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Microwave Tomography Data Deconstruct of Spatially Diverse C-Band Scatter Components Using clustering Algorithms

IEEE ACCESS 2022年 10卷 98013-98033页

作者： Sundaram, G. A. Shanmugha Gandhiraj, R. Binoy, B. N. Harun, S., I Surya, S. N. Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Coimbatore Tamil Nadu India Amrita Vishwa Vidyapeetham SIERS Res Lab Amrita Sch Engn Coimbatore Tamil Nadu India Nanyang Technol Univ Sch Elect & Elect Engn Singapore 637457 Singapore Delft Univ Technol TU Delft Dept Microelect Microwave Sensing Signals & Syst MS3 Grp NL-2628 Delft Netherlands

Communication signals that propagate through free space are subject to multi-path interference due to scattering by various objects in the propagation channel. The effect is especially severe in complex situations in dense urban environments. To investigate the problem, a typical multi-static detection scenario is reconstructed under controlled laboratory conditions, from which suitable data sets are created. Data-driven models are then employed in EDGE computing platforms to profile the scatter centers based on the subjective manner in which they affect the signals. These have been interpreted primarily based on clustering algorithm (CA) operations- using a select suite of pre-processing models that effectively tame the variations in the C-band spatial-temporal data. A subset of the data of interest could then be subjected to an optional, compute-intensive machine learning (ML) approach. The relative advantages of the proposed method vis-a-vis an array of conventional schemes are highlighted, while also considering its carbon friendly attribute. Given the more significant association of the data to antenna radiation patterns, estimation of the latter can now be performed free of any anechoic chamber set up in a time and cost agnostic manner. The benefit of this work would lie in the realm of mid-band 5G-NR (and the future 6G) cellular communication systems deployment, where optimizing the distributed antenna location attributes on time and cost-constrained scales becomes imperative before any large-scale deployment.

关键词： 5G mobile communication Computational modeling Scattering Artificial intelligence clustering algorithms Radio frequency Distortion Microwave radio communication multi-path channels pattern clustering methods radio propagation terrain factors scattering signal processing antennas

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Uncovering Measurements of Social and Demographic Behavior From Smartphone Location Data

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IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2013年第2期43卷 188-198页

作者： Kelly, Daniel Smyth, Barry Caulfield, Brian Univ Coll Dublin Clar Ctr Sensor Web Technol Dublin 4 Ireland

Human behavior, and in particular location behavior, is highly routine based. Modern mobile phones, through global position system (GPS) technology and cell tower and WiFi location identification, enable us to trace human location behavior at scales that were previously unattainable. The goal of this paper is to examine human location behavior, through mobile phone data, and investigate if links can be made between location behavior patterns and particular demographic and social characteristics about an individual. We hypothesize that an individual's daily predictability can be key to linking their behavior to certain characteristics, and we propose predictability and geographic areas of interest models to analyze this hypothesis. Experiments reveal that measurements, which are based on our proposed location predictability models, can correctly infer 17 different characteristics about an individual with an average accuracy of 85.5%.

关键词： Global positioning system (GPS) motion analysis pattern clustering methods pattern recognition

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CUSTOM ON-DEMAND AND DATA ACCESS SERVICES FOR NEWS MERGED A-TRAIN DATA 30

CUSTOM ON-DEMAND AND DATA ACCESS SERVICES FOR NEWS MERGED A-...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Hua, Hook Fetzer, Eric Lewis, Steven Henderson, Mathew Guillaume, Alexandre Lee, Seungwon Juarez, Manuel de la Torre Dang, Van Braverman, Amy CALTECH Jet Prop Lab Pasadena CA 91109 USA

ISBN: (纸本)9781424495665

To simplify access to large and complex satellite data sets for climate analysis and model verification, we developed a web service tool that is used to study long-term and global-scale trends in climate, water and energy cycle, and weather variability. A related NASA Energy and Water Cycle Study (NEWS) task has created a merged NEWS Level 2 data from multiple instruments in NASA's A-Train constellation of satellites. We used this data to enable creation of climatologies that include correlation between observed temperature, water vapor and cloud properties from the A-Train sensors. Instead of imposing on the user an often rigid and limiting web-based analysis environment, we recognize the need for simple and well-designed distributed services so that users can perform analysis in their own familiar computing environments. A Federated OpenSearch capability with full text + space + time search of data products also facilitated interoperability with other data systems.

