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35th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Labra, Nicole Figueroa, Miguel Guevara, Pamela Duclap, Delphine Hoeunou, Josselin Poupon, Cyril Mangin, Jean-Francois Univ Concepcion Dept Elect Engn Concepcion Chile Univ Concepcion Ctr Opt & Photon Concepcion Chile CEA I2BM Neurospin Gif Sur Yvette France INSERM U955 Unit Paris France

ISBN: (纸本)9781457702167

This paper presents a parallel implementation of an algorithm for automatic segmentation of white matter fibers from tractography data. We execute the algorithm in parallel using a high-end video card with a Graphics processing Unit (GPU) as a computation accelerator, using the CUDA language. By exploiting the parallelism and the properties of the memory hierarchy available on the GPU, we obtain a speedup in execution time of 33.6 with respect to an optimized sequential version of the algorithm written in C, and of 240 with respect to the original Python/C++ implementation. The execution time is reduced from more than two hours to only 35 seconds for a subject dataset of 800,000 fibers, thus enabling applications that use interactive segmentation and visualization of small to medium-sized tractography datasets.

关键词： C++ language biomedical MRI brain graphics processing units image segmentation medical image processing parallel architectures parallel languages video signal processing

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一种舰载毫米波雷达海杂波背景下的慢速小目标检测算法研究

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舰船电子对抗 2014年第6期37卷 60-62,68页

作者：洪伟李红虎中国船舶重工集团公司第723研究所扬州225001

掠海飞行慢速小目标的检测一直是雷达信号处理领域研究的热点问题。分析了海杂波数据的统计特性,利用天线扫描间海杂波非相关特性,采取单次扫描脉冲间非相参积累,结合天线扫描间积累和杂波图迭代平滑处理技术,抑制海杂波,积累目标能量,... 详细信息

掠海飞行慢速小目标的检测一直是雷达信号处理领域研究的热点问题。分析了海杂波数据的统计特性,利用天线扫描间海杂波非相关特性,采取单次扫描脉冲间非相参积累,结合天线扫描间积累和杂波图迭代平滑处理技术,抑制海杂波,积累目标能量,改善检测性能。仿真结果表明利用本方法毫米波雷达能有效检测海杂波环境下的慢速小目标。

关键词：舰载毫米波雷达海杂波慢速小目标目标检测

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Through-the-Wall Localization of a Moving Target by Two Independent Ultra Wideband (UWB) Radar Systems

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SENSORS 2013年第9期13卷 11969-11997页

作者： Kocur, Dusan Svecova, Maria Rovnakova, Jana Tech Univ Kosice Dept Elect & Multimedia Commun Kosice 04120 Slovakia

In the case of through-the-wall localization of moving targets by ultra wideband (UWB) radars, there are applications in which handheld sensors equipped only with one transmitting and two receiving antennas are applied. Sometimes, the radar using such a small antenna array is not able to localize the target with the required accuracy. With a view to improve through-the-wall target localization, cooperative positioning based on a fusion of data retrieved from two independent radar systems can be used. In this paper, the novel method of the cooperative localization referred to as joining intersections of the ellipses is introduced. This method is based on a geometrical interpretation of target localization where the target position is estimated using a properly created cluster of the ellipse intersections representing potential positions of the target. The performance of the proposed method is compared with the direct calculation method and two alternative methods of cooperative localization using data obtained by measurements with the M-sequence UWB radars. The direct calculation method is applied for the target localization by particular radar systems. As alternative methods of cooperative localization, the arithmetic average of the target coordinates estimated by two single independent UWB radars and the Taylor series method is considered.

关键词： collaborative localization direct calculation method ellipse intersections radar signal processing target localization Taylor series method UWB radar

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Quantifying Neural Coding Noise in Linear Threshold Models

Quantifying Neural Coding Noise in Linear Threshold Models

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6th International IEEE EMBS conference on Neural Engineering (NER)

作者： Meyer, Arne F. Diepenbrock, Jan-Philipp Ohl, Frank W. Anemueller, Joern Carl von Ossietzky Univ Oldenburg Cluster Excellence Hearing4all Dept Med Phys & Acoust D-26111 Oldenburg Germany

