Efficient collaborative sparse channel estimation in massive MIMO

KAUST Repository

Masood, Mudassir

2015-08-12

We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.

Efficient collaborative sparse channel estimation in massive MIMO

KAUST Repository

Masood, Mudassir; Afify, Laila H.; Al-Naffouri, Tareq Y.

2015-01-01

We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.

Partial PIC-MRC Receiver Design for Single Carrier Block Transmission System over Multipath Fading Channels

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Juinn-Horng Deng

2012-01-01

Full Text Available Single carrier block transmission (SCBT system has become one of the most popular modulation systems due to its low peak-to-average power ratio (PAPR, and it is gradually considered to be used for uplink wireless communication systems. In this paper, a low complexity partial parallel interference cancellation (PIC with maximum ratio combining (MRC technology is proposed to use for receiver to combat the intersymbol interference (ISI problem over multipath fading channel. With the aid of MRC scheme, the proposed partial PIC technique can effectively perform the interference cancellation and acquire the benefit of time diversity gain. Finally, the proposed system can be extended to use for multiple antenna systems to provide excellent performance. Simulation results reveal that the proposed low complexity partial PIC-MRC SIMO system can provide robust performance and outperform the conventional PIC and the iterative frequency domain decision feedback equalizer (FD-DFE systems over multipath fading channel environment.

Multipath Detection Using Boolean Satisfiability Techniques

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Fadi A. Aloul

2011-01-01

Full Text Available A new technique for multipath detection in wideband mobile radio systems is presented. The proposed scheme is based on an intelligent search algorithm using Boolean Satisfiability (SAT techniques to search through the uncertainty region of the multipath delays. The SAT-based scheme utilizes the known structure of the transmitted wideband signal, for example, pseudo-random (PN code, to effectively search through the entire space by eliminating subspaces that do not contain a possible solution. The paper presents a framework for modeling the multipath detection problem as a SAT application. It also provides simulation results that demonstrate the effectiveness of the proposed scheme in detecting the multipath components in frequency-selective Rayleigh fading channels.

A Robust Parametric Technique for Multipath Channel Estimation in the Uplink of a DS-CDMA System

Directory of Open Access Journals (Sweden)

2006-01-01

Full Text Available The problem of estimating the multipath channel parameters of a new user entering the uplink of an asynchronous direct sequence-code division multiple access (DS-CDMA system is addressed. The problem is described via a least squares (LS cost function with a rich structure. This cost function, which is nonlinear with respect to the time delays and linear with respect to the gains of the multipath channel, is proved to be approximately decoupled in terms of the path delays. Due to this structure, an iterative procedure of 1D searches is adequate for time delays estimation. The resulting method is computationally efficient, does not require any specific pilot signal, and performs well for a small number of training symbols. Simulation results show that the proposed technique offers a better estimation accuracy compared to existing related methods, and is robust to multiple access interference.

Analysis of multipath channel fading techniques in wireless communication systems

Science.gov (United States)

Mahender, Kommabatla; Kumar, Tipparti Anil; Ramesh, K. S.

2018-04-01

Multipath fading occurs in any environment where there is multipath propagation and there is some movement of elements within the radio communications system. This may include the radio transmitter or receiver position, or in the elements that give rise to the reflections. The multipath fading can often be relatively deep, i.e. the signals fade completely away, whereas at other times the fading may not cause the signal to fall below a useable strength. Multipath fading may also cause distortion to the radio signal. As the various paths that can be taken by the signals vary in length, the signal transmitted at a particular instance will arrive at the receiver over a spread of times. This can cause problems with phase distortion and inter symbol interference when data transmissions are made. As a result, it may be necessary to incorporate features within the radio communications system that enables the effects of these problems to be minimized. This paper analyses the effects of various types of multipath fading in wireless transmission system.

Performance Analysis of a Six-Port Receiver in a WCDMA Communication System including a Multipath Fading Channel

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A. O. Olopade

2014-01-01

Full Text Available Third generation communication systems require receivers with wide bandwidth of operation to support high transmission rates and are also reconfigurable to support various communication standards with different frequency bands. An ideal software defined radio (SDR will be the absolute answer to this requirement but it is not achievable with the current level of technology. This paper proposes the use of a six-port receiver (SPR front-end (FE in a WCDMA communication system. A WCDMA end-to-end physical layer MATLAB demo which includes a multipath channel distortion block is used to determine the viability of the six-port based receiver. The WCDMA signal after passing through a multipath channel is received using a constructed SPR FE. The baseband signal is then calibrated and corrected in MATLAB. The six-port receiver performance is measured in terms of bit error rate (BER. The signal-to-noise ratio (SNR of the transmitted IQ data is varied and the BER profile of the communication system is plotted. The effect of the multipath fading on the receiver performance and the accuracy of the calibration algorithm are obtained by comparing two different measured BER curves for different calibration techniques to the simulated BER curve of an ideal receiver.

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels.

Science.gov (United States)

Chen, S; Samingan, A K; Hanzo, L

2001-01-01

The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.

Channel Estimation in DCT-Based OFDM

Science.gov (United States)

Wang, Yulin; Zhang, Gengxin; Xie, Zhidong; Hu, Jing

2014-01-01

This paper derives the channel estimation of a discrete cosine transform- (DCT-) based orthogonal frequency-division multiplexing (OFDM) system over a frequency-selective multipath fading channel. Channel estimation has been proved to improve system throughput and performance by allowing for coherent demodulation. Pilot-aided methods are traditionally used to learn the channel response. Least square (LS) and mean square error estimators (MMSE) are investigated. We also study a compressed sensing (CS) based channel estimation, which takes the sparse property of wireless channel into account. Simulation results have shown that the CS based channel estimation is expected to have better performance than LS. However MMSE can achieve optimal performance because of prior knowledge of the channel statistic. PMID:24757439

Chaos-based wireless communication resisting multipath effects

Science.gov (United States)

Yao, Jun-Liang; Li, Chen; Ren, Hai-Peng; Grebogi, Celso

2017-09-01

In additive white Gaussian noise channel, chaos has been shown to be the optimal coherent communication waveform in the sense of using a very simple matched filter to maximize the signal-to-noise ratio. Recently, Lyapunov exponent spectrum of the chaotic signals after being transmitted through a wireless channel has been shown to be unaltered, paving the way for wireless communication using chaos. In wireless communication systems, inter-symbol interference caused by multipath propagation is one of the main obstacles to achieve high bit transmission rate and low bit-error rate (BER). How to resist the multipath effect is a fundamental problem in a chaos-based wireless communication system (CWCS). In this paper, a CWCS is built to transmit chaotic signals generated by a hybrid dynamical system and then to filter the received signals by using the corresponding matched filter to decrease the noise effect and to detect the binary information. We find that the multipath effect can be effectively resisted by regrouping the return map of the received signal and by setting the corresponding threshold based on the available information. We show that the optimal threshold is a function of the channel parameters and of the information symbols. Practically, the channel parameters are time-variant, and the future information symbols are unavailable. In this case, a suboptimal threshold is proposed, and the BER using the suboptimal threshold is derived analytically. Simulation results show that the CWCS achieves a remarkable competitive performance even under inaccurate channel parameters.

Code Tracking Algorithms for Mitigating Multipath Effects in Fading Channels for Satellite-Based Positioning

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Markku Renfors

2007-12-01

Full Text Available The ever-increasing public interest in location and positioning services has originated a demand for higher performance global navigation satellite systems (GNSSs. In order to achieve this incremental performance, the estimation of line-of-sight (LOS delay with high accuracy is a prerequisite for all GNSSs. The delay lock loops (DLLs and their enhanced variants (i.e., feedback code tracking loops are the structures of choice for the commercial GNSS receivers, but their performance in severe multipath scenarios is still rather limited. In addition, the new satellite positioning system proposals specify the use of a new modulation, the binary offset carrier (BOC modulation, which triggers a new challenge in the code tracking stage. Therefore, in order to meet this emerging challenge and to improve the accuracy of the delay estimation in severe multipath scenarios, this paper analyzes feedback as well as feedforward code tracking algorithms and proposes the peak tracking (PT methods, which are combinations of both feedback and feedforward structures and utilize the inherent advantages of both structures. We propose and analyze here two variants of PT algorithm: PT with second-order differentiation (Diff2, and PT with Teager Kaiser (TK operator, which will be denoted herein as PT(Diff2 and PT(TK, respectively. In addition to the proposal of the PT methods, the authors propose also an improved early-late-slope (IELS multipath elimination technique which is shown to provide very good mean-time-to-lose-lock (MTLL performance. An implementation of a noncoherent multipath estimating delay locked loop (MEDLL structure is also presented. We also incorporate here an extensive review of the existing feedback and feedforward delay estimation algorithms for direct sequence code division multiple access (DS-CDMA signals in satellite fading channels, by taking into account the impact of binary phase shift keying (BPSK as well as the newly proposed BOC modulation

Subspace Based Blind Sparse Channel Estimation

DEFF Research Database (Denmark)

Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki

2012-01-01

The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by replacing the 2–norm minimization in the conventional subspace based method by the 1–norm minimization problem. Numerical results confirm that the proposed method can significantly improve...

