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A multi-stage sparse coding framework to explore the effects of Prenatal Alcohol Exposure 1

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1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2016

作者： Zhao, Shijie Han, Junwei Lv, Jinglei Jiang, Xi Hu, Xintao Zhang, Shu Lynch, Mary Ellen Coles, Claire Guo, Lei Hu, Xiaoping Liu, Tianming School of Automation Northwestern Polytechnical University Xi’an China Cortical Architecture Imaging and Discovery Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States Emory University AtlantaGA United States

ISBN: (数字)9783319467207

ISBN: (纸本)9783319467191

In clinical neuroscience,task-based fMRI (tfMRI) is a popular method to explore the brain network activation difference between healthy controls and brain diseases like Prenatal Alcohol Exposure (PAE). Traditionally,most studies adopt the general linear model (GLM) to detect task-evoked activations. However,GLM has been demonstrated to be limited in reconstructing concurrent heterogeneous networks. In contrast,sparse representation based methods have attracted increasing attention due to the capability of automatically reconstructing concurrent brain activities. However,this data-driven strategy is still challenged in establishing accurate correspondence across individuals and characterizing group-wise consistent activation maps in a principled way. In this paper,we propose a novel multi-stage sparse coding framework to identify group-wise consistent networks in a structured method. By applying this novel framework on two groups of tfMRI data (healthy control and PAE),we can effectively identify group-wise consistent activation maps and characterize brain networks/regions affected by PAE. © Springer International Publishing AG 2016.

关键词： Heterogeneous networks

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Developing web & TwinCAT PLC-based remote Control laboratories for modern web-browsers or mobile devices

Developing web & TwinCAT PLC-based remote Control laboratori...

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IEEE Multi-Conference on Systems and Control

作者： Julian Bermudez-Ortega Eva Besada-Portas Jose A. Lopez-Orozco Jesus Chacon Jesus M. de la Cruz Department of Computer Architecture and Systems Engineering of Universidad Complutense de Madrid Computer Science and Automation Department Universidad Nacional de Educacion a Distancia

ISBN: (纸本)9781509007561

This paper describes a new approach to develop remote Control laboratories accessible from modern web-browsers and student devices (PCs, laptops, tablets and smart-phones) based on TwinCAT Programmable Controllers (PLCs), Easy JavaScript Simulations (EJsS) webpages, and a *** laboratory web-server. On the one hand, implementing the laboratory back-end application (responsible of closing the feedback loop over the plant under study) using a TwinCAT PLC provides the laboratory controllers with standard/industrial automation methodologies, real-time support and connectivity to a wide range of input/output signals. On the other hand, defining the controller front-end (graphical/interactive interface used by the students to parametrize the PLC behavior and observe the evolution of the plant and PLC signals) with EJsS facilitates the organization of its visual/interactive elements and allows the generation of a JavaScript and HTML5 webpage that is accessible from modern web-browsers and various types of students devices running different operating systems. Last, but not least, developing the laboratory web-server (in charge of managing the student access to the lab and of hosting its different webpages, including the controller front-end) within the JavaScript development and runtime platform *** ensures a lightweight behavior of the remote lab, provides a robust connectivity to the students, and supports efficient (real-time) communication between the controller back & front-ends. This paper also shows how the new strategy is used to update and overcome the current accessibility limitations of some existing (and published) experiences with Proportional/Integral/Differential (PIDs) and Estimator and State Feedback (ESF) Controllers based on TwinCAT PLCs and Easy Java Simulations (EJS) applets front-ends.

关键词： Laboratories Servers Mobile handsets Graphical user interfaces Real-time systems Robustness Java

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A technique for low power, stuck-at fault diagnosable and reconfigurable scan architecture

A technique for low power, stuck-at fault diagnosable and re...

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East-West Design & Test Symposium (EWDTS)

作者： Binod Kumar Boda Nehru Brajesh Pandey Virendra Singh Jaynarayan Tudu Computer Architecture and Dependable Systems Lab (CADSL) Indian Institute of Technology Bombay India Dept. of Computer Science and Automation Indian Institute of Science Bangalore

ISBN: (纸本)9781509006946

Power dissipation is a major issue with testing of designs having full scan architectures. The proposed scan technique minimizes toggle activity while scanning in test patterns. The method uses bit inversion technique to avoid toggles in scan flip-flops. The setup is dynamically configurable to one among the logic reversal structure and traditional scan while shift-in/shift-out of test patterns. Experimental results indicate that the average toggle activity is minimized substantially compared to California Scan architecture. It has features of full diagnosability of single stuck-at faults along the scan chain path.