ISBN: (纸本)9781467319690

Neurons integrating sensory stimulus features are found in many brain areas up to cortical level, and their understanding is essential for building and improving neural prostheses. Here, we focus on auditory neural coding in the inferior colliculus (IC). Much work has been conducted to identify the primary spectro-temporal sound features. However, a description of how reliable these features are encoded at IC level remains an open question. In a simplified model, the encoding process can be described by a linear integrator followed by a threshold nonlinearity that creates a binary spike sequence. To account for variability in neural responses, coding noise has to be taken into account. However, coding noise reduces the certainty about the stimulus features encoded. Traditional approaches to quantify the amount of noise based on information theory are prone to sampling bias and cannot be bounded in a simply way, making it hard to interpret the results obtained and to compare them across neural populations. Here, we reformulate neural coding as a spike detection task and show that methods from signal detection theory allow an alternative description to quantify coding noise. Using neural responses from the IC in Mongolian gerbils to acoustic stimuli, we demonstrate that this approach allows a reliable description of neural coding noise, particularly in the small data limit, while being highly correlated with information-theoretic quantities in the large-data regime.

关键词： bioelectric phenomena brain encoding medical signal detection medical signal processing neurophysiology prosthetics signal denoising

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HMM-based Snorer Group Recognition for Sleep Apnea Diagnosis

HMM-based Snorer Group Recognition for Sleep Apnea Diagnosis

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35th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Herath, Dulip L. Abeyratne, Udantha R. Hukins, Craig Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Princess Alexandra Hosp Dept Respiratory & Sleep Med Brisbane Qld 4102 Australia

ISBN: (纸本)9781457702167

This paper presents an Hidden Markov Models (HMM)-based snorer group recognition approach for Obstructive Sleep Apenea diagnosis. It models the spatio-temporal characteristics of different snorer groups belonging to different genders and AHI severity levels. The current experiment includes selecting snore data from subjects, identifying snorer groups based on gender and AHI values (AHI < 15 and AHI > 15), detecting snore episodes, MFCC computation, training and testing HMMs. A set of multi-level classification rules is employed for incremental diagnosis of OSA. The proposed method, with a relatively small data set, produces results nearly comparable to any existing methods with single feature class. It classifies snore episodes with 62.0% (male), 67.0% (female) and recognizes snorer group with 78.5% accuracy. The approach makes its diagnosis decision at 85.7% (sensitivity), 71.4% (specificity) for males and 85.7% (sensitivity and specificity) for females.

关键词： hidden Markov models medical disorders medical signal processing patient diagnosis pneumodynamics signal classification spatiotemporal phenomena

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Model-Based Software Engineering for an Imaging CubeSat and its Extrapolation to Other Missions

Model-Based Software Engineering for an Imaging CubeSat and ...

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IEEE Aerospace conference

作者： Mohammad, Atif Straub, Jeremy Korvald, Christoffer Grant, Emanuel Univ N Dakota Dept Comp Sci Grand Forks ND 58202 USA

ISBN: (纸本)9781467318112

small satellites with their limited computational capabilities require that software engineering techniques promote efficient use of spacecraft resources. A model-driven approach to software engineering is an excellent solution to this resource maximization challenge as it facilitates visualization of the key solution processes and data elements. The software engineering process utilized for the OpenOrbiter spacecraft, which is a remote sensing technology demonstrator, is presented. Key challenges presented by the Open Orbiter project included concurrent operation and tasking of five computer-on-module (COM) units and a flight computer and the associated data marshaling between local and general storage. The payload processing system (consisting of the five COM units) would determine (based on high-level ground controller instructions) targets of interest which it then tasks to the operating software to effect image capture of. The operating software, in addition to satisfying the aforementioned tasks from the payload software, must also monitor spacecraft health, status and operations and control the activities of the various subsystems (e. g., communications, attitude determination and control, etc.). One of the principal challenges with this highly-distributed architecture is to ensure that tasks are reasonably evenly distributed between different elements of the computing units, such that a single unit is not acting as a bottleneck for other onboard computers' tasks. A work distribution model was created to characterize the system needs and facilitate high-level task-type assignment between the computing systems. A communications model was also created to characterize the level of data throughput required (based on user needs) and possible (based on several communications approaches) to the ground. This allowed selection of the most optimal communications approach and, thus, allowed maximization of each communications opportunity. This paper presents the model-b

关键词： data visualization

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Denoising of gravity anomaly data considering statistical independence of signals

Denoising of gravity anomaly data considering statistical in...

