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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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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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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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Application of Entropy Filter in the Analysis of X-Ray Images

Application of Entropy Filter in the Analysis of X-Ray Image...

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International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)

作者： N.A. Likhachov A.V. Likhachov Department of structural mechanics Novosibirsk State University of Architecture and Civil Engineering NSUACE (Sibstrin) Novosibirsk Russia Research group of computer science and applied mathematics Institute of Automation and Electrometry IAE SB RAS Novosibirsk Russia

The problem of automatic analysis of X-ray images of large reinforced concrete structures is considered. The aim is to improve the accuracy of the evaluation of the shape and size of projections of internal cracks in the armature. The analysis is based on the classification of image elements by brightness threshold. In this case, it is complicated by the fact that the image has low contrast. To increase it, it is proposed to use an entropy filter whose window size is consistent with the noise level in the image. The study, including a computational experiment, confirmed the effectiveness of the proposed method.

关键词： Brightness Entropy Filtering Detectors Industrial engineering Microsoft Windows Concrete

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High energy storage efficiency and large electrocaloric effect in lead-free BaTi0.89Sn0.11O3 ceramic

arXiv

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arXiv 2020年

作者： Merselmiz, Soukaina Hanani, Zouhair Mezzane, Daoud Spreitzer, Matjaz Bradeško, Andraž Fabijan, David Vengust, Damjan Hajji, Lahoucine Abkhar, Zahra Razumnaya, Anna Rožič, Brigita Luk'yanchuk, Igor A. Kutnjak, Zdravko IMED-Lab Cadi Ayyad University Marrakesh40000 Morocco ICMCB University of Bordeaux Pessac33600 France Jozef Stefan Institute Ljubljana1000 Slovenia LPMC University of Picardy Jules Verne Amiens80039 France Physics Faculty Southern Federal University Rostov-on-Don344090 Russia Department of Cybersecurity and Computer Engineering Faculty of Automation and Information Technologies Kyiv National University of Construction and Architecture Kyiv Ukraine

Lead-free BaTi0.89Sn0.11O3 (BTSn) ceramic was elaborated via a solid-state reaction method and its dielectric, ferroelectric, energy storage, electromechanical as well as electrocaloric properties were investigated at 25 kV/cm. Pure perovskite structure was confirmed by X-ray diffraction analysis. The maximum of the dielectric constant was found to be 17390 at 41 °C. The enhanced total energy density, the recovered energy density, and the energy storage efficiency of 92.7 mJ/cm3, 84.4 mJ/cm3, and 91.04%, respectively, were observed at 60 °C, whereas the highest storage efficiency of 95.87 % was obtained at 100 °C. At room temperature, the electromechanical strain and the large-signal piezoelectric coefficient reached a maximum of 0.07 % and 280 pm/V. The large electrocaloric effect of 0.71 K and the electrocaloric responsivity of 0.28 × 10-6 ***/kV at 49 °C under 25 kV/cm were indirectly calculated via Maxwell relation from the ferroelectric polarization P (T, E) that was determined from the P-E hysteresis loops. By exploiting the Landau-Ginzburg-Devonshire (LGD) phenomenological theory, the electrocaloric response was estimated to be of 0.61 K at 50 °C under 25 kV/cm. We conclude that BTSn lead-free ceramic is a promising candidate for potential applications in high-efficiency energy storage devices and solid-state refrigeration technology. © 2020, CC-BY.

关键词： Piezoelectricity

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Cluster analysis for Power Quality monitoring 11

Cluster analysis for Power Quality monitoring

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11th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2017

作者： Florencias-Oliveros, Olivia Aguera-Perez, Agustin Gonzalez-De-La-Rosa, Juan-Jose Palomares-Salas, Jose-Carlos Sierra-Fernandez, Jose-Maria Montero, Alvaro Jimenez Spain Department of Automation Engineering Electronics Architecture Computer Networks Polytechnic School of Algeciras University of Cadiz Spain

ISBN: (纸本)9781509049639

This paper proposes a technique based in the graphical cluster analysis to be implemented in Smart Power Quality Analyzers (SPQA), a proposal Instrument Class S, according to the standard UNE-EN 61000-4-30, with the goal of monitoring electrical grids where new events arise as a consequence of the penetration of new energy resources. In the presence of an electrical fault the instrument triggers the measurement procedure, according to a settled threshold of the PQ, and calculates the higher-order statistics (HOS) features in the time domain that enable the classification. Performance results over a battery of 160 signals show two clusters (sags and transients), with an acceptable accuracy of 80%. The work also presents the concept of the uncertainty inter-cluster area. The analysis of the intensity cluster disturbances helps the grid operator characterizing better the database and introducing some qualitative criteria for monitoring the smart grid. © 2017 IEEE.

关键词： Power quality

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Data hiding algorithm for hevc using intra-coded frames 50

Data hiding algorithm for hevc using intra-coded frames

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

作者： Rodríguez, David Botella, Guillermo Del Barrio, Alberto A. Cuesta, David Computer Science Faculty Universidad Complutense de Madrid Prof. José García Santesmases 9 Madrid Spain La Paz University Hospital Spain Department of Computer Architecture and Automation Universidad Complutense de Madrid Prof. José García Santesmases 9 Madrid Spain Technical Direction Admr. Infraestructuras Ferroviarias Sor Ángela de la Cruz 3 Madrid Spain

ISBN: (纸本)9781510860209

This paper proposes a method to hide information in a video and to recover it after having been encoded under the standard H.265/HEVC. The process is based on manipulating the luminance properties of the YUV video sequence, without the need to modify the HEVC standard. Results show that the quality loss due to embedding the message is negligible in comparison with the original video. Besides, the proposed recovery method is able to extract all the information from the encoded video. © 2018 Society for Modeling & Simulation International (SCS).

关键词： Steganography

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Advanced migraine prediction simulation system 49

Advanced migraine prediction simulation system

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49th Summer computer Simulation Conference, SCSC 2017, Part of the 2017 Summer Simulation Multi-Conference, SummerSim 2017

作者： Pagán, Josué Moya, José M. Mittal, Saurabh Ayala, José L. Department of Computer Architecture and Automation CCS-Center for Computational Simulation Complutense University of Madrid Madrid Spain LSI - Electronic Engineering Department CCS-Center for Computational Simulation Technical University of Madrid Madrid Spain Complex Systems Research Lab Dunip Technologies LLC HerndonVA United States Department of Computer Architecture and Automation Computer Science Faculty Complutense University of Madrid Spain

ISBN: (纸本)9781510838284

In the Internet of Things (IoT) era, there is growing interest in wireless monitoring sensors for detection, classification and prediction of health symptoms. The prediction of symptoms in chronic diseases such as migraines brings new hope to improve patients' lives. The prediction of a migraine symptomatic event through monitoring hemodynamic variables has been previously demonstrated in our earlier work. In this paper, a simulation-based approach for a real-Time migraine prediction system is described. The simulation has been implemented using the specifications of the formal description language Discrete EVent Systems (DEVS). The simulation system is a proof of concept that is ready for testing in a real-world ambulatory monitoring environment. The results obtained encourage developing a hardware/software (HW/SW) cosimulation system that incorporates Hardware-in-The-Loop (HIL) components as prior step to the expensive and slow hardware implementation of a complete migraine prediction device. When such a system is used in a real-Time setting, it can simulate failures in sensors and trigger alarms for active patient response. © 2017 Society for Modeling & Simulation International (SCS).

关键词： Forecasting

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