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International Symposium on Biomedical Imaging

作者： Liting Wang Xintao Hu Meng Wang Jinglei Lv Junwei Han Shijie Zhao Qinglin Dong Lei Guo Tianming Liu School of Automation Northwestern Polytechnical University Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia

ISBN: (纸本)9781509011735

Equipped with selective auditory attention (SAA), people are able to rapidly shift their attention to auditory events of interest. Although abstract neuroimaging paradigms are fundamental for exploring the neural basis of SAA, whether those findings are valid in a more naturalistic condition and how the types of auditory stimuli affect SAA are largely unknown. Here we propose a brain decoding study to explore SAA using naturalistic auditory excerpts in three categories (pop music, classical music and speech) as stimuli for functional magnetic resonance imaging (fMRI). We adopted a computational auditory attention model to estimate attentional allocation for the excerpts. We then extracted brain activity features from fMRI data via sparse representation and used them to decode the auditory attention allocation. Our experimental results showed that the primary auditory cortex was commonly involved in the attentional processing of the three categories and the contribution of distinct brain networks to the decoding model in each group. Our study on the one hand provides novel insights into neural SAA in naturalistic experience, on the other hand shows the possibility of leveraging neuroimaging studies by integrating naturalistic stimuli and computational auditory information processing approaches.

关键词： Dynamic auditory attention naturalistic experience brain decoding sparse representation

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EXPLORING HUMAN BRAIN ACTIVATION VIA NESTED SPARSE CODING AND FUNCTIONAL OPERATORS

EXPLORING HUMAN BRAIN ACTIVATION VIA NESTED SPARSE CODING AN...

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International Symposium on Biomedical Imaging

作者： Shu Zhang Xiang Li Lei Guo Tianming Liu Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia School of Automation Northwestern Polytechnical University

ISBN: (纸本)9781509011735

Traditional task-based fMRI activation detection methods, such as the general linear model (GLM), assume that the fMRI signals of activated brain regions follow the external stimulus paradigm. Typically, these activated regions are detected independently in a voxel-wise fashion, and the interaction among voxels is nevertheless neglected. Despite the wide use and remarkable success of GLM, the temporal and spatial relationships among activated regions remain unveiled. In response to this challenge, we present a novel method that combines two-stage sparse representation framework and the operator modulations (integral and derivative) to explore the temporal and spatial organizations underlying fMRI-derived activations in the brain. The two-stage sparse representation framework is designed to deal with big data and the functional operator is focused on finding the refined activation areas in the brain under task performances. Experiments demonstrated that diverse temporal and spatial organizations between activated regions exist and different functional operators may lead to different activation areas, thus significantly supplementing to the available principle of GLM that has been widely used in the human brain mapping field.

关键词： Functional Operator Activation Detection Nested Sparse Codings

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FMRI DATA CLASSIFICATION BASED ON HYBRID TEMPORAL AND SPATIAL SPARSE REPRESENTATION

FMRI DATA CLASSIFICATION BASED ON HYBRID TEMPORAL AND SPATIA...

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International Symposium on Biomedical Imaging

作者： Huan Liu Mianzhi Zhang Xintao Hu Yudan Ren Shu Zhang Junwei Han Lei Guo Tianming Liu School of Automation Northwestern Polytechnical University Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia

ISBN: (纸本)9781509011735

Task-based functional magnetic resonance imaging (tfMRI) is widely used to localize brain regions or networks in response to various cognitive tasks. However, given two groups of tfMRI data acquired under distinct task paradigms, it is not clear whether there exist intrinsic inter-group differences in signal composition patterns, and if so, whether these differences could be used for data discrimination. The major challenges originate from the high dimensionality and low signal-to-noise ratio of fMRI data. In this paper, we proposed a novel framework using hybrid temporal and spatial sparse representation to tackle above challenges. We applied the proposed framework to the Human Connectome Project (HCP) tfMRI data. Our experimental results demonstrated that the task types of fMRI data can be successfully classified, achieving a 100% classification accuracy. We also showed that both task-related components and resting state networks (RSNs) can be reliably identified. Our study provides a novel data-driven approach to detecting discriminative inter-group differences in fMRI data based on signal composition patterns, and thus potentially can be used to control fMRI data quality and to infer biomarkers in brain disorders.

关键词： FMRI classification hybrid temporal and spatial sparse representation

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Adaptive compressed sensing at the fingertip of Internet-of-Things sensors: An ultra-low power activity recognition 20

Adaptive compressed sensing at the fingertip of Internet-of-...

