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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets"

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Radar high-resolution range profile recognition via multi-SV method

Radar high-resolution range profile recognition via multi-SV...

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IET International Radar conference 2018, IRC 2018

作者： Li, Long Jiang, Jie Liu, Zheng Xi'an Research Institute of Navigation Technology Xi'an China National Laboratory of Radar Signal Processing Xidian University Xi'an China

The radar high-resolution range profile (HRRP) is of great significance for target recognition in the ground compound environment. As two essential processes of HRRP target recognition, discrimination and classification performance improvement are studied in this study. A novel target recognition method is designed, denoted by multiple support vector (SV) method. In the training phase, a multiple correlate SV model is constructed, which separates the multi-target feature space into several central-coherence distributed regions. Each region has been described by the modified SV method, and the category determination is based on the description of multiple regions. This method can overcome the issues of both outlier discrimination and targets classification. Due to the hierarchical process in the testing phase, this method needs less memory and calculation to fit the practical radar target recognition task. Finally, the experiment based on the measured data verifies the excellent performance of this method. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Radar target recognition

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Development and feasibility testing of an acoustic buried cable detection system

Development and feasibility testing of an acoustic buried ca...

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OCEANS

作者： S. Szender M.J. Kosalos Naval and Maritime Systems Mukilteo Operations Hughes Aircraft Company Mukilteo WA USA

Minehunting, ordinance disposal, cable and pipeline relocation, and route survey operations often require the ability to detect and locate objects buried in the seafloor. A high-resolution acoustic subbottom imaging system capable of locating targets buried up to three meters deep, from altitudes of ten-meters or more in a variety of sediments has been tested. The system uses bistatic geometry, wide bandwidth, multiple-beam processing, and multiple-aspect oversampling to create profile imagery of the subbottom with centimeter-level resolution. Multi-aspect data were examined separately and found to provide target classification information using predictable backscatter pattern differences between linear targets (uniform cylindrical cables and pipelines) and small and large discreet lump scatterers. The system uses a separated wide-band transmitter, a poly-vinyl difluoride (PVDF) line-array hydrophone, and a data acquisition system. Sixteen individual channels of data were continuously recorded enabling the formation of steered multiple beams for multiple-aspect examination of target backscatter characteristics in post-processing. Tests were conducted using small buried cylindrical targets ranging in diameter from 12 to 40 millimeters as well as targets-of-opportunity in the sediments. Cylindrical target characteristics and sediment characteristics within a Puget Sound test range were measured independently. Detailed backscatter measurements for a subset of these targets were acquired in an acoustic test tank prior to burial of targets in a natural environment.

关键词： Acoustic testing Sediments Backscatter Pipelines Acoustic measurements Weapons Acoustic signal detection Buried object detection Sea floor Acoustic imaging

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Refocusing of moving targets in Spotlight SAR raw data

Refocusing of moving targets in Spotlight SAR raw data

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IEEE signal processing and Communications Applications (SIU)

作者： İbrahim Papila Selçuk Paker Mesut Kartal Sedef Kent Elektronik ve Haberleşme Mühendisliği Bölümü İstanbul Teknik Üniversitesi İstanbul Türkiye

In this paper a method for moving targets detection based on Spotlight Synthetic Aperture Radar (SAR) images is proposed and tested in simulation data. To this purpose two moving target detection schemes are considered: The first is based on a bank of Chirp Scaling Algorithms (CSAs), and the second is based on Change Detection (CD) applied to sub-images obtained by splitting the overall aperture into sub-apertures. At the next step, selecting and arranging target focusing center with a new technique based on re-centering phase computation to a reference point for each moving target. Finally Polar Format Algorithm (PFA) is applied to each re-organized raw data to obtain highly focused moving targets individually.

关键词： Synthetic aperture radar Chirp Azimuth Focusing Change detection algorithms Object detection Target tracking

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Performance Evaluation of Convolutional Neural Network at Hyperspectral and Multispectral Resolution for Classification 25

Performance Evaluation of Convolutional Neural Network at Hy...