关键词： computer architecture Silicon Circuit faults Testing Logic gates Inverters Clocks

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Parallel implementation of the simplex growing algorithm for hyperspectral unmixing using OpenCL

Parallel implementation of the simplex growing algorithm for...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sergio Bernabé Guillermo Botella José M. R. Navarro Carlos Orueta Manuel Prieto-Matias Antonio Plaza Department of Computer Architecture and Automation Complutense University Madrid Spain Hyperspectral Computing Laboratory University of Extremadura Cáceres Spain

Many algorithms for spectral unmixing have been proposed in the last years applied on hyperspectral imaging. This process is composed by three stages where the extraction of endmembers is the most consuming step. However, endmember extraction algorithms (EEAs) can be computationally very expensive and its acceleration on parallel architectures is still an interesting and open problem. In this paper, we present a parallel implementation of the simplex growing algorithm for hyperspectral unmixing called P-SGA on different platforms using the OpenCL framework. The proposed implementation exploits the memory hierarchy to accelerate the parts of this method which are more time-consuming. The proposed algorithm is evaluated in terms of both accuracy and computational performance through Monte Carlo simulations using the following architectures: multi-core Xeon CPU, NVidia GeForce GTX 980 GPU and Intel Xeon Phi accelerator. Experiments are conducted using real hyperspectral data set revealing considerable acceleration factors, which satisfies the real-time constraints given by the data acquisition rate.

关键词： Hyperspectral imaging Graphics processing units Acceleration Kernel computer architecture Performance evaluation

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Comparative analysis of segmentation methods, including adaptive and HOS based algorithms

Comparative analysis of segmentation methods, including adap...

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International Conference on Harmonics and Quality of Power

作者： Enrique Alameda-Hernandez Fernando Aznar Guillermo Botella ETSI Caminos Canales y Puertos Granada University Granada Spain Computer Architecture and Automation Department Universidad Complutense Madrid Spain

ISBN: (纸本)9781509037933

Power perturbations affect the normal operation of the grid, both from the network side and the consumer side. In order to eradicate or attenuate the harmful effects, a first step is needed: voltage or current segmentation into healthy and perturbed intervals. In this work we extend the available plethora of documented methods by implementing an adaptive version of Yule-Walker equation for autoregressive (AR) modeling based on higher-order statistics. This proposal is compared with a wide variety of existing segmentation methods: notch filters, second order AR modeling and cumulant based methods. Simulations confirm that the proposed method being much lighter computationally provides us with useful information for segmentation, besides, for some cases, this information is clearer and easier to interpret, facilitating the segmentation process.

关键词： Mathematical model Adaptation models Computational modeling Power quality Higher order statistics Parametric statistics Transforms

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Latent source mining in FMRI data via deep neural network

Latent source mining in FMRI data via deep neural network

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IEEE International Symposium on Biomedical Imaging

作者： Heng Huang Xintao Hu Junwei Han Jinglei Lv Nian Liu Lei Guo Tianming Liu 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 US

ISBN: (纸本)9781479923519

Independent component analysis (ICA) and its variants have been the dominant methods to the problem of blind source separation (BSS) for functional magnetic resonance imaging (fMRI) data. However, the functional interactions among spatially distributed brain regions and concurrent brain networks deteriorate the basic assumption in ICA-based BSS, that is, the spatial independence of the sources. In this paper, we proposed a novel method for BSS based on recently advanced deep neural network (DNN) algorithm, aiming to detect both internal and functional interaction-induced latent sources simultaneously. We used the motor task fMRI data in the Human Connectome Project (HCP) as a test-bed in the experiments. The results demonstrated the feasibility and effectiveness of the proposed method and its outperformance compared with ICA.

关键词： Training Correlation Data mining Biological neural networks Blind source separation Visualization Signal reconstruction

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Temporal concatenated sparse coding of resting state fMRI data reveal network interaction changes in mTBI 1

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作者： Lv, Jinglei Iraji, Armin Ge, Fangfei Zhao, Shijie Hu, Xintao Zhang, Tuo Han, Junwei Guo, Lei Kou, Zhifeng 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 The University of Georgia AthensGA United States Department of Biomedical Engineering Wayne State University DetroitMI United States Department of Radiology Wayne State University DetroitMI United States

ISBN: (数字)9783319467207

ISBN: (纸本)9783319467191

Resting state fMRI (rsfMRI) has been a useful imaging modality for network level understanding and diagnosis of brain diseases,such as mild traumatic brain injury (mTBI). However,there call for effective methodologies which can detect group-wise and longitudinal changes of network interactions in mTBI. The major challenges are two folds: (1) There lacks an individualized and common network system that can serve as a reference platform for statistical analysis;(2) Networks and their interactions are usually not modeled in the same algorithmic structure,which results in bias and uncertainty. In this paper,we propose a novel temporal concatenated sparse coding (TCSC) method to address these challenges. Based on the sparse graph theory the proposed method can model the commonly shared spatial maps of networks and the local dynamics of the networks in each subject in one algorithmic structure. Obviously,the local dynamics are not comparable across subjects in rsfMRI or across groups;however,based on the correspondence established by the common spatial profiles,the interactions of these networks can be modeled individually and statistically assessed in a group-wise fashion. The proposed method has been applied on an mTBI dataset with acute and sub-acute stages,and experimental results have revealed meaningful network interaction changes in mTBI. © Springer International Publishing AG 2016.