Accurate Bit Error Rate Calculation for Asynchronous Chaos-Based DS-CDMA over Multipath Channel

Science.gov (United States)

Kaddoum, Georges; Roviras, Daniel; Chargé, Pascal; Fournier-Prunaret, Daniele

2009-12-01

An accurate approach to compute the bit error rate expression for multiuser chaosbased DS-CDMA system is presented in this paper. For more realistic communication system a slow fading multipath channel is considered. A simple RAKE receiver structure is considered. Based on the bit energy distribution, this approach compared to others computation methods existing in literature gives accurate results with low computation charge. Perfect estimation of the channel coefficients with the associated delays and chaos synchronization is assumed. The bit error rate is derived in terms of the bit energy distribution, the number of paths, the noise variance, and the number of users. Results are illustrated by theoretical calculations and numerical simulations which point out the accuracy of our approach.

Multiuser TOA Estimation Algorithm in DS-CDMA Sparse Channel for Radiolocation

Science.gov (United States)

Kim, Sunwoo

This letter considers multiuser time delay estimation in a sparse channel environment for radiolocation. The generalized successive interference cancellation (GSIC) algorithm is used to eliminate the multiple access interference (MAI). To adapt GSIC to sparse channels the alternating maximization (AM) algorithm is considered, and the continuous time delay of each path is estimated without requiring a priori known data sequences.

Efficient coordinated recovery of sparse channels in massive MIMO

KAUST Repository

Masood, Mudassir; Afify, Laila H.; Al-Naffouri, Tareq Y.

2015-01-01

on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation

Analysis of a Combined Antenna Arrays and Reverse-Link Synchronous DS-CDMA System over Multipath Rician Fading Channels

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Kim Yong-Seok

2005-01-01

Full Text Available We present the BER analysis of antenna array (AA receiver in reverse-link asynchronous multipath Rician channels and analyze the performance of an improved AA system which applies a reverse-link synchronous transmission technique (RLSTT in order to effectively make a better estimation of covariance matrices at a beamformer-RAKE receiver. In this work, we provide a comprehensive analysis of user capacity which reflects several important factors such as the ratio of the specular component power to the Rayleigh fading power, the shape of multipath intensity profile, and the number of antennas. Theoretical analysis demonstrates that for the case of a strong specular path's power or for a high decay factor, the employment of RLSTT along with AA has the potential of improving the achievable capacity by an order of magnitude.

An Efficient Code-Timing Estimator for DS-CDMA Systems over Resolvable Multipath Channels

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Jian Li

2005-04-01

Full Text Available We consider the problem of training-based code-timing estimation for the asynchronous direct-sequence code-division multiple-access (DS-CDMA system. We propose a modified large-sample maximum-likelihood (MLSML estimator that can be used for the code-timing estimation for the DS-CDMA systems over the resolvable multipath channels in closed form. Simulation results show that MLSML can be used to provide a high correct acquisition probability and a high estimation accuracy. Simulation results also show that MLSML can have very good near-far resistant capability due to employing a data model similar to that for adaptive array processing where strong interferences can be suppressed.

An investigation of 'sparse channel networks'. Characteristic behaviours and their causes

International Nuclear Information System (INIS)

Black, J.H.; Barker, J.A.; Woodman, N.D.

2007-09-01

This report represents a third study in a series concerned with groundwater flow in poorly permeable fractured crystalline rocks. The study has brought together three linked, but distinct, elements; a mathematical analysis of the intersection of ellipses, a review of field measurements associated with nuclear waste repository investigations and probabilistic simulations using a lattice network numerical model. We conclude that the model of channels that traverse fracture intersections without necessarily branching is a very likely representation of reality. More generally, assembling all the lines of evidence, it is suggested that groundwater flow systems in fractured crystalline rocks in the environs of underground laboratories have the following characteristics: Groundwater flows within a sparse network of channels just above the percolation limit. The frequency of intersections is low in that individual channels extend considerable distances between significant junctions. Individual channels often extend over many fracture surfaces and the resulting flow system is only weakly related to the density or size of mappable fractures. The sparseness of systems compared to the size of drifts and tunnels means that only a very few flow channels are intersected by drifts and tunnels. Highly convergent flow is required to connect to the rest of the network and this is misinterpreted as a skin of low hydraulic conductivity. Systems are so sparse that they are controlled by a few 'chokes' that give rise to compartments of head, and probably, of groundwater chemistry. Channels occur on all fracture planes, including those within fracture zones, and although the characteristics of the fracture zone channel networks may differ from those in surrounding rocks, they are nonetheless still channel networks. The actively flowing sparse channel network, occurring within any particular rock, is a naturally selected, small sub-set of the available channels. Hence, there are many

Performance of an Orthogonal Multicarrier CDMA System in a Multicell/Multipath Environment

Energy Technology Data Exchange (ETDEWEB)

Yoon, W.S. [Ajou University, Suwon (Korea)

1999-08-01

We have considered an improved orthogonal multicarrier (MC) CDMA system. This system combines the advantages of both DS-CDMA with a concatenated spreading scheme and an MC modulation technique to combat the effects of a multipath fading channel and intersymbol interference (IS). The performance of the system is analyzed under a multicell, multiuser, and multipath Rician fading channel. The system is shown to outperform the orthogonal MC-CDMA system with a conventional PN sequence. (author). 4 refs., 3 figs.

Sparse Channel Estimation for MIMO-OFDM Two-Way Relay Network with Compressed Sensing

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Aihua Zhang

2013-01-01

Full Text Available Accurate channel impulse response (CIR is required for equalization and can help improve communication service quality in next-generation wireless communication systems. An example of an advanced system is amplify-and-forward multiple-input multiple-output two-way relay network, which is modulated by orthogonal frequency-division multiplexing. Linear channel estimation methods, for example, least squares and expectation conditional maximization, have been proposed previously for the system. However, these methods do not take advantage of channel sparsity, and they decrease estimation performance. We propose a sparse channel estimation scheme, which is different from linear methods, at end users under the relay channel to enable us to exploit sparsity. First, we formulate the sparse channel estimation problem as a compressed sensing problem by using sparse decomposition theory. Second, the CIR is reconstructed by CoSaMP and OMP algorithms. Finally, computer simulations are conducted to confirm the superiority of the proposed methods over traditional linear channel estimation methods.

Interference-Aware OFDM Receiver for Channels with Sparse Common Supports

DEFF Research Database (Denmark)

Barbu, Oana-Elena; Manchón, Carles Navarro; Badiu, Mihai Alin

2017-01-01

We design an algorithm for OFDM receivers operating in co-channel interference conditions, where the serving and interfering transmitters are synchronized in time. The channel estimation problem is formulated as one of sparse signal reconstruction using multiple measurement vectors. The proposed...

OFDM receiver for fast time-varying channels using block-sparse Bayesian learning

DEFF Research Database (Denmark)

Barbu, Oana-Elena; Manchón, Carles Navarro; Rom, Christian

2016-01-01

characterized with a basis expansion model using a small number of terms. As a result, the channel estimation problem is posed as that of estimating a vector of complex coefficients that exhibits a block-sparse structure, which we solve with tools from block-sparse Bayesian learning. Using variational Bayesian...... inference, we embed the channel estimator in a receiver structure that performs iterative channel and noise precision estimation, intercarrier interference cancellation, detection and decoding. Simulation results illustrate the superior performance of the proposed receiver over state-of-art receivers....

Pilot-based parametric channel estimation algorithm for DCO-OFDM-based visual light communications

Science.gov (United States)

Qian, Xuewen; Deng, Honggui; He, Hailang

2017-10-01

Due to wide modulation bandwidth in optical communication, multipath channels may be non-sparse and deteriorate communication performance heavily. Traditional compressive sensing-based channel estimation algorithm cannot be employed in this kind of situation. In this paper, we propose a practical parametric channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based visual light communication (VLC) systems based on modified zero correlation code (ZCC) pair that has the impulse-like correlation property. Simulation results show that the proposed algorithm achieves better performances than existing least squares (LS)-based algorithm in both bit error ratio (BER) and frequency response estimation.

Space-Time Joint Interference Cancellation Using Fuzzy-Inference-Based Adaptive Filtering Techniques in Frequency-Selective Multipath Channels

Directory of Open Access Journals (Sweden)

Chen Yu-Fan

2006-01-01

Full Text Available An adaptive minimum mean-square error (MMSE array receiver based on the fuzzy-logic recursive least-squares (RLS algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ( , , into a forgetting factor . For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS algorithm using the fuzzy-inference-controlled step-size . This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS and variable forgetting factor RLS (VFF-RLS algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER for multipath fading channels.