关键词： Information systems Meteorology pattern clustering methods Data processing Data management

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Kernel Based Hybrid Fuzzy clustering for Non-Linear Fuzzy Classifiers

Kernel Based Hybrid Fuzzy Clustering for Non-Linear Fuzzy Cl...

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Annual Meeting of the North-American-Fuzzy-Information-Processing-Society

作者： Celikyilmaz, Asli Turksen, I. Burhan Univ Calif Berkeley Dept Comp Sci Berkeley CA 94720 USA TOBB Econ & Technol Univ Ind Engn Dept Ankara Turkey Univ Toronto Med & Ind Engn Dept Toronto ON Canada

ISBN: (纸本)9781424445752

In this paper, an objective function based approach is presented to characterize a fuzzy classifier system via a kernel learning algorithms for non-linear data. We combine the distance based kernel fuzzy clustering and the non-linear support vector classification (SVC) with a conjoint objective based fuzzy clustering method in a novel way in order to learn a fuzzy classifier system. The two objectives are balanced with a regularization term. An additional merit of the novel method is that the information on natural groupings of the data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function learnt from the non-linear SVC to improve the accuracy of the classifier model. The comparative experiments demonstrate the effectiveness of the proposed method in building a classifier model for a detection system.

关键词： hybrid fuzzy clustering kernels pattern clustering methods

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VISUAL SALIENCY WITH SIDE INFORMATION

VISUAL SALIENCY WITH SIDE INFORMATION

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Jiang, Wei Xie, Lexing Chang, Shih-Fu Columbia Univ New York NY 10027 USA IBM Corp TJ Watson Res Ctr Hawthorne NY USA

ISBN: (纸本)9781424423538

We propose novel algorithms for organizing large image and video datasets using both the visual content and the associated side-information, such as time, location, authorship, and so on. Earlier research have used side-information as pre-filter before visual analysis is performed, and we design a machine learning algorithm to model the join statistics of the content and the side information. Our algorithm, Diverse-Density Contextual clustering (D2C2), starts by finding unique patterns for each sub-collection sharing the same side-info, e.g., scenes from winter. It then finds the common patterns that are shared among all subsets, e.g., persistent scenes across all seasons. These unique and common prototypes are found with Multiple Instance Learning and subsequent clustering steps. We evaluate D2C2 on two web photo collections from Flickr and one news video collection from TRECVID. Results show that not only the visual patterns found by D2C2 are intuitively salient across different seasons, locations and events, classifiers constructed from the unique and common patterns also outperform state-of-the-art bag-of-features classifiers.

关键词： pattern clustering methods Image classification

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Microwave-Assisted Magnetization Reversal in Individual Isolated Clusters of Cobalt

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IEEE TRANSACTIONS ON MAGNETICS 2008年第11期44卷 2812-2815页

作者： Raufast, Cecile Tamion, Alexandre Bernstein, Estela Dupuis, Veronique Tournier, Thierry Crozes, Thierry Bonet, Edgar Wernsdorfer, Wolfgang Univ Lyon 1 Lab Phys Mat Condensee & Nanostruct F-69622 Villeurbanne France CNRS Inst Neel F-38042 Grenoble France

At the nanometric range, the superparamagnetic state has become a crucial issue in fundamental research as well as practical applications. In both cases, technological improvements require understanding dynamical magnetization reversal processes at the nanosecond time scales. In this paper, we present the first magnetization reversal measurements performed on a single cobalt cluster (counting only 1000 spins) using the micro-SQUID technique by applying a constant magnetic field combined with a radio-frequency field pulse. First of all, we present the different technical steps necessary to detect the magnetic reversals at low temperature (T = 35 mK) of a well-defined nanoparticle prepared by low-energy clusters beam deposition. We previously showed that the 3-D switching Stoner-Wohlfarth astroid represents the magnetic anisotropy of the nanoparticle. Then, an improved device, coupled with a gold stripe line, allow us to reverse such a macrospin using an RF pulse. A qualitative understanding of the magnetization reversal by nonlinear resonance has been obtained with the Landau-Lifschitz-Gilbert equation.