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20th Design, automation and Test in Europe, DATE 2017

作者： Fallahzadeh, Ramin Ortiz, Josue Pagan Ghasemzadeh, Hassan School of Electrical Engineering and Computer Science Washington State University PullmanWA99164-2752 United States Department of Computer Architecture and Automation Complutense University of Madrid Madrid Spain

ISBN: (纸本)9783981537093

With the proliferation of wearable devices in the Internet-of-Things applications, designing highly power-efficient solutions for continuous operation of these technologies in life-critical settings emerges. We propose a novel ultra-low power framework for adaptive compressed sensing in activity recognition. The proposed design uses a coarse-grained activity recognition module to adaptively tune the compressed sensing module for minimized sensing/transmission costs. We pose an optimization problem to minimize activity-specific sensing rates and introduce a polynomial time approximation algorithm using a novel heuristic dynamic optimization tree. Our evaluations on real-world data shows that the proposed autonomous framework is capable of generating feedback with -80% confidence and improves power reduction performance of the state-of-the-art approach by a factor of two. © 2017 IEEE.

关键词： Compressed sensing

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Improvement of extrusion equipment and technologies for processing secondary combined polymer materials and mixtures

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Journal of Physics: Conference Series 2019年第1期1210卷

作者： V V Dyadichev A V Kolesnikov S G Menyuk A V Dyadichev V.I. Vernadsky Crimean Federal University Academy of Construction and Architecture Engineering Center 'Bio-positive Construction and Resource-Saving' 3 Pavlenko Str. Simferopol Crimea 295000 Russia Lugansk National University named after Vladimir Dal Department of Automation and Computer-Integrated Technologies 20-A Molodezhnyi Sq. Lugansk 91034 Ukraine

The article is devoted to the solution of scientific and technical problem of perfecting the extrusion equipment and technology for processing secondary combined polymeric materials and polymeric mixtures including wastes, by the method of extrusion with use of multi-section screw that allows to manufacture new high-quality products and reduce the environment pollution. A new screw design is proposed consisting of several functional sections. A mathematical model for operation of the extruder with the multi-section screw has been developed to analyse the operation parameters and produce new designs of the extruder screw. Based on the studies of the functional purpose of the extruder zones and features of processing the polymeric mixtures with secondary raw materials, a functional diagram of the screw is proposed. The thickness values of the strips of the polymer mixture components at the outlet of the extruder sections for analysing the mixing capacities have been obtained. It has been established that the extruder with the multi-section screw can be used with effective combination of two stages: mixing and basic processing. With the help of mathematical modelling the diagrams of linear velocities and shear rates for each extruder section have been obtained.

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Yet Another Robot Application?

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IEEE ROBOTICS & automation MAGAZINE 2016年第2期23卷 12-+页

作者： Salvini, Pericle Korsah, Ayorkor Nourbakhsh, Illah Department of Computer Architecture and Automation Computer Science Faculty Complutense University of Madrid c∖José García Santesmases s∖n Madrid Spain Key Laboratory of Dependable Service Computing in the Cyber Physical Society of the Ministry of Education Chongqing University and the College of Automation Chongqing University Chongqing China School of Automation Chongqing University Chongqing China Department of Electrical Engineering and Computer Science Northwestern University Evanston IL School of Electrical and Electronic Engineering Nanyang Technological University Singapore

The articles in this special section is to find out what lies beyond the fascination toward robots that we have inherited from popular culture, the pleasure of learning with robots derived from the gamification of learning, and the strong appeal for robotics technologies given by its aura of innovation. Are there authentic ways in which robotics can improve education, and what affordances does robotics, in particular, provide for such educational transformation? Indeed, one of the biggest gray areas of educational robotics concerns the lack of empirical evidence on the educational (pedagogical as well as psychological) effectiveness of robots that is related to both the lack of standardized evaluation criteria and the methodological difficulties in conducting sound scientific tests.

关键词： Special issues and sections Robots computer applications Technology forecasting Engineering education STEM Education Programming

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GROUP-WISE SPARSE REPRESENTATION OF RESTING-STATE FMRI DATA FOR BETTER UNDERSTANDING OF SCHIZOPHRENIA

GROUP-WISE SPARSE REPRESENTATION OF RESTING-STATE FMRI DATA ...

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International Symposium on Biomedical Imaging

作者： Lin Yuan Tianming Liu Dewen Hu College of Mechanotrics and Automation National University of Defense Technology Changsha Hunan 410073 China Cortical Architecture Imaging and Discovery Lab Department of Computer Science The University of Georgia Athens United States of America

ISBN: (纸本)9781509011735

Resting-state functional magnetic resonance imaging (rs-fMRI) has become a powerful technique for analyzing cognitive function and its disruption in mental diseases, including schizophrenia. It has played an important role in analyzing and diagnosing mental diseases. In this paper, we present a novel machine learning approach called group-wise sparse representation of rs-fMRI signals to find differences between schizophrenia patients and healthy controls. Firstly, we extract the fMRI signals from all subjects that are registered into the MNI atlas space in the pre-processing step to build a large input signal matrix. Secondly, we use online dictionary learning and sparse coding methods to derive the coefficient matrix. Thirdly, we use two-sample t-test to analyze the regions of increased and decreased activity in schizophrenia patients compared to healthy controls. Finally, by using the AAL atlas the distributions of the detected regions are obtained. We test our approach on the COBRE dataset and the experimental results show that the schizophrenia patients have increased activities mainly at the fronto-parietal network. All other networks excluding the fronto-parietal network show decreased phenomenon. These results provide novel insights into better understanding of schizophrenia.