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11th International conference on Structural Safety and Reliability, ICOSSAR 2013

作者： Honda, Riki Khatri, Prem Prakash Morikawa, Hitoshi Department of International Studies University of Tokyo Japan Department of Civil Engineering University of Tokyo Japan Department of Built Environment Tokyo Institute of Technology Japan

ISBN: (纸本)9781138000865

For the efficient measurement of gravity anomaly which is used for the estimation of ground structure, compact gravity observation system using force-balance (FB) accelerometer is presented. It has a problem that the observed data is severely contaminated by various kinds of disturbances. This paper presents a scheme to extract the gravity anomaly signal from the noise-contaminated observed data, by exploiting the statistical independent property of gravity anomaly data and other noise signals. Since statistical independence is not affected by the amplitude of signals, it can deal with the gravity data with small amplitude and noise signal with large amplitude. Two methods for the blind source separation;Second Order Blind Identification method (SOBI) and Independent Component Analysis (ICA) are investigated. It was found that under certain conditions, the proposed method was able to salvage the gravity anomaly data from the observed data with the accuracy sufficient for the purpose of identification of gravity anomaly distribution. Its performance is affected by various factors such as the motion of the carrier vessel influences. For efficient removal of noise signals, appropriate pre-processing of the observed data is essential. © 2013 Taylor & Francis Group, London.

关键词： Blind source separation

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Optimal Vision System Design for Characterization of Apples Using US/VIS/NIR Spectroscopy data

Optimal Vision System Design for Characterization of Apples ...

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20th International conference on Systems, signals and Image processing (IWSSIP)

作者： Sharifzadeh, Sara Clemmensen, Line H. Ersboll, Bjarne K. Vega, Mabel V. Martinez Tech Univ Denmark Dept Math & Comp Sci Copenhagen Denmark Univ Copenhagen Dept Plant & Environm Sci Copenhagen Denmark

ISBN: (纸本)9781479909414;9781479909421

Quality monitoring of the food items by spectroscopy provides information in a large number of wavelengths including highly correlated and redundant information. Although increasing the information, the increase in the number of wavelengths causes the vision set-up to be more complex and expensive. In this paper, three sparse regression methods;lasso, elastic-net and fused lasso are employed for estimation of the chemical and physical characteristics of one apple cultivar using their high dimensional spectroscopic measurements. The use of sparse regression reduces the number of required wavelengths for prediction and thus, simplifies the required vision set-up. It is shown that, considering a tradeoff between the number of selected bands and the corresponding validation performance during the training step can result in a significant reduction in the number of bands at a small price in the test performance. Furthermore, appropriate regression methods for different number of bands and spectrophotometer design are determined.

关键词： Sparse regression spectroscopy lasso elastic-net fused lasso

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Millimeter Wave Radar for Automotive Blind Spot Detection System

Millimeter Wave Radar for Automotive Blind Spot Detection Sy...

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4th International conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Li, Shouxiao Cao, Yunxia Bi, Xin Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Liaoning Provin Peoples R China

ISBN: (纸本)9783037856932

Considering the problem of rearview mirror blind spot during driving, the paper studied and designed the blind spot detection system based on MMW radar. Radar was installed at an appropriate position on the detection target signal by transmitting, when another car enter the detecting area, the small alarm light beside A pillar would shine or alarm few times, to remind drivers careful change road. And the effect would not effect by weather or time. For the radar sensor application environment, triangle wave LFMCW can effectively solve the speed from the coupling phenomenon. The paper showed experimental and simulation data.

关键词： Millimeters radar sensor Blind spot FMCW signal processing

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Blind Analysis of Multispectral and Polarimetric data via Canonical Correlation Analysis

Blind Analysis of Multispectral and Polarimetric Data via Ca...

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conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX

作者： Polat, Ozgur Murat Ozkazanc, Yakup ASELSAN Microelect Guidance & Electroopt Div Ankara Turkey Hacettepe Univ Dept Elect & Elect Engn Ankara Turkey

ISBN: (纸本)9780819495341

In blind scene analysis, the aim is to obtain information about background and targets without any prior information Blind methods can be considered as pre-processing steps for scene understanding. By means of blind signal separation methodologies, anomalies can be detected and these anomalies can be exploited for target detection. There are many imaging sensor systems which uses different properties of the emittance or the reflectance characteristics of the scene components. Spectral reflectance properties are related to the material composition and these multispectral characteristics can be exploited for detection, identification and classification of the scene components. As the light scattered from the scene elements shows polarization, polarized measurements can be used as extra features. Multispectral and polarimetric images of a scene provide information to some level and this information can be used to get further information on the scene and to facilitate detection. In this study, spectral and polarimetric images of a scene are analyzed via Canonical Correlation Analysis (CCA) which is a powerful multivariate statistical methodology. Multispectral and polarimetric data (spectro-polarimetric data) are treated as two different sets. Canonical variants obtained by CCA give different scene components such as background elements and some man-made objects. The linear relationship of the polarimetric and multispectral data of the same scene is also obtained by CCA.

关键词： Multispectral data polarimetric data canonical correlation analysis

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