关键词： Dynamic response electron beam lithography pattern clustering methods superconducting quantum interference device (SQUID) magnetometers

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Three-Dimensional LiDAR Data Classifying to Extract Road Point in Urban Area

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2008年第4期5卷 725-729页

作者： Choi, Yun-Woong Jang, Young-Woon Lee, Hyo-Jong Cho, Gi-Sung Chonbuk Natl Univ Res Ctr Ind Technol Jeonju Si 561756 South Korea

The Light Detection and Ranging (LiDAR) system is one of the best ways to accurately and effectively gather 3-D terrain information. However, it is complicated to process the LiDAR cloud data due to its irregularity and large number of collected data points. This letter proposes a novel method to automatically extract urban road network from 3-D LiDAR data. This method uses height and reflectance of LiDAR data, and clustered road point information. Geometric information of general roads is also applied to correctly extract road points group. The proposed method has been tested on various urban areas which contain complicated road networks. The results demonstrate that the integration of height, reflectance, and geometric information of roads is a crucial factor that distinguishes the proposed method in its ability to reliably and correctly classify road points.

关键词： Classification filtering Light Detection and Ranging (LiDAR) data object detection pattern clustering methods

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FOREGROUND STABILIZATION OF IMAGE SEQUENCES

FOREGROUND STABILIZATION OF IMAGE SEQUENCES

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IEEE International Conference on Multimedia and Expo (ICME 2008)

作者： Yang, Shih-Hsuan Huang, Pei-Cheng Natl Taipei Univ Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781424425709

Hand jitters result in unintentional fluctuation of image sequences taken by hand-held video cameras. Stabilization of the foreground object of interest in pictures is essential for good visual quality. In this paper, foreground stabilization algorithms of image sequences are proposed and evaluated. After performing the block-based motion estimation, grouping techniques are used to identify the foreground motion. The tested grouping techniques include the iterative centroid of foreground, the k-means clustering, and the LMedS (Least Median of Squares) algorithms. An adaptive IIR filtering technique is subsequently applied for motion correction. Compared to conventional stabilization techniques without foreground separation, the proposed methods substantially improve the visual quality of image sequences both subjectively and objectively.

关键词： Image motion analysis pattern clustering methods video cameras video signal processing

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An information-based clustering approach for fMRI activation detection

An information-based clustering approach for fMRI activation...

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5th IEEE International Symposium on Biomedical Imaging

作者： Bai, Lijun Qin, Wei Liang, Jimin Tian, Jie Xidian Univ Life Sci Res Ctr Xian Peoples R China Chinese Acad Sci Inst Automat Med Image Proc Grp Beijing 100864 Peoples R China

ISBN: (纸本)9781424420025

Most clustering algorithms in fMRI analysis implicitly require some nontrivial assumption on data structure. Due to arbitrary distribution of fMRI time series in the temporal domain, such analysis may mislead and limit the detector's performance. In this work, the authors exploited the application of an information-based clustering algorithm (Iclust) which could avoid these assumptions and provide many other benefits, such as no cluster shape restriction, no need of a prior definition about similarity measure, and the ability of capturing both linear and nonlinear dependence. Results from both artificial and real fMRI data indicated that the proposed framework could achieve better spatiotemporal accuracy, and enabled the exploration of fine furictional distinction of the human visual system in accordance with its well-known anatomy organizations.

关键词： pattern clustering methods magnetic resonance imaging

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LEARNING EFFICIENT CODES FOR 3D FACE RECOGNITION

LEARNING EFFICIENT CODES FOR 3D FACE RECOGNITION

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15th IEEE International Conference on Image Processing (ICIP 2008)

作者： Zhong, Cheng Sun, Zhenan Tan, Tieniu Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781424417650

Face representation based on the Visual Codebook becomes popular because of its excellent recognition performance, in which the critical problem is how to learn the most efficient codes to represent the facial characteristics. In this paper, we introduce the Quadtree clustering algorithm to learn the facial-codes to boost 3D face recognition performance. The merits of Quadtree clustering come from: (1) It is robust to data noises;(2) It can adaptively assign clustering centers according to the density of data distribution. We make a comparison between Quadtree and some widely used clustering methods, such as G-means, K-means, Normalized-cut and Mean-shift. Experimental results show that using the facial-codes learned by Quadtree clustering gives the best performance for 3D face recognition.

关键词： Face recognition pattern clustering methods Image texture analysis pattern recognition Image analysis

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