Mitigation of multipath effect in GNSS short baseline positioning by the multipath hemispherical map

Science.gov (United States)

Dong, D.; Wang, M.; Chen, W.; Zeng, Z.; Song, L.; Zhang, Q.; Cai, M.; Cheng, Y.; Lv, J.

2016-03-01

Multipath is one major error source in high-accuracy GNSS positioning. Various hardware and software approaches are developed to mitigate the multipath effect. Among them the MHM (multipath hemispherical map) and sidereal filtering (SF)/advanced SF (ASF) approaches utilize the spatiotemporal repeatability of multipath effect under static environment, hence they can be implemented to generate multipath correction model for real-time GNSS data processing. We focus on the spatial-temporal repeatability-based MHM and SF/ASF approaches and compare their performances for multipath reduction. Comparisons indicate that both MHM and ASF approaches perform well with residual variance reduction (50 %) for short span (next 5 days) and maintains roughly 45 % reduction level for longer span (next 6-25 days). The ASF model is more suitable for high frequency multipath reduction, such as high-rate GNSS applications. The MHM model is easier to implement for real-time multipath mitigation when the overall multipath regime is medium to low frequency.

Performance evaluation of CPPM modulation in multi-path environments

International Nuclear Information System (INIS)

Tasev, Zarko; Kocarev, Ljupco

2003-01-01

Chaotic pulse position modulation (CPPM) is a novel technique to communicate with chaotic signals based upon pulse trains in which the intervals between two pulses are determined by chaotic dynamics of a pulse generator. Using numerical simulations we show that CPPM offers excellent multi-path performance. We simulated the CPPM radio system, which is designed for a WLAN application and operates in the 2.4 GHz ISM frequency band with IEEE 802.11 compliant channel spacing. In this case, the average performance loss due the multi-path for CPPM is less than 5 dB

Performance evaluation of CPPM modulation in multi-path environments

Energy Technology Data Exchange (ETDEWEB)

Tasev, Zarko E-mail: ztasev@ucsd.edu; Kocarev, Ljupco E-mail: lkocarev@ucsd.edu

2003-01-01

Chaotic pulse position modulation (CPPM) is a novel technique to communicate with chaotic signals based upon pulse trains in which the intervals between two pulses are determined by chaotic dynamics of a pulse generator. Using numerical simulations we show that CPPM offers excellent multi-path performance. We simulated the CPPM radio system, which is designed for a WLAN application and operates in the 2.4 GHz ISM frequency band with IEEE 802.11 compliant channel spacing. In this case, the average performance loss due the multi-path for CPPM is less than 5 dB.

An investigation of 'sparse channel networks'. Characteristic behaviours and their causes

Energy Technology Data Exchange (ETDEWEB)

Black, J.H. (In Situ Solutions, East Bridgford (GB)); Barker, J.A.; Woodman, N.D. (Univ. of Southampton (GB))

2007-09-15

This report represents a third study in a series concerned with groundwater flow in poorly permeable fractured crystalline rocks. The study has brought together three linked, but distinct, elements; a mathematical analysis of the intersection of ellipses, a review of field measurements associated with nuclear waste repository investigations and probabilistic simulations using a lattice network numerical model. We conclude that the model of channels that traverse fracture intersections without necessarily branching is a very likely representation of reality. More generally, assembling all the lines of evidence, it is suggested that groundwater flow systems in fractured crystalline rocks in the environs of underground laboratories have the following characteristics: Groundwater flows within a sparse network of channels just above the percolation limit. The frequency of intersections is low in that individual channels extend considerable distances between significant junctions. Individual channels often extend over many fracture surfaces and the resulting flow system is only weakly related to the density or size of mappable fractures. The sparseness of systems compared to the size of drifts and tunnels means that only a very few flow channels are intersected by drifts and tunnels. Highly convergent flow is required to connect to the rest of the network and this is misinterpreted as a skin of low hydraulic conductivity. Systems are so sparse that they are controlled by a few 'chokes' that give rise to compartments of head, and probably, of groundwater chemistry. Channels occur on all fracture planes, including those within fracture zones, and although the characteristics of the fracture zone channel networks may differ from those in surrounding rocks, they are nonetheless still channel networks. The actively flowing sparse channel network, occurring within any particular rock, is a naturally selected, small sub-set of the available channels. Hence, there are

Smooth Approximation l 0-Norm Constrained Affine Projection Algorithm and Its Applications in Sparse Channel Estimation

Science.gov (United States)

2014-01-01

We propose a smooth approximation l 0-norm constrained affine projection algorithm (SL0-APA) to improve the convergence speed and the steady-state error of affine projection algorithm (APA) for sparse channel estimation. The proposed algorithm ensures improved performance in terms of the convergence speed and the steady-state error via the combination of a smooth approximation l 0-norm (SL0) penalty on the coefficients into the standard APA cost function, which gives rise to a zero attractor that promotes the sparsity of the channel taps in the channel estimation and hence accelerates the convergence speed and reduces the steady-state error when the channel is sparse. The simulation results demonstrate that our proposed SL0-APA is superior to the standard APA and its sparsity-aware algorithms in terms of both the convergence speed and the steady-state behavior in a designated sparse channel. Furthermore, SL0-APA is shown to have smaller steady-state error than the previously proposed sparsity-aware algorithms when the number of nonzero taps in the sparse channel increases. PMID:24790588

Space-Time Joint Interference Cancellation Using Fuzzy-Inference-Based Adaptive Filtering Techniques in Frequency-Selective Multipath Channels

Science.gov (United States)

Hu, Chia-Chang; Lin, Hsuan-Yu; Chen, Yu-Fan; Wen, Jyh-Horng

2006-12-01

An adaptive minimum mean-square error (MMSE) array receiver based on the fuzzy-logic recursive least-squares (RLS) algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ([InlineEquation not available: see fulltext.],[InlineEquation not available: see fulltext.]), into a forgetting factor[InlineEquation not available: see fulltext.]. For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS) algorithm using the fuzzy-inference-controlled step-size[InlineEquation not available: see fulltext.]. This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS) and variable forgetting factor RLS (VFF-RLS) algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER) for multipath fading channels.

Low-Complexity Bayesian Estimation of Cluster-Sparse Channels

KAUST Repository

Ballal, Tarig

2015-09-18

This paper addresses the problem of channel impulse response estimation for cluster-sparse channels under the Bayesian estimation framework. We develop a novel low-complexity minimum mean squared error (MMSE) estimator by exploiting the sparsity of the received signal profile and the structure of the measurement matrix. It is shown that due to the banded Toeplitz/circulant structure of the measurement matrix, a channel impulse response, such as underwater acoustic channel impulse responses, can be partitioned into a number of orthogonal or approximately orthogonal clusters. The orthogonal clusters, the sparsity of the channel impulse response and the structure of the measurement matrix, all combined, result in a computationally superior realization of the MMSE channel estimator. The MMSE estimator calculations boil down to simpler in-cluster calculations that can be reused in different clusters. The reduction in computational complexity allows for a more accurate implementation of the MMSE estimator. The proposed approach is tested using synthetic Gaussian channels, as well as simulated underwater acoustic channels. Symbol-error-rate performance and computation time confirm the superiority of the proposed method compared to selected benchmark methods in systems with preamble-based training signals transmitted over clustersparse channels.

Low-Complexity Bayesian Estimation of Cluster-Sparse Channels

KAUST Repository

Ballal, Tarig; Al-Naffouri, Tareq Y.; Ahmed, Syed

2015-01-01

This paper addresses the problem of channel impulse response estimation for cluster-sparse channels under the Bayesian estimation framework. We develop a novel low-complexity minimum mean squared error (MMSE) estimator by exploiting the sparsity of the received signal profile and the structure of the measurement matrix. It is shown that due to the banded Toeplitz/circulant structure of the measurement matrix, a channel impulse response, such as underwater acoustic channel impulse responses, can be partitioned into a number of orthogonal or approximately orthogonal clusters. The orthogonal clusters, the sparsity of the channel impulse response and the structure of the measurement matrix, all combined, result in a computationally superior realization of the MMSE channel estimator. The MMSE estimator calculations boil down to simpler in-cluster calculations that can be reused in different clusters. The reduction in computational complexity allows for a more accurate implementation of the MMSE estimator. The proposed approach is tested using synthetic Gaussian channels, as well as simulated underwater acoustic channels. Symbol-error-rate performance and computation time confirm the superiority of the proposed method compared to selected benchmark methods in systems with preamble-based training signals transmitted over clustersparse channels.

