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Multi-sensor integration and mapping strategies for the detection and remediation of the red mud spill in Kolontar, Hungary: Estimating the thickness of the spill layer using hyperspectral imaging and Lidar

Multi-sensor integration and mapping strategies for the dete...

引用

3rd workshop on Hyperspectral Image and signal processing: Evolution in Remote Sensing, WHISPERS 2011

作者： Lenart, Csaba Burai, Peter Smailbegovic, Amer Biro, Tibor Katona, Zsolt Andricevic, Roko Karoly Robert College Hungary Photon D.o.o. Split Croatia Envirosense Hungary Ltd. Hungary Faculty of Civil Engineering and Architecture University of Split Croatia

the village of Kolontr, Hungary had became the site of one of the largest industrial spills in Europe on October 4th, 2010. the primary objective of the hyperspectral remote sensing mission was monitoring needed to estimate environmental damage, the precise size of the polluted area, the rating of substance concentration in the mud, and the overall condition of the flooded district. the secondary objective was aimed to provide geodetic data necessary for the high-resolution visual information from the data obtained with an additional Lidar survey, and for coherent modelling of the event. For quick assessment and remediation purposes, it was deemed important to estimate the thickness of the red mud, particularly the areas where the depths of the layer were more than 3cm. the results showed that some of the existing tools can be readily modified and implemented to get the most out of the available advanced remotely sensed data. © 2011 ieee.

关键词： Optical radar

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A computationally efficient robust adaptive beamforming for general-rank signal model with positive semi-definite constraint

A computationally efficient robust adaptive beamforming for ...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Arash Khabbazibasmenj Sergiy A. Vorobyov Department Electrical and Computer Engineering University of Alberta Edmonton AB Canada

the robust adaptive beamforming problem for general-rank signal model with positive semi-definite (PSD) constraint is considered. the existing approaches for solving the corresponding non-convex optimization problem are iterative methods for which the convergence is not guaranteed. Moreover, these methods solve the problem only suboptimally. We revisit this problem and develop a new beamforming method based on a new solution for the corresponding optimization problem. the new proposed method is iterative and is based on a reformulation and then linearization of a single non-convex difference-of-two-convex functions (DC) constraint. Our simulation results confirm that the new proposed method finds the global optimum of the problem in few iterations and outperforms the state-of-the-art robust adaptive beamforming methods for general-rank signal model with PSD constraint.

关键词： array signal processing Robustness Covariance matrix Interference Adaptation models Optimized production technology

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Prescient beamforming in multi-user interweave cognitive radio networks

Prescient beamforming in multi-user interweave cognitive rad...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Amitav Mukherjee A. Lee Swindlehurst Electrical Engineering and Computer Science University of California Irvine USA

this work investigates a fundamentally novel interweave cognitive radio network where the primary transmitter takes a proactive approach towards improving the accuracy of the spectrum sensing outcomes at the secondary users (SUs). For the single-primary-receiver scenario considered here, the multi-antenna primary user constructs its transmit beamforming vector so as to increase the detection probability at the SUs while meeting a desired Quality-of-Service (QoS) target on its own link, by exploiting partial channel state information of the SUs. the objective of such a proactive approach, which we refer to as prescient precoding, is to minimize the probability of interference from SUs at the primary receiver due to imperfect spectrum sensing in fading channels. Numerical results are presented to verify the advantages of the proposed prescient transmission techniques for non-cooperative spectrum sensing scenarios.

关键词： Interference sensors Receivers array signal processing signal to noise ratio Cognitive radio Radio transmitters

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Orthogonalization techniques for single group multicasting in cooperative amplify-and-forward networks

Orthogonalization techniques for single group multicasting i...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： A. Abdelkader M. Pesavento A. B. Gershman Darmstadt University of Technology Darmstadt Germany

We consider the problem of distributed beamforming in amplify-and-forward (AF) relay networks for single-group multicasting, where a single transmitter sends common information symbols to a group of users via cooperating relays. the objective is to design a computationally efficient beamforming scheme to minimize the total transmitted power at the relays subject to quality-of-service (QoS) constraints of all users. We extend the simple orthogonalization techniques, originally developed in the context of conventional broadcasting systems with instantaneous channel state information (CSI) to solve the present problem in the case when second-order statistics of the channel are available. Simulation results show that our technique outperforms the popular SDR-based technique and provides an excellent performance to complexity trade-off over a large range of QoS constraint thresholds.

关键词： Relays Vectors Multicast communication Receivers signal to noise ratio array signal processing Quality of service

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Fast LASSO based DOA tracking

Fast LASSO based DOA tracking

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Ashkan Panahi Mats Viberg Signal Processing Group Department ofSignals and Systems Chalmers University of Technology Gothenburg Sweden

In this paper, we propose a sequential, fast DOA tracking technique using the measurements of a uniform linear sensor array in the far field of a set of narrow band sources. Our approach is based on sparse approximation technique LASSO (Least Absolute Shrincage and Selection Operator), which has recently gained considerable interest for DOA and other estimation problems. Considering the LASSO optimization as a Bayesian estimation, we first define a class of prior distributions suitable for the sparse representation of the model and discuss its relation to the priors over DOAs and waveforms. Inspired by the Kalman filtering method, we introduce a nonlinear sequential filter on this family of distributions. We derive the filter for a simple random walk motion model of the DOAs. the method consists of consecutive implementation of weighted LASSO optimizations using each new measurement and updating the LASSO weights for the next step.