关键词： Graph theory

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Fast CU size decision based on temporal homogeneity detection

Fast CU size decision based on temporal homogeneity detectio...

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Conference on Design of Circuits and Integrated Circuits (DCIS)

作者： D. G. Fernández A. A. Del Barrio G. Botella C. García Video Processing Department Procesamiento Digital y Sistemas S.L (Prodys) Madrid Spain Computer Architecture and Automation Department Universidad Complutense de Madrid Madrid Spain

ISBN: (纸本)9781509045662

High-Efficiency Video Coding (HEVC) is the latest video coding standard of the Joint Collaborative Team on Video Coding (JCT-VC). HEVC noticeably improves compression performance when comparing with previous standards such as H264, providing a major leap forward in video compression technology. However this improvement is achieved increasing the complexity of the encoding process. In this paper an optimized CU size decision algorithm is proposed in order to reduce HEVC computational cost by means of temporal homogeneity classification, which is directly applied to the input image using a GPU.

关键词： Encoding Standards Motion estimation Complexity theory Algorithm design and analysis Graphics processing units High efficiency video coding

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Discover mouse gene coexpression landscape using dictionary learning and sparse coding 1

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作者： Li, Yujie Chen, Hanbo Jiang, Xi Li, Xiang Lv, Jinglei Peng, Hanchuan Tsien, Joe Z. Liu, Tianming Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States School of Automation Northwestern Polytechnical University Xi’an China Allen Institute for Brain Science SeattleWA United States Brain and Behavior Discovery Institute Medical College of Georgia at Augusta University AugustaGA United States

ISBN: (数字)9783319467207

ISBN: (纸本)9783319467191

Gene coexpression patterns carry rich information of complex brain structures and functions. Characterization of these patterns in an unbiased and integrated manner will illuminate the higher order transcriptome organization and offer molecular foundations of functional circuitry. Here we demonstrate a data-driven method that can effectively extract coexpression networks from transcriptome profiles using the Allen Mouse Brain Atlas dataset. For each of the obtained networks,both genetic compositions and spatial distributions in brain volume are learned. A simultaneous knowledge of precise spatial distributions of specific gene as well as the networks the gene plays in and the weights it carries can bring insights into the molecular mechanism of brain formation and functions. Gene ontologies and the comparisons with published data reveal interesting functions of the identified coexpression networks,including major cell types,biological functions,brain regions,and/or brain diseases. © Springer International Publishing AG 2016.

关键词： Genes

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Cambricon-X: An accelerator for sparse neural networks 49

Cambricon-X: An accelerator for sparse neural networks

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Shijin Zhang Zidong Du Lei Zhang Huiying Lan Shaoli Liu Ling Li Qi Guo Tianshi Chen Yunji Chen Cambricon Technologies Co. Ltd. China University of Chinese Academy of Sciences Beijing China SKL of Computer Architecture CAS Beijing China Institute of Automation CAS Beijing China

ISBN: (纸本)9781509035090

Neural networks (NNs) have been demonstrated to be useful in a broad range of applications such as image recognition, automatic translation and advertisement recommendation. State-of-the-art NNs are known to be both computationally and memory intensive, due to the ever-increasing deep structure, i.e., multiple layers with massive neurons and connections (i.e., synapses). Sparse neural networks have emerged as an effective solution to reduce the amount of computation and memory required. Though existing NN accelerators are able to efficiently process dense and regular networks, they cannot benefit from the reduction of synaptic weights. In this paper, we propose a novel accelerator, Cambricon-X, to exploit the sparsity and irregularity of NN models for increased efficiency. The proposed accelerator features a PE-based architecture consisting of multiple Processing Elements (PE). An Indexing Module (IM) efficiently selects and transfers needed neurons to connected PEs with reduced bandwidth requirement, while each PE stores irregular and compressed synapses for local computation in an asynchronous fashion. With 16 PEs, our accelerator is able to achieve at most 544 GOP/s in a small form factor (6.38 mm 2 and 954 mW at 65 nm). Experimental results over a number of representative sparse networks show that our accelerator achieves, on average, 7.23x speedup and 6.43x energy saving against the state-of-the-art NN accelerator.

关键词： Artificial neural networks Neurons Biological neural networks computer architecture Graphics processing units architecture Feature extraction

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