Compressive Sensing Based Bayesian Sparse Channel Estimation for OFDM Communication Systems: High Performance and Low Complexity

Science.gov (United States)

Xu, Li; Shan, Lin; Adachi, Fumiyuki

2014-01-01

In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, for example, orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that proposed method can improve the estimation performance when comparing with conventional SCE methods. PMID:24983012

Spatial Dynamics of Indoor Radio Wideband Channels

Directory of Open Access Journals (Sweden)

Hayar Aawatif

2010-01-01

Full Text Available The multipath components of superwideband (2–17.2 GHz nonline-of-sight channel responses measured inside several buildings are stable along sections that are 27 cm long on average with a standard deviation of 16 cm. The stability regions of multipath components have an approximately log-normal histogram. An analysis of measured channels that explicitly includes finite spatial areas of visibility of the multipath components is superior to the classic analysis that attributes spatial dynamics to interference of the multipath. The spatial stability of measured responses, that is, the size of the typical area of visibility of each multipath component, decreases as the carrier frequency increases but does not depend on bandwidth. The results offer insight into the nature of the diffuse part of the radio channel.

A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements

KAUST Repository

Sana, Furrukh

2016-06-01

Subsurface reservoir flow channels are characterized by high-permeability values and serve as preferred pathways for fluid propagation. Accurate estimation of their geophysical structures is thus of great importance for the oil industry. The ensemble Kalman filter (EnKF) is a widely used statistical technique for estimating subsurface reservoir model parameters. However, accurate reconstruction of the subsurface geological features with the EnKF is challenging because of the limited measurements available from the wells and the smoothing effects imposed by the \\\\ell _{2} -norm nature of its update step. A new EnKF scheme based on sparse domain representation was introduced by Sana et al. (2015) to incorporate useful prior structural information in the estimation process for efficient recovery of subsurface channels. In this paper, we extend this work in two ways: 1) investigate the effects of incorporating time-lapse seismic data on the channel reconstruction; and 2) explore a Bayesian sparse reconstruction algorithm with the potential ability to reduce the computational requirements. Numerical results suggest that the performance of the new sparse Bayesian based EnKF scheme is enhanced with the availability of seismic measurements, leading to further improvement in the recovery of flow channels structures. The sparse Bayesian approach further provides a computationally efficient framework for enforcing a sparse solution, especially with the possibility of using high sparsity rates through the inclusion of seismic data.

A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements

KAUST Repository

Sana, Furrukh; Ravanelli, Fabio; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim

2016-01-01

Subsurface reservoir flow channels are characterized by high-permeability values and serve as preferred pathways for fluid propagation. Accurate estimation of their geophysical structures is thus of great importance for the oil industry. The ensemble Kalman filter (EnKF) is a widely used statistical technique for estimating subsurface reservoir model parameters. However, accurate reconstruction of the subsurface geological features with the EnKF is challenging because of the limited measurements available from the wells and the smoothing effects imposed by the \\ell _{2} -norm nature of its update step. A new EnKF scheme based on sparse domain representation was introduced by Sana et al. (2015) to incorporate useful prior structural information in the estimation process for efficient recovery of subsurface channels. In this paper, we extend this work in two ways: 1) investigate the effects of incorporating time-lapse seismic data on the channel reconstruction; and 2) explore a Bayesian sparse reconstruction algorithm with the potential ability to reduce the computational requirements. Numerical results suggest that the performance of the new sparse Bayesian based EnKF scheme is enhanced with the availability of seismic measurements, leading to further improvement in the recovery of flow channels structures. The sparse Bayesian approach further provides a computationally efficient framework for enforcing a sparse solution, especially with the possibility of using high sparsity rates through the inclusion of seismic data.

Direct Position Determination of Unknown Signals in the Presence of Multipath Propagation

Directory of Open Access Journals (Sweden)

Jianping Du

2018-03-01

Full Text Available A novel geolocation architecture, termed “Multiple Transponders and Multiple Receivers for Multiple Emitters Positioning System (MTRE” is proposed in this paper. Existing Direct Position Determination (DPD methods take advantage of a rather simple channel assumption (line of sight channels with complex path attenuations and a simplified MUltiple SIgnal Classification (MUSIC algorithm cost function to avoid the high dimension searching. We point out that the simplified assumption and cost function reduce the positioning accuracy because of the singularity of the array manifold in a multi-path environment. We present a DPD model for unknown signals in the presence of Multi-path Propagation (MP-DPD in this paper. MP-DPD adds non-negative real path attenuation constraints to avoid the mistake caused by the singularity of the array manifold. The Multi-path Propagation MUSIC (MP-MUSIC method and the Active Set Algorithm (ASA are designed to reduce the dimension of searching. A Multi-path Propagation Maximum Likelihood (MP-ML method is proposed in addition to overcome the limitation of MP-MUSIC in the sense of a time-sensitive application. An iterative algorithm and an approach of initial value setting are given to make the MP-ML time consumption acceptable. Numerical results validate the performances improvement of MP-MUSIC and MP-ML. A closed form of the Cramér–Rao Lower Bound (CRLB is derived as a benchmark to evaluate the performances of MP-MUSIC and MP-ML.

Direct Position Determination of Unknown Signals in the Presence of Multipath Propagation.

Science.gov (United States)

Du, Jianping; Wang, Ding; Yu, Wanting; Yu, Hongyi

2018-03-17

A novel geolocation architecture, termed "Multiple Transponders and Multiple Receivers for Multiple Emitters Positioning System (MTRE)" is proposed in this paper. Existing Direct Position Determination (DPD) methods take advantage of a rather simple channel assumption (line of sight channels with complex path attenuations) and a simplified MUltiple SIgnal Classification (MUSIC) algorithm cost function to avoid the high dimension searching. We point out that the simplified assumption and cost function reduce the positioning accuracy because of the singularity of the array manifold in a multi-path environment. We present a DPD model for unknown signals in the presence of Multi-path Propagation (MP-DPD) in this paper. MP-DPD adds non-negative real path attenuation constraints to avoid the mistake caused by the singularity of the array manifold. The Multi-path Propagation MUSIC (MP-MUSIC) method and the Active Set Algorithm (ASA) are designed to reduce the dimension of searching. A Multi-path Propagation Maximum Likelihood (MP-ML) method is proposed in addition to overcome the limitation of MP-MUSIC in the sense of a time-sensitive application. An iterative algorithm and an approach of initial value setting are given to make the MP-ML time consumption acceptable. Numerical results validate the performances improvement of MP-MUSIC and MP-ML. A closed form of the Cramér-Rao Lower Bound (CRLB) is derived as a benchmark to evaluate the performances of MP-MUSIC and MP-ML.

Direct Position Determination of Unknown Signals in the Presence of Multipath Propagation

Science.gov (United States)

Yu, Hongyi

2018-01-01

A novel geolocation architecture, termed “Multiple Transponders and Multiple Receivers for Multiple Emitters Positioning System (MTRE)” is proposed in this paper. Existing Direct Position Determination (DPD) methods take advantage of a rather simple channel assumption (line of sight channels with complex path attenuations) and a simplified MUltiple SIgnal Classification (MUSIC) algorithm cost function to avoid the high dimension searching. We point out that the simplified assumption and cost function reduce the positioning accuracy because of the singularity of the array manifold in a multi-path environment. We present a DPD model for unknown signals in the presence of Multi-path Propagation (MP-DPD) in this paper. MP-DPD adds non-negative real path attenuation constraints to avoid the mistake caused by the singularity of the array manifold. The Multi-path Propagation MUSIC (MP-MUSIC) method and the Active Set Algorithm (ASA) are designed to reduce the dimension of searching. A Multi-path Propagation Maximum Likelihood (MP-ML) method is proposed in addition to overcome the limitation of MP-MUSIC in the sense of a time-sensitive application. An iterative algorithm and an approach of initial value setting are given to make the MP-ML time consumption acceptable. Numerical results validate the performances improvement of MP-MUSIC and MP-ML. A closed form of the Cramér–Rao Lower Bound (CRLB) is derived as a benchmark to evaluate the performances of MP-MUSIC and MP-ML. PMID:29562601

Performance of Reverse-Link Synchronous DS-CDMA System on a Frequency-Selective Multipath Fading Channel with Imperfect Power Control

Directory of Open Access Journals (Sweden)

Duk Kyung Kim

2002-08-01

Full Text Available We analyze the performance for reverse-link synchronous DS-CDMA system in a frequency-selective Rayleigh fading channel with an imperfect power control scheme. The performance degradation due to power control error (PCE, which is approximated by a log-normally distributed random variable, is estimated as a function of the standard deviation of the PCE. In addition, we investigate the impacts of the multipath intensity profile (MIP shape and the number of resolvable paths on the performance. Finally, the coded bit error performance is evaluated in order to estimate the system capacity. Comparing with the conventional CDMA system, we show an achievable gain of from 59% to 23% for reverse-link synchronous transmission technique (RLSTT in the presence of imperfect power control over asynchronous transmission for BER=10−6. As well, the effect of tradeoff between orthogonality and diversity can be seen according to the number of multipaths, and the tendency is kept even in the presence of PCE. We conclude that the capacity can be further improved via the RLSTT, because the DS-CDMA system is very sensitive to power control imperfections.

