pentanomials and the functions S_i and T_i were split in the form S i = s k i S k i + ... + s 0 i S 0 i and T i = t k i T i k + ... + t 0 i T 0i , with s j i , t j i ϵ GF(2) and k = log 2 m. The terms S i j and T i j represent the sum of 2 j products a_kb_l and therefore can be implemented as a j-level complete binary tree of XOR gates. The addition in pairs of binary

关键词： Field programmable gate arrays Silicon Binary trees Complexity theory Delays Tools

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Modeling task fMRI data via mixture of deep expert networks 15

Modeling task fMRI data via mixture of deep expert networks

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15th IEEE International Symposium on Biomedical Imaging, ISBI 2018

作者： Huang, Heng Hu, Xintao Dong, Qinglin Zhao, Shijie Zhang, Shu Zhao, Yu Quo, Lei Liu, Tianming School of Automation Northwestern Polytechnical University Xi'an China Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center University of Georgia United States

ISBN: (纸本)9781538636367

Task-based fMRI (tfMRI) has been a powerful noninvasive tool to study cognitive behaviors of the human brain. Computational modeling of tfMRI data involves two important aspects: the development of algorithms to learn a wide variety of meaningful patterns from specific task fMRI signals and the development of models to discriminate among different tasks. Although both aspects are important, most previous fMRI studies focused either on learning meaningful patterns from single tasks or learning discriminations across different tasks, and as a consequence, a unified approach to studying both aspects is rarely explored. To bridge this knowledge gap, in this study, we adopted the basic idea from convolutional neural network (CNN) and proposed a new variant of CNN called deep expert network (DEN) to model tfMRI data in a hierarchical manner. At the same time, by mixing these DEN models, we are able to achieve discriminations across different tasks. To validate the effectiveness and efficiency of the proposed mixture of DENs (MoDEN), we applied them on three Human Connectome Project (HCP) task fMRI datasets (language, social and working memory tasks). Our experiments achieved promising results and demonstrated the superior ability of MoDEN in modeling task-based fMRI data. © 2018 IEEE.

关键词： Mixtures

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Characterizing task-evoked and intrinsic functional networks from task-based fMRI data via two-stage sparse dictionary learning 15

Characterizing task-evoked and intrinsic functional networks...

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15th IEEE International Symposium on Biomedical Imaging, ISBI 2018

作者： Liu, Huan Zhao, Shijie Jiang, Xi Zhang, Shu Hu, Xintao Quo, Lei Liu, Tianming School of Automation Northwestern Polytechnical University Xi'an China Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center University of Georgia United States

ISBN: (纸本)9781538636367

Recently, increasing studies suggest that task-evoked brain networks and intrinsic connectivity networks are concurrent during task performance in the architecture of functional brain organization. However, it remains challenging to identify and quantitatively characterize these mixtures of networks from task-based functional magnetic resonance imaging (fMRI) data. In this paper, we propose a two-stage sparse dictionary learning method by establishing spatial correspondences among the brain networks of multiple subjects and automatically categorize these networks into task-evoked, intrinsic and uncertain ones. The proposed framework is applied to Human Connectome Project (HCP) task-based fMRI data. Experimental results demonstrate that this method can effectively identify group-wise task-evoked and intrinsic networks simultaneously. Our study provides a novel data-driven method to facilitate a comprehensive understanding of the functional brain architecture. © 2018 IEEE.

关键词： Network architecture

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Correction to: Gyral-sulcal contrast in intrinsic functional brain networks across task performances

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Brain imaging and behavior 2021年第4期15卷 2235页

作者： Lin Zhao Tuo Zhang Lei Guo Tianming Liu Xi Jiang School of Automation Northwestern Polytechnical University Xi'an China. Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia Athens GA USA. School of Automation Northwestern Polytechnical University Xi'an China. tuozhang@***. School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China. xijiang@***.

A Correction to this paper has been published: https://***/10.1007/s11682-020-00426-z

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Open Multi-Processing Acceleration for Unsupervised Land Cover Categorization Using Probabilistic Latent Semantic Analysis

Open Multi-Processing Acceleration for Unsupervised Land Cov...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： S. Bernabé C. García R. Fernández-Beltrán M. E. Paoletti J. M. Haut J. Plaza A. Plaza Department of Computer Architecture and Automation Complutense University Madrid Spain Institute of New Imaging Technologies University Jaume I Castellón Spain Hyperspectral Computing Laboratory University of Extremadura Cáceres Spain

The probabilistic Latent Semantic Analysis (pLSA) model has recently shown a great potential to uncover highly descriptive semantic features from limited amounts of remote sensing data. Nonetheless, the high computational cost of this algorithm often constraints its operational application for land cover categorization tasks. In this scenario, this paper presents an Open Multi-Processing (OpenMP) implementation of the pLSA algorithm for unsupervised Synthetic Aperture Radar (SAR) and Multi-Spectral Imaging (MSI) image categorization. The experimental results suggest that multi-core systems are an important architecture for the efficient processing of both SAR and MSI datasets. Specifically, the proposed approach is able to cover a real scenario exhibiting good results in both accuracy and performance terms.

关键词： Remote sensing Semantics Probabilistic logic Synthetic aperture radar Agriculture Buildings Forestry

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