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conference on Image and signal processing for Remote Sensing XXV

作者： Paul, Subir Vinayaraj, Poliyapram Kumar, D. Nagesh Nakamura, Ryosuke Indian Inst Sci Dept Civil Engn Bengaluru 560012 India Natl Inst Adv Ind Sci & Technol Tokyo Tech Real World Big Data Computat Open Inno Tokyo 1528550 Japan Indian Inst Sci Ctr Earth Sci Bengaluru 560012 India Natl Inst Adv Ind Sci & Technol Tokyo 1350064 Japan

ISBN: (数字)9781510630147

ISBN: (纸本)9781510630147

Convolutional Neural Network (CNN) has established as an effective deep learning model for hyperspectral image classification by considering both spectral and spatial information. In this study, the performance of two-dimensional (2D) CNN architecture is evaluated at hyperspectral and multispectral resolution. Two types of multispectral data are analyzed viz., original and transformed multispectral data. Hyperspectral bands are transformed to spectral resolution of multispectral bands by averaging the reflectances of specific hyperspectral narrow bands which are falling within the spectral ranges of multispectral bands. The well-known Pavia University dataset and a new dataset of Pear orchard are investigated in this study. In case of Pear orchard dataset, classification is performed with both types of multispectral data. All the experiments are carried out with the same 2D CNN architecture. In case of Pavia University dataset, hyperspectral and transformed multispectral data achieve OA(%) of 94.29 +/- 1.28 and 94.27 +/- 2.01 respectively considering 20% samples as training. In case of Pear orchard dataset, hyperspectral, multispectral and transformed multispectral data achieve OA(%) of 91.59 +/- 0.89, 88.65 +/- 1.35, and 93.24 +/- 0.16 respectively considering 20% samples as training. It is evident that transformed multispectral data, which comprises of inherent hyperspectral information, provides similar or better performance compared to hyperspectral data. Further, with the use of 3D CNN architecture, classification performance improves in case of Pavia University dataset, whereas it remains statistically similar in case of Pear orchard dataset. The present promising results illustrates the performance of CNN even in small dataset which is comparable to several published state-of-the art results on the same dataset.

关键词： Convolutional neural network deep learning hyperspectral data multispectral data spectral-spatial classification 2D CNN 3D CNN

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Sub-pixel detection of active targets

Sub-pixel detection of active targets

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IEEE Pacific Rim conference on Communications, Computers and signal processing

作者： S. Seelemann G. McLean Mechanical Engineering University of Victoria Victoria BC Canada

The problem of accurately detecting the location of active targets using low-cost machine vision systems is presented, with a requirement for location accuracies greater than 0.5 pixels. Methods of locating bloblike images of active targets are presented, including the calculation of the centroid of binarized versions of the blob, and the fitting of a parametric model to the blobs intensity distribution. Both methods are evaluated experimentally with simulated data and found to provide excellent blob localization. A physical experiment was conducted to evaluate the location accuracy, leading to the conclusion that the performance of the two methods is equivalent, with both providing the capability of locating active targets to an accuracy of 0.25 pixels.< >

关键词： Pixel Machine vision Quantization Navigation Marine vehicles Additive noise Mechanical engineering Costs Parametric statistics Image sampling

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Support vector machine classifications for microarray expression data set 5

Support vector machine classifications for microarray expres...

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5th International conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003

作者： Zhang, Junying Lee, R. Wang, Y.J. National Key Lab. of Radar Signal Processing Xidian University Xi'an China Department of Electrical Engineering and Computer Science Catholic University of America WashingtonDC United States Lombardi Cancer Center Georgetown university WashingtonDC United States

ISBN: (纸本)0769519571

Gene selection, cancer classification and functional gene classification are three main concerns and interests by biologists for cancer detection, cancer classification, and understanding the functions of genes from the molecular level of tissues, where the large number of genes and relatively small number of experiments in gene expression data generate a great challenge. After a brief introduction of support vector machine(SVM) for classification, this paper presents recent SVM approaches for gene selection, cancer classification and functional gene classification followed by analysis on the advantages and limitations of SVM on these applications. © 2003 IEEE.

关键词： Support vector machines

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WALL-CLUTTER MITIGATION USING CROSS-BEAMFORMING IN THROUGH-THE-WALL RADAR IMAGING

WALL-CLUTTER MITIGATION USING CROSS-BEAMFORMING IN THROUGH-T...

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European signal processing conference

作者： Christian Debes Christian Weiss Abdelhak M. Zoubir Moeness G. Amin Signal Processing Group Technische Universitat Darmstadt Center for Advanced Communications Villanova University

A new technique for removal of the wall EM returns in Through-the-Wall Radar Imaging is presented. It is based on the spatial notch-filtering to separate wall and target reflections. The proposed technique forms squint beams at the receiver using a divided aperture. In doing so, it removes the strong wall signature without eliminating those of the targets. The proposed scheme provides desirable 3D target detection which is evaluated using real data examples from Through-the-Wall radar imaging experiments.