Design of complete software GPS signal simulator with low complexity and precise multipath channel model

Directory of Open Access Journals (Sweden)

G. Arul Elango

2016-09-01

Full Text Available The need for GPS data simulators have become important due to the tremendous growth in the design of versatile GPS receivers. Commercial hardware and software based GPS simulators are expensive and time consuming. In this work, a low cost simple novel GPS L1 signal simulator is designed for testing and evaluating the performance of software GPS receiver in a laboratory environment. A typical real time paradigm, similar to actual satellite derived GPS signal is created on a computer generated scenario. In this paper, a GPS software simulator is proposed that may offer a lot of analysis and testing flexibility to the researchers and developers as it is totally software based primarily running on a laptop/personal computer without the requirement of any hardware. The proposed GPS simulator allows provision for re-configurability and test repeatability and is developed in VC++ platform to minimize the simulation time. It also incorporates Rayleigh multipath channel fading model under non-line of sight (NLOS conditions. In this work, to efficiently design the simulator, several Rayleigh fading models viz. Inverse Discrete Fourier Transform (IDFT, Filtering White Gaussian Noise (FWFN and modified Sum of Sinusoidal (SOS simulators are tested and compared in terms of accuracy of its first and second order statistical metrics, execution time and the later one is found to be as the best appropriate Rayleigh multipath model suitable for incorporating with GPS simulator. The fading model written in ‘MATLAB’ engine has been linked with software GPS simulator module enable to test GPS receiver’s functionality in different fading environments.

Iterative Sparse Channel Estimation and Decoding for Underwater MIMO-OFDM

Directory of Open Access Journals (Sweden)

Berger ChristianR

2010-01-01

Full Text Available We propose a block-by-block iterative receiver for underwater MIMO-OFDM that couples channel estimation with multiple-input multiple-output (MIMO detection and low-density parity-check (LDPC channel decoding. In particular, the channel estimator is based on a compressive sensing technique to exploit the channel sparsity, the MIMO detector consists of a hybrid use of successive interference cancellation and soft minimum mean-square error (MMSE equalization, and channel coding uses nonbinary LDPC codes. Various feedback strategies from the channel decoder to the channel estimator are studied, including full feedback of hard or soft symbol decisions, as well as their threshold-controlled versions. We study the receiver performance using numerical simulation and experimental data collected from the RACE08 and SPACE08 experiments. We find that iterative receiver processing including sparse channel estimation leads to impressive performance gains. These gains are more pronounced when the number of available pilots to estimate the channel is decreased, for example, when a fixed number of pilots is split between an increasing number of parallel data streams in MIMO transmission. For the various feedback strategies for iterative channel estimation, we observe that soft decision feedback slightly outperforms hard decision feedback.

Bandwidth efficient channel estimation method for airborne hyperspectral data transmission in sparse doubly selective communication channels

Science.gov (United States)

Vahidi, Vahid; Saberinia, Ebrahim; Regentova, Emma E.

2017-10-01

A channel estimation (CE) method based on compressed sensing (CS) is proposed to estimate the sparse and doubly selective (DS) channel for hyperspectral image transmission from unmanned aircraft vehicles to ground stations. The proposed method contains three steps: (1) the priori estimate of the channel by orthogonal matching pursuit (OMP), (2) calculation of the linear minimum mean square error (LMMSE) estimate of the received pilots given the estimated channel, and (3) estimate of the complex amplitudes and Doppler shifts of the channel using the enhanced received pilot data applying a second round of a CS algorithm. The proposed method is named DS-LMMSE-OMP, and its performance is evaluated by simulating transmission of AVIRIS hyperspectral data via the communication channel and assessing their fidelity for the automated analysis after demodulation. The performance of the DS-LMMSE-OMP approach is compared with that of two other state-of-the-art CE methods. The simulation results exhibit up to 8-dB figure of merit in the bit error rate and 50% improvement in the hyperspectral image classification accuracy.

Global Positioning System (GPS) Receiver Design For Multipaths Mitigation

National Research Council Canada - National Science Library

Gadallah, El-Sayed

1998-01-01

.... This research introduces a new estimator that can detect the presence of multipath, can determine the unknown number of multipath components and can estimate multipath parameters in the GPS receiver...

Distortion Cancellation via Polyphase Multipath Circuits

NARCIS (Netherlands)

Mensink, E.; Klumperink, Eric A.M.; Nauta, Bram

The central question of this paper is: can we enhance the spectral purity of nonlinear circuits with the help of polyphase multipath circuits. Polyphase multipath circuits are circuits with two or more paths that exploit phase differences between the paths to cancel unwanted signals. It turns out

Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

Directory of Open Access Journals (Sweden)

Jin-Jia Wang

2015-01-01

Full Text Available Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs. Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.

Propagation measurements and multipath channel modelling for line-of-sight links at 19.5 GHz

CSIR Research Space (South Africa)

Naicker, K

2006-06-01

Full Text Available The research aims to characterise the performance of the link by evaluating the effects of multipath propagation under various meteorological conditions. A LOS link was established between the Howard College and Westville campuses of UKZN and passes...

MC-DS-CDMA System based on DWT and STBC in ITU Multipath Fading Channels Model

Directory of Open Access Journals (Sweden)

Nader Abdullah Khadam

2018-03-01

Full Text Available In this paper, the performance of multicarrier direct sequence code division multiple access (MC-DS-CDMA in fixed MC-DS-CDMA and Mobile MC-DS-CDMA applications have been improved by using the compensations of space time block coding and Discrete Fast Fourier transforms (FFT or Discrete Wavelets transform DWT. These MC-DS-CDMA systems had been simulated using MATLAB 2015a. Through simulation of the proposed system, various parameters can be changed and tested. The Bit Error Rate (BERs of these systems are obtained over wide range of signal to noise ratio. All simulation results had been compared with each other using different subcarrier size of FFT or DWT with STBC for 1,2,3 and 4 antennas in transmitter and under different ITU multipath fading channels and different Doppler frequencies (fd. The proposed structures of STBC-MC-DS-CDMA system based on (DWT batter than based on (FFT in varies Doppler frequencies and subcarrier size. Also, proposed system with STBC based on 4 transmitters better than other systems based on 1 or 2 or 3 transmitters in all Doppler frequencies and subcarrier size in all simulation results.

Multipath modeling for aeronautical communications.

Science.gov (United States)

Painter, J. H.; Gupta, S. C.; Wilson, L. R.

1973-01-01

One of the fundamental technical problems in aeronautical digital communications is that of multipath propagation between aircraft and ground terminal. This paper examines in detail a model of the received multipath signal that is useful for application of modern detection and estimation theories. The model treats arbitrary modulation and covers the selective and nonselective cases. The necessarily nonstationary statistics of the received signal are determined from the link geometry and the surface roughness parameters via a Kirchhoff solution.

Spectral Purity Enhancement via Polyphase Multipath Circuits

NARCIS (Netherlands)

Mensink, E.; Klumperink, Eric A.M.; Nauta, Bram

2004-01-01

The central question of this paper is: can we enhance the spectral purity of nonlinear circuits by using polyphase multipath circuits? The basic idea behind polyphase multipath circuits is to split the nonlinear circuits into two or more paths and exploit phase differences between these paths to

Inverse Scattering in a Multipath Environment

Directory of Open Access Journals (Sweden)

A. Cuccaro

2016-09-01

Full Text Available In this contribution an inverse scattering problem is ad- dressed in a multipath environment. In particular, multipath is created by known ”extra” point-like scatterers (passive elements expressely deployed between the scene under in- vestigation and the source/measurement domains. Through a back-projection imaging scheme, the role of the passive elements on the achievable performance is shown and com- pared to the free-space case.

Advanced Measurements of the Aggregation Capability of the MPT Network Layer Multipath Communication Library

Directory of Open Access Journals (Sweden)

Gábor Lencse

2015-05-01

Full Text Available The MPT network layer multipath communicationlibrary is a novel solution for several problems including IPv6transition, reliable data transmission using TCP, real-time transmissionusing UDP and also wireless network layer routingproblems. MPT can provide an IPv4 or an IPv6 tunnel overone or more IPv4 or IPv6 communication channels. MPT canalso aggregate the capacity of multiple physical channels. In thispaper, the channel aggregation capability of the MPT libraryis measured up to twelve 100Mbps speed channels. Differentscenarios are used: both IPv4 and IPv6 are used as the underlyingand also as the encapsulated protocols and also both UDP andTCP are used as transport protocols. In addition, measurementsare taken with both 32-bit and 64-bit version of the MPT library.In all cases, the number of the physical channels is increased from1 to 12 and the aggregated throughput is measured.