关键词： Direction of arrival estimation Estimation Vectors Bayesian methods Approximation methods Laplace equations arrays

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On the design and performance of TDBC-based bi-directional network beamforming

On the design and performance of TDBC-based bi-directional n...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Mohammad Zaeri-Amirani Shahram Shahbazpanahi Min Dong Faculty of Engineering and Applied Science University of Ontario Institute of Technology (UOIT) Canada

We study and compare the performance of two bi-directional relay network beamforming schemes, namely time division broadcast channel (TDBC) strategy and multiple access broadcast channel (MABC) strategy, under joint optimal power control and beamforming design. Under the presence of a direct link between the two transceivers, we first design the optimal TDBC-based bi-directional network beamformer to minimize the total power consumption in the network subject to quality of service (QoS) constraints. We obtain the optimal second-order cone programming based solution as well as fast gradient-based solution to this optimization problem. With this result, we then numerically compare the beamforming performance of the TDBC approach to that of the MABC approach developed in [1]. We show that the TDBC approach can outperform the MABC approach, under the same rate constraint, provided that the direct link is strong enough.

关键词： Transceivers Relays array signal processing Vectors signal to noise ratio Bidirectional control

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Low-power wireless sensor network with compressed sensing theory

Low-power wireless sensor network with compressed sensing th...

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Caneus Fly By Wireless workshop

作者： Balouchestani, Mohammadreza Department of Electrical and Computer Engineering Ryerson University Toronto Canada

ISBN: (纸本)9781457709715

Wireless sensor Networks (WSNs) have application in a variety of fields including inhospital locations, military purposes, transportation automation, home and industrial automation. WSNs also are used in monitoring synchronous or asynchronous events that require periodic data collection. WSNs consist of a large number small device or Wireless Nodes (WNs) and are responsible for sensing, collecting, processing and monitoring information of real world environments. WSNs consist of a Data Acquisition Network (DAN) and a Data distribution Network (DDN) which monitored and controlled by a management center. the primary limiting factor for the lifetime of a WSN is the power supply. Regarding the applications of WSNs it is often impossible to obtain physical access to replace or charge battery. therefore we can design low power WSNs. In WSNs, the events are sparse signal compared with the number of sources. that is why;the compressed sensing theory holds promising to reduce power consumption. Compressed Sensing shows that spars signals such as signals of WSNs can be exactly reconstructed from a small number of random linear measurements. Compressed Sensing theory can reduce number of bits information through whole of the network and consequently decrease amount of current that drawn from power supply. With this in mind, we introduce a new mechanism to design low-power WSN with compressed sensing theory. this paper gives a background of compressed sensing theory, and then describes important concepts in wireless sensor networks, and finally our simulation by applying compressed sensing in WSNs theory is described. © 2011 ieee.

关键词： Compressed sensing

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A least squares algorithm for global joint decomposition of complex matrix sets

A least squares algorithm for global joint decomposition of ...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Tual Trainini Eric Moreau LSEET UMR CNRS 6017 Université du Sud Toulon Var La Valette-du-Var France

this paper deals with a new approach for the joint decomposition of complex matrix sets. Such problems arise naturally in various signal processing problems, among which the blind source separation one. the suggested algorithm is based on an Alternating Least Square (ALS) optimization procedure. An improved version is also proposed including a global Enhanced Line Search (ELS) in the recursive procedure. In practice, the main interest of our approach is to take advantage of a greater amount of signal information within the same context, since sets of Hermitian and symmetric complex matrices are combined altogether. Simulations are performed to highlight the advantages of this method as compared to other existing algorithms.

关键词： Matrix decomposition Joints signal processing algorithms Indexes Monte Carlo methods Symmetric matrices signal to noise ratio

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A glance on geographical positioning

A glance on geographical positioning

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Jonathan Bosse Anne Ferréol Pascal Larzabal SATIE ENS Cachan CNRS Universud Cachan France THALES Communication Colombes France

Geographical positioning has attracted considerable interest during the last decades due to the potential of such an information for civilian applications as well as for military applications. this paper recalls the main issues and solutions in some localization problems depending on the experimental context. It offers a brief review and evokes recent directions in which studies are led. this paper is an introduction paper for the special session “Advances in geographical positionning” in CAMSAP 2011. It provides a common framework for the research papers which will be presented in the session.

关键词： Estimation signal processing Context sensor arrays Base stations Conferences

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Localization with multicomponent seismic array

Localization with multicomponent seismic array

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： L.A. Inza J.I. Mars J.P. Métaxian G.S. O'Brien O. Macedo Institut des Sciences de la Terre IRD R219 CNRS Le Bourget du Lac France Instituto Geofisico del Peru Lima Peru Dept Image and Signal Grenoble Institute of Technology Saint Martin dHéres France School of Geological Sciences University College Dublin Ireland

Seismo-volcano source localization is essential to improve our understanding of volcano systems. the lack of clear seismic wave phases prohibits the use of classical location methods. Seismic antennas composed of one-component (1C) seismometers provide a good estimate of the back-azimuth of the waveeld. the depth estimation, on the other hand, is difficult or impossible to determine. In order to determine the source location parameters (back-azimuth and depth), we extend the 1C seismic antenna approach to 3Cs. this communication discusses a high-resolution location method using a 3C array survey (3C-MUSIC algorithm) with data from two seismic antennas installed on an andesitic volcano in Peru (Ubinas volcano). After introducing the 3C MUSIC processing, we evaluate the robustness of the location method on a full waveeld 3D synthetic dataset generated using a digital elevation model of Ubinas volcano and an homogeneous velocity model. Results show that the back-azimuth determined using the 3C array has a smaller error than a 1C array. Only the 3C method allows the recovery of the source depths. Finally, we applied the 3C-MUSIC to two seismic events recorded in 2009. therefore, extending 1C arrays to 3C arrays in volcano monitoring allows a more accurate determination of the source epicenter and now an estimate for the depth.

关键词： arrays Antennas Volcanoes Multiple signal classification sensors Explosions Earthquakes

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