关键词： resting-state fMRI group-wise sparse representation schizophrenia research

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A FIWARE-Based Component for Data Analysis in Smart Mobility Context

A FIWARE-Based Component for Data Analysis in Smart Mobility...

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IEEE First Summer School on Smart Cities (S3C)

作者： Rosangela de Fátima Pereira Marquesone Tereza Cristina Melo de Brito Carvalho Lucas Batista Guimarães Eduardo Mario Dias Laboratory of Computer Networks and Architecture - LARC Polytechnic School University of São Paulo São Paulo Brazil Department of Energy Engineering and Automation Polytechnic School University of São Paulo São Paulo Brazil

Smart mobility is an emerging concept characterized by the use of disruptive technologies, such as cloud computing, Big Data and artificial intelligence. In this paper we propose a FIWARE-based component focus on improving the data analysis process, from data generated in services and applications existing in the smart mobility context. Our proposal aims to provide features that allows the development of solutions from the industry 4.0 concept and smart cities. Firstly, we briefly contextualize these terms, then we describe the characteristics and architectural design of our FIWARE component, highlighting how it can improve the support to smart services and applications. We also present real application scenarios where this component can be applied, specially considering the Brazilian cities.

关键词： Data analysis Big Data Internet Companies Smart cities

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Modular Fault Diagnosis in Fixed-Block Railway Signaling Systems

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IFAC-PapersOnLine 2016年第3期49卷 459-464页

作者： Durmus, Mustafa S. Ustoglu, lker Tsarev, Roman Y. Schwarz, Michael Electrical and Electronics Engineering Department Pamukkale University Denizli Turkey Control and Automation Engineering Department Yildiz Technical University Istanbul Turkey Institute of Space and Information Technology Siberian Federal University Krasnoyarsk Russia Computer Architecture and System Programming Kassel University Kassel Germany

The diagnosis of possible faults in railway signaling systems is an important issue to provide safe travel and transportation in railways. Signaling system designers have to consider the possible faults which may occur in railway field components both on the requirements preparation phase and on the development phase of the signaling system software or namely, the interlocking system. Although the diagnosis of different unobservable faults is relatively hard, especially for large scale railway fields, this complexity can be overcome by using the Discrete Event System (DES) based modular diagnosis approach which is explained in this paper. The main advantage of using such modular approach for fault diagnosis in fixed-block signaling systems is the inspection of the diagnosability of the whole system with respect to its subsystems (railway field components). In this study, the diagnosability of the railway field equipment and the whole system is also explained with a case study. © 2016

关键词： Fault detection Discrete event simulation Failure analysis Railroad signal systems Railroads Signaling Development phase Field components Field equipment Fixed block signaling Interlocking systems Modular approach Railway signaling systems Signaling systems

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Local energy micro-storage systems in smart communities with active, reactive and harmonic control 16

Local energy micro-storage systems in smart communities with...

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16th International Conference on Environment and Electrical Engineering, EEEIC 2016

作者： Milanes-Montero, Maria-Isabel Gonzalez-Romera, Eva Barrero-Gonzalez, Fermin Romero-Cadaval, Enrique Moreno-Munoz, Antonio Electrical Electronic and Automation Engineering Dept. University of Extremadura Badajoz Spain Computer Architecture Electronics and Electronic Technology Department Universidad de Córdoba Córdoba Spain

ISBN: (纸本)9781509023196

This paper aims to provide control strategies for distributed micro-storage energy systems at the residential level to contribute to smart grid goals. The operation of local Energy Storage Systems (ESS) at homes in a Smart Community with distributed generation based of renewable energies is analyzed by simulation. Each individual home receive the active and reactive power set-points from the Smart Community Energy Management System (SCEMS), acting as an aggregator of resources in the community according to the expected demand and production. Besides, in case the ESS has got available capacity, once the SCEMS requirements are satisfied, it is used to inject the harmonic current components demanded by the household circuitry. It allows a local improvement in the power quality of the demanded current, and so contributes to the global power quality consumption of the community. Simulation results showing the operation of the local ESS are presented to validate the proposed control strategies. © 2016 IEEE.

关键词： Energy management systems

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