An improved method for Multipath Hemispherical Map (MHM) based on Trend Surface Analysis

Science.gov (United States)

Wang, Zhiren; Chen, Wen; Dong, Danan; Yu, Chao

2017-04-01

Among various approaches developed for detecting the multipath effect in high-accuracy GNSS positioning, Only MHM (Multipath Hemispherical Map) and SF (Sidereal Filtering) can be implemented to real-time GNSS data processing. SF is based on the time repeatability of satellites which just suitable for static environment, while the spatiotemporal repeatability-based MHM is applicable not only for static environment but also for dynamic carriers with static multipath environment such as ships and airplanes, and utilizes much smaller number of parameters than ASF. However, the MHM method also has certain defects. Since the MHM take the mean of residuals from the grid as the filter value, it is more suitable when the multipath regime is medium to low frequency. Now existing research data indicate that the newly advanced Sidereal Filtering (ASF) method perform better with high frequency multipath reduction than MHM by contrast. To solve the above problem and improve MHM's performance on high frequency multipath, we combined binary trend surface analysis method with original MHM model to effectively analyze particular spatial distribution and variation trends of multipath effect. We computed trend surfaces of the residuals within a grid by least-square procedures, and chose the best results through the moderate successive test. The enhanced MHM grid was constructed from a set of coefficients of the fitted equation instead of mean value. According to the analysis of the actual observation, the improved MHM model shows positive effect on high frequency multipath reduction, and significantly reduced the root mean square (RMS) value of the carrier residuals. Keywords: Trend Surface Analysis; Multipath Hemispherical Map; high frequency multipath effect

Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment

Directory of Open Access Journals (Sweden)

Yuze Wang

2018-04-01

Full Text Available A deep understanding of multipath characteristics is essential to design signal simulators and receivers in global navigation satellite system applications. As a new constellation is deployed and more applications occur in the urban environment, the statistical multipath models of navigation signal need further study. In this paper, we present statistical distribution models of multipath time delay, multipath power attenuation, and multipath fading frequency based on the experimental data in the urban canyon environment. The raw data of multipath characteristics are obtained by processing real navigation signal to study the statistical distribution. By fitting the statistical data, it shows that the probability distribution of time delay follows a gamma distribution which is related to the waiting time of Poisson distributed events. The fading frequency follows an exponential distribution, and the mean of multipath power attenuation decreases linearly with an increasing time delay. In addition, the detailed statistical characteristics for different elevations and orbits satellites is studied, and the parameters of each distribution are quite different. The research results give useful guidance for navigation simulator and receiver designers.

Ultrasonic imaging of material flaws exploiting multipath information

Science.gov (United States)

Shen, Xizhong; Zhang, Yimin D.; Demirli, Ramazan; Amin, Moeness G.

2011-05-01

In this paper, we consider ultrasonic imaging for the visualization of flaws in a material. Ultrasonic imaging is a powerful nondestructive testing (NDT) tool which assesses material conditions via the detection, localization, and classification of flaws inside a structure. Multipath exploitations provide extended virtual array apertures and, in turn, enhance imaging capability beyond the limitation of traditional multisensor approaches. We utilize reflections of ultrasonic signals which occur when encountering different media and interior discontinuities. The waveforms observed at the physical as well as virtual sensors yield additional measurements corresponding to different aspect angles. Exploitation of multipath information addresses unique issues observed in ultrasonic imaging. (1) Utilization of physical and virtual sensors significantly extends the array aperture for image enhancement. (2) Multipath signals extend the angle of view of the narrow beamwidth of the ultrasound transducers, allowing improved visibility and array design flexibility. (3) Ultrasonic signals experience difficulty in penetrating a flaw, thus the aspect angle of the observation is limited unless access to other sides is available. The significant extension of the aperture makes it possible to yield flaw observation from multiple aspect angles. We show that data fusion of physical and virtual sensor data significantly improves the detection and localization performance. The effectiveness of the proposed multipath exploitation approach is demonstrated through experimental studies.

Multipath Routing in Wireless Sensor Networks: Survey and Research Challenges

Science.gov (United States)

Radi, Marjan; Dezfouli, Behnam; Bakar, Kamalrulnizam Abu; Lee, Malrey

2012-01-01

A wireless sensor network is a large collection of sensor nodes with limited power supply and constrained computational capability. Due to the restricted communication range and high density of sensor nodes, packet forwarding in sensor networks is usually performed through multi-hop data transmission. Therefore, routing in wireless sensor networks has been considered an important field of research over the past decade. Nowadays, multipath routing approach is widely used in wireless sensor networks to improve network performance through efficient utilization of available network resources. Accordingly, the main aim of this survey is to present the concept of the multipath routing approach and its fundamental challenges, as well as the basic motivations for utilizing this technique in wireless sensor networks. In addition, we present a comprehensive taxonomy on the existing multipath routing protocols, which are especially designed for wireless sensor networks. We highlight the primary motivation behind the development of each protocol category and explain the operation of different protocols in detail, with emphasis on their advantages and disadvantages. Furthermore, this paper compares and summarizes the state-of-the-art multipath routing techniques from the network application point of view. Finally, we identify open issues for further research in the development of multipath routing protocols for wireless sensor networks. PMID:22368490

Multipath routing in wireless sensor networks: survey and research challenges.

Science.gov (United States)

Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Lee, Malrey

2012-01-01

A wireless sensor network is a large collection of sensor nodes with limited power supply and constrained computational capability. Due to the restricted communication range and high density of sensor nodes, packet forwarding in sensor networks is usually performed through multi-hop data transmission. Therefore, routing in wireless sensor networks has been considered an important field of research over the past decade. Nowadays, multipath routing approach is widely used in wireless sensor networks to improve network performance through efficient utilization of available network resources. Accordingly, the main aim of this survey is to present the concept of the multipath routing approach and its fundamental challenges, as well as the basic motivations for utilizing this technique in wireless sensor networks. In addition, we present a comprehensive taxonomy on the existing multipath routing protocols, which are especially designed for wireless sensor networks. We highlight the primary motivation behind the development of each protocol category and explain the operation of different protocols in detail, with emphasis on their advantages and disadvantages. Furthermore, this paper compares and summarizes the state-of-the-art multipath routing techniques from the network application point of view. Finally, we identify open issues for further research in the development of multipath routing protocols for wireless sensor networks.

Vector sparse representation of color image using quaternion matrix analysis.

Science.gov (United States)

Xu, Yi; Yu, Licheng; Xu, Hongteng; Zhang, Hao; Nguyen, Truong

2015-04-01

Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain.

Relationship Between Capacity and Pathloss for Indoor MIMO Channels

DEFF Research Database (Denmark)

Nielsen, Jesper Ødum; Andersen, Jørgen Bach; Bauch, Gerhard

2006-01-01

MIMO transmission systems exploit scattering in the radio channel to achieve high capacity for a given SNR. A high pathloss is generally expected for channels with rich scattering, suggesting that a high SNR and rich multipath are competing goals. The current work investigates this issue based on...

A novel communication mechanism based on node potential multi-path routing

Science.gov (United States)

Bu, Youjun; Zhang, Chuanhao; Jiang, YiMing; Zhang, Zhen

2016-10-01

With the network scales rapidly and new network applications emerge frequently, bandwidth supply for today's Internet could not catch up with the rapid increasing requirements. Unfortunately, irrational using of network sources makes things worse. Actual network deploys single-next-hop optimization paths for data transmission, but such "best effort" model leads to the imbalance use of network resources and usually leads to local congestion. On the other hand Multi-path routing can use the aggregation bandwidth of multi paths efficiently and improve the robustness of network, security, load balancing and quality of service. As a result, multi-path has attracted much attention in the routing and switching research fields and many important ideas and solutions have been proposed. This paper focuses on implementing the parallel transmission of multi next-hop data, balancing the network traffic and reducing the congestion. It aimed at exploring the key technologies of the multi-path communication network, which could provide a feasible academic support for subsequent applications of multi-path communication networking. It proposed a novel multi-path algorithm based on node potential in the network. And the algorithm can fully use of the network link resource and effectively balance network link resource utilization.

OpenVPN and Multipath TCP

OpenAIRE

Paulus, Aloïs

2015-01-01

Testing the performances of using Multipath TCP and OpenVPN on a consumer grade router to aggregate multiple Internet connections and provide a fast and reliable connection to individuals and small companies. Master [60] en sciences informatiques, Université catholique de Louvain, 2015

LoRa Performance under Variable Interference and Heavy-Multipath Conditions

Directory of Open Access Journals (Sweden)

Kamil Staniec

2018-01-01

Full Text Available LoRa (or LoRaWAN is by far the best known representative of narrowband communication systems designed for the Internet of Things. As a system dedicated specifically for long-range operations, it possesses a considerable processing gain for the energetic link budget improvement and a remarkable immunity to multipath and interference. The paper describes outcomes of measurement campaigns during which the LoRa performance was tested against these two factors, that is, a heavy-multipath propagation and a controlled, variable interference generated, respectively, in a reverberation chamber and an anechoic chamber. Results allow quantitative appraisal of the system behavior under these harsh conditions with respect to LoRa’s three major configurable parameters: the spreading factor, bandwidth, and code rate. They also allow dividing LoRa configurational space into three distinct sensitivity regions: in the white region it is immune to both interference and multipath propagation, in the light-grey region it is only immune to the multipath phenomenon but sensitive to interference, and in the dark grey region LoRa is vulnerable to both phenomena.

Cross-Correlation-Function-Based Multipath Mitigation Method for Sine-BOC Signals

Directory of Open Access Journals (Sweden)

H. H. Chen

2012-06-01

Full Text Available Global Navigation Satellite Systems (GNSS positioning accuracy indoor and urban canyons environments are greatly affected by multipath due to distortions in its autocorrelation function. In this paper, a cross-correlation function between the received sine phased Binary Offset Carrier (sine-BOC modulation signal and the local signal is studied firstly, and a new multipath mitigation method based on cross-correlation function for sine-BOC signal is proposed. This method is implemented to create a cross-correlation function by designing the modulated symbols of the local signal. The theoretical analysis and simulation results indicate that the proposed method exhibits better multipath mitigation performance compared with the traditional Double Delta Correlator (DDC techniques, especially the medium/long delay multipath signals, and it is also convenient and flexible to implement by using only one correlator, which is the case of low-cost mass-market receivers.

Target Localization with a Single Antenna via Directional Multipath Exploitation

Directory of Open Access Journals (Sweden)

Ali H. Muqaibel

2015-01-01

Full Text Available Target localization in urban sensing can benefit from angle dependency of the pulse shape at a radar receiver antenna. We propose a localization approach that utilizes the embedded directivity in ultra-wideband (UWB antennas to estimate target positions. A single radar unit sensing operation of indoor targets surrounded by interior walls is considered, where interior wall multipaths are exploited to provide target cross-range. This exploitation assumes resolvability of the multipath components, which is made possible by the virtue of using UWB radar signals. The proposed approach is most attractive when only few multipaths are detectable due to propagation obstructions or owing to low signal-to-noise ratios. Both simulated and experimental data are used to demonstrate the effectiveness of the proposed approach.

Mapping GPS multipath: a case study for the lunar laser ranger ...

African Journals Online (AJOL)

We investigate and attempt to map multipath as part of the site investigation for the installation of the timing antenna for lunar laser ranging applications at the Hartebeesthoek Radio Astronomy Observatory (HartRAO). A high-resolution wavelet power spectrum and a standard deviation parameter are used to map multipath ...

Mapping GPS Multipath: a Case Study for the Lunar Laser Ranger ...

African Journals Online (AJOL)

Cilence Munghemezulu

Mapping GPS Multipath: a Case Study for the Lunar Laser .... assess the level of multipath in the vicinity of the GNSS antenna by making use of ..... navigation in urban environment', Indian Journal of Radio and Space Physics, vol. ... Torrence C & Compo GP 1998, 'A practical guide to wavelet Analysis', Bulletin of the ...

Acoustic MIMO communications in a very shallow water channel

Science.gov (United States)

Zhou, Yuehai; Cao, Xiuling; Tong, Feng

2015-12-01

Underwater acoustic channels pose significant difficulty for the development of high speed communication due to highly limited band-width as well as hostile multipath interference. Enlightened by rapid progress of multiple input multiple output (MIMO) technologies in wireless communication scenarios, MIMO systems offer a potential solution by enabling multiple spatially parallel communication channels to improve communication performance as well as capacity. For MIMO acoustic communications, deep sea channels offer substantial spatial diversity among multiple channels that can be exploited to address simultaneous multipath and co-channel interference. At the same time, there are increasing requirements for high speed underwater communication in very shallow water area (for example, a depth less than 10 m). In this paper, a space-time multichannel adaptive receiver consisting of multiple decision feedback equalizers (DFE) is adopted as the receiver for a very shallow water MIMO acoustic communication system. The performance of multichannel DFE receivers with relatively small number of receiving elements are analyzed and compared with that of the multichannel time reversal receiver to evaluate the impact of limited spatial diversity on multi-channel equalization and time reversal processing. The results of sea trials in a very shallow water channel are presented to demonstrate the feasibility of very shallow water MIMO acoustic communication.

Comparison of Pilot Symbol Embedded Channel Estimation Algorithms

Directory of Open Access Journals (Sweden)

P. Kadlec

2009-12-01

Full Text Available In the paper, algorithms of the pilot symbol embedded channel estimation are compared. Attention is turned to the Least Square (LS channel estimation and the Sliding Correlator (SC algorithm. Both algorithms are implemented in Matlab to estimate the Channel Impulse Response (CIR of a channel exhibiting multi-path propagation. Algorithms are compared from the viewpoint of computational demands, influence of the Additive White Gaussian Noise (AWGN, an embedded pilot symbol and a computed CIR over the estimation error.

An overview of turbo decoding on fading channels

OpenAIRE

ATILGAN, Doğan

2009-01-01

A review of turbo coding and decoding has been presented in the literature [1]. In that paper, turbo coding and decoding on AWGN (Additive White Gaussian Noise) channels has been elaborated. In wireless communications, a phenomennon called multipath fading is frequently encountered. Therefore, investigation of efficient techniques to tackle with the destructive effects of fading is essential. Turbo coding has been proven as an efficient channel coding technique for AWGN channels. Some of the ...

Feedforward Delay Estimators in Adverse Multipath Propagation for Galileo and Modernized GPS Signals

Directory of Open Access Journals (Sweden)

Lohan Elena Simona

2006-01-01

Full Text Available The estimation with high accuracy of the line-of-sight delay is a prerequisite for all global navigation satellite systems. The delay locked loops and their enhanced variants are the structures of choice for the commercial GNSS receivers, but their performance in severe multipath scenarios is still rather limited. The new satellite positioning system proposals specify higher code-epoch lengths compared to the traditional GPS signal and the use of a new modulation, the binary offset carrier (BOC modulation, which triggers new challenges in the delay tracking stage. We propose and analyze here the use of feedforward delay estimation techniques in order to improve the accuracy of the delay estimation in severe multipath scenarios. First, we give an extensive review of feedforward delay estimation techniques for CDMA signals in fading channels, by taking into account the impact of BOC modulation. Second, we extend the techniques previously proposed by the authors in the context of wideband CDMA delay estimation (e.g., Teager-Kaiser and the projection onto convex sets to the BOC-modulated signals. These techniques are presented as possible alternatives to the feedback tracking loops. A particular attention is on the scenarios with closely spaced paths. We also discuss how these feedforward techniques can be implemented via DSPs.

A novel integrated circuit for semiconductor radiation detectors with sparse readout

International Nuclear Information System (INIS)

Zhang Yacong; Chen Zhognjian; Lu Wengao; Zhao Baoying; Ji Lijiu

2008-01-01

A novel fully integrated CMOS readout circuit for semiconductor radiation detector with sparse readout is presented. The new sparse scheme is: when one channel is being read out, the trigger signal from other channels is delayed and then processed. Therefore, the dead time is reduced and so is the error rate. Besides sparse readout, sequential readout is also allowed, which means the analog voltages and addresses of all the channels are read out sequentially once there is a channel triggered. The circuit comprises Charge Sensitive Amplifier (CSA), pulse shaper, peak detect and hold circuit, and digital logic. A test chip of four channels designed in a 0.5 μ DPTM CMOS technology has been taped out. The results of post simulation indicate that the gain is 79.3 mV/fC with a linearity of 99.92%. The power dissipation is 4 mW per channel. Theory analysis and calculation shows that the error probability is approximately 2.5%, which means a reduction of about 37% is obtained compared with the traditional scanning scheme, assuming a 16-channel system with a particle rate of 100 k/s per channel. (authors)

Multipathing Via Three Parameter Common Image Gathers (CIGs) From Reverse Time Migration

Science.gov (United States)

Ostadhassan, M.; Zhang, X.