关键词： wall radar Radar imaging new technique targets observational data Imaging Eliminate Receivers Strabismus

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Effective data acquisition for machine learning algorithm in EEG signal processing 1st

引用

1st International conference on Soft Computing: Theories and Applications, SoCTA 2016

作者： Bonello, James Garg, Lalit Garg, Gaurav Audu, Eliazar Elisha University of Malta Msida Malta University of Liverpool Liverpool United Kingdom University of Ulster Magee Campus Londonderry United Kingdom

ISBN: (纸本)9789811056987

The aim of this paper is to demonstrate that small dataset can be used in machine learning for seizure monitoring and detection using smart organization of multichannel EEG sensor data. This reduces training time and improves computational performance in terms of space and time complexities on hardware implementations. The proposed approach has been tested and validated using CHB-MIT dataset containing EEG recordings of 24 clinically verified seizure and non-seizure pediatric patients. The predictability is discussed in terms of the latency and the required length of data for the proposed approach over the state-of-the-art method in the field of EEG-based seizure prediction. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease

Application of the empirical mode decomposition to the analy...

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conference Proceedings - 26th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004

作者： Liang, Hualou Lin, Qiu-Hua Chen, J.D.Z. Sch. of Health Information Sciences University of Texas at Houston Houston TX 77030 United States Sch. of Electron. and Info. Eng. Dalian University of Technology Dalian 116023 China Division of Gastroenterology University of Texas Medical Branch Galveston TX 77555 United States

The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and non-stationary time series. The central idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode functions (IMFs). An IMF is defined as any function having the number of extrema and the number of zero-crossings equal (or differing at most by one), and also having symmetric envelopes defined by the local minima, and maxima respectively. The decomposition procedure is adaptive, data-driven, therefore, highly efficient. In this paper, the EMD is first described, and its performance is validated by simulations. The EMD is then applied to the analysis of esophageal manometric time series in gastroesophageal reflux disease. The results show that the EMD may prove to be a vital technique for the analysis of esophageal manometric data.

关键词： Gastroenterology

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Automatic Gait Phases Detection in Parkinson Disease: A Comparative Study 42

Automatic Gait Phases Detection in Parkinson Disease: A Comp...

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42nd Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Castano-Pino, Yor J. Gonzalez, Maria C. Quintana-Pena, Valentina Valderrama, Jaime Munoz, Beatriz Orozco, Jorge Navarro (SM), Andres CENIT i2t Res Ctr Cali Colombia Univ ICESI Cali Colombia Stavanger Univ Hosp Stavanger Ctr Age Related Med SESAM Stavanger Norway Fdn Valle Lili Ctr Invest Clin CIC Cali Colombia Fdn Valle Lili Neurol Dept Cali Colombia

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119908

Background: Parkinson's disease (PD) is a chronic condition that can be diagnosed and monitored by evaluating changes in the gait and arm movement parameters. In the gait movement, each cycle consists of two phases: stance and swing. Using gait analysis techniques, it is possible to get spatiotemporal variables derived from both phases. Objective: In this paper, we compared two techniques: wavelet and peak detection. Previously, the wavelet technique was assessed for the gait phases detection, and peak detection was evaluated for arm swing analysis. These methods were evaluated using a low-cost RGB-D camera as data input source. This comparison could provide a unified and integrated method to analyze gait and arm swing signals. Methods: Twenty-five PD patients and 25 age-matched, healthy subjects were included. Mann-Whitney U test was used to compare the continuous variables between groups. Hamming distances and Spearman rank correlation were used to evaluate the agreement between the signals and the spatiotemporal variables obtained by both methods. Results: PD group showed significant reductions in speed (wavelet p=0.001, peak detection p<0.001) and significantly greater swing (wavelet p=0.003, peak detection p=0.005) and stance times (wavelet p=0.003, peak detection p=0.004). Hamming distances showed small differences between the signals obtained by both methods (16 to 18 signal points). A very strong correlation (Spearman rho > 0.8, p<0.05) was found between the spatiotemporal variables obtained by each signal processing technique. Conclusion: Wavelet and peak detection techniques showed a high agreement in the signal obtained from gait data. The spatiotemporal variables obtained by both methods showed significant differences between the walking patterns of PD patients and healthy subjects. The peak detection technique can be used for integral motion analysis, providing the identification of the phases in the gait cycle, and arm swing parameters.

关键词： Wavelet analysis Cameras Legged locomotion Wavelet transforms Spatiotemporal phenomena signal processing Correlation

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