2015-12-01

A noteworthy problem for seismic exploration is effects of multipathing (both wanted or unwanted) caused by subsurface complex structures. We show that reverse time migration (RTM) combined with a unified, systematic three parameter framework that flexibly handles multipathing can be accomplished by adding one more dimension (image time) to the angle domain common image gather (ADCIG) data. RTM is widely used to generate prestack depth migration images. When using the cross-correlation image condition in 2D prestack migration in RTM, the usual practice is to sum over all the migration time steps. Thus all possible wave types and paths automatically contribute to the resulting image, including destructive wave interferences, phase shifts, and other distortions. One reason is that multipath (prismatic wave) contributions are not properly sorted and mapped in the ADCIGs. Also, multipath arrivals usually have different instantaneous attributes (amplitude, phase and frequency), and if not separated, the amplitudes and phases in the final prestack image will not stack coherently across sources. A prismatic path satisfies an image time for it's unique path; Cavalca and Lailly (2005) show that RTM images with multipaths can provide more complete target information in complex geology, as multipaths usually have different incident angles and amplitudes compared to primary reflections. If the image time slices within a cross-correlation common-source migration are saved for each image time, this three-parameter (incident angle, depth, image time) volume can be post-processed to generate separate, or composite, images of any desired subset of the migrated data. Images can by displayed for primary contributions, any combination of primary and multipath contributions (with or without artifacts), or various projections, including the conventional ADCIG (angle vs depth) plane. Examples show that signal from the true structure can be separated from artifacts caused by multiple

Multipath detection with the combination of SNR measurements - Example from urban environment

Science.gov (United States)

Špánik, Peter; Hefty, Ján

2017-12-01

Multipath is one of the most severe station-dependent error sources in both static and kinematic positioning. Relatively new and simple detection technique using the Signal-to-Noise (SNR) measurements on three frequencies will be presented based on idea of Strode and Groves. Exploitation of SNR measurements is benefi cial especially for their unambiguous character. Method is based on the fact that SNR values are closely linked with estimation of pseudo-ranges and phase measurements during signal correlation processing. Due to this connection, combination of SNR values can be used to detect anomalous behavior in received signal, however some kind of calibration in low multipath environment has to be done previously. In case of multipath, phase measurements on different frequencies will not be affected in the same manner. Specular multipath, e.g. from building wall introduces additional path delay which is interpreted differently on each of the used carrier, due to different wavelengths. Experimental results of multipath detection in urban environment will be presented. Originally proposed method is designed to work with three different frequencies in each epoch, thus only utilization of GPS Block II-F and Galileo satellites is possible. Simplification of detection statistics to use only two frequencies is made and results using GPS and GLONASS systems are presented along with results obtained using original formula.

Precise RFID localization in impaired environment through sparse signal recovery

Science.gov (United States)

Subedi, Saurav; Zhang, Yimin D.; Amin, Moeness G.

2013-05-01

Radio frequency identification (RFID) is a rapidly developing wireless communication technology for electronically identifying, locating, and tracking products, assets, and personnel. RFID has become one of the most important means to construct real-time locating systems (RTLS) that track and identify the location of objects in real time using simple, inexpensive tags and readers. The applicability and usefulness of RTLS techniques depend on their achievable accuracy. In particular, when multilateration-based localization techniques are exploited, the achievable accuracy primarily relies on the precision of the range estimates between a reader and the tags. Such range information can be obtained by using the received signal strength indicator (RSSI) and/or the phase difference of arrival (PDOA). In both cases, however, the accuracy is significantly compromised when the operation environment is impaired. In particular, multipath propagation significantly affects the measurement accuracy of both RSSI and phase information. In addition, because RFID systems are typically operated in short distances, RSSI and phase measurements are also coupled with the reader and tag antenna patterns, making accurate RFID localization very complicated and challenging. In this paper, we develop new methods to localize RFID tags or readers by exploiting sparse signal recovery techniques. The proposed method allows the channel environment and antenna patterns to be taken into account and be properly compensated at a low computational cost. As such, the proposed technique yields superior performance in challenging operation environments with the above-mentioned impairments.

Definition of multipath/RFI experiments for orbital testing with a small applications technology satellite

Science.gov (United States)

Birch, J. N.; French, R. H.

1972-01-01

An investigation was made to define experiments for collection of RFI and multipath data for application to a synchronous relay satellite/low orbiting satellite configuration. A survey of analytical models of the multipath signal was conducted. Data has been gathered concerning the existing RFI and other noise sources in various bands at VHF and UHF. Additionally, designs are presented for equipment to combat the effects of RFI and multipath: an adaptive delta mod voice system, a forward error control coder/decoder, a PN transmission system, and a wideband FM system. The performance of these systems was then evaluated. Techniques are discussed for measuring multipath and RFI. Finally, recommended data collection experiments are presented. An extensive tabulation is included of theoretical predictions of the amount of signal reflected from a rough, spherical earth.

Application of the Evidence Procedure to the Estimation of Wireless Channels

Directory of Open Access Journals (Sweden)

Fleury Bernard H

2007-01-01

Full Text Available We address the application of the Bayesian evidence procedure to the estimation of wireless channels. The proposed scheme is based on relevance vector machines (RVM originally proposed by M. Tipping. RVMs allow to estimate channel parameters as well as to assess the number of multipath components constituting the channel within the Bayesian framework by locally maximizing the evidence integral. We show that, in the case of channel sounding using pulse-compression techniques, it is possible to cast the channel model as a general linear model, thus allowing RVM methods to be applied. We extend the original RVM algorithm to the multiple-observation/multiple-sensor scenario by proposing a new graphical model to represent multipath components. Through the analysis of the evidence procedure we develop a thresholding algorithm that is used in estimating the number of components. We also discuss the relationship of the evidence procedure to the standard minimum description length (MDL criterion. We show that the maximum of the evidence corresponds to the minimum of the MDL criterion. The applicability of the proposed scheme is demonstrated with synthetic as well as real-world channel measurements, and a performance increase over the conventional MDL criterion applied to maximum-likelihood estimates of the channel parameters is observed.

Carrier frequency offset estimation for an acoustic-electric channel using 16 QAM modulation

Science.gov (United States)

Cunningham, Michael T.; Anderson, Leonard A.; Wilt, Kyle R.; Chakraborty, Soumya; Saulnier, Gary J.; Scarton, Henry A.

2016-05-01

Acoustic-electric channels can be used to send data through metallic barriers, enabling communications where electromagnetic signals are ineffective. This paper considers an acoustic-electric channel that is formed by mounting piezoelectric transducers on metallic barriers that are separated by a thin water layer. The transducers are coupled to the barriers using epoxy and the barriers are positioned to axially-align the PZTs, maximizing energy transfer efficiency. The electrical signals are converted by the transmitting transducers into acoustic waves, which propagate through the elastic walls and water medium to the receiving transducers. The reverberation of the acoustic signals in these channels can produce multipath distortion with a significant delay spread that introduces inter-symbol interference (ISI) into the received signal. While the multipath effects can be severe, the channel does not change rapidly which makes equalization easier. Here we implement a 16-QAM system on this channel, including a method for obtaining accurate carrier frequency offset (CFO) estimates in the presence of the quasi-static multipath propagation. A raised-power approach is considered but found to suffer from excessive data noise resulting from the ISI. An alternative approach that utilizes a pilot tone burst at the start of a data packet is used for CFO estimation and found to be effective. The autocorrelation method is used to estimate the frequency of the received burst. A real-time prototype of the 16 QAM system that uses a Texas Instruments MSP430 microcontroller-based transmitter and a personal computer-based receiver is presented along with performance results.

On the Performance Analysis of Digital Communications over Weibull-Gamma Channels

KAUST Repository

Ansari, Imran Shafique; Alouini, Mohamed-Slim

2015-01-01

In this work, the performance analysis of digital communications over a composite Weibull-Gamma (WG) multipath-fading and shadowing channel is presented wherein WG distribution is appropriate for modeling fading environments when multipath is superimposed on shadowing. More specifically, in this work, exact closed-form expressions are derived for the probability density function, the cumulative distribution function, the moment generating function, and the moments of a composite WG channel. Capitalizing on these results, new exact closed-form expressions are offered for the outage probability, the higher- order amount of fading, the average error rate for binary and M-ary modulation schemes, and the ergodic capacity under various types of transmission policies, mostly in terms of Meijer's G functions. These new analytical results were also verified via computer-based Monte-Carlo simulation results. © 2015 IEEE.

Performance Evaluation of Concurrent Multipath Video Streaming in Multihomed Mobile Networks

Directory of Open Access Journals (Sweden)

James Nightingale

2013-01-01

Full Text Available High-quality real-time video streaming to users in mobile networks is challenging due to the dynamically changing nature of the network paths, particularly the limited bandwidth and varying end-to-end delay. In this paper, we empirically investigate the performance of multipath streaming in the context of multihomed mobile networks. Existing schemes that make use of the aggregated bandwidth of multiple paths can overcome bandwidth limitations on a single path but suffer an efficiency penalty caused by retransmission of lost packets in reliable transport schemes or path switching overheads in unreliable transport schemes. This work focuses on the evaluation of schemes to permit concurrent use of multiple paths to deliver video streams. A comprehensive streaming framework for concurrent multipath video streaming is proposed and experimentally evaluated, using current state-of-the-art H.264 Scalable Video Coding (H.264/SVC and the next generation High Efficiency Video Coding (HEVC standards. It provides a valuable insight into the benefit of using such schemes in conjunction with encoder specific packet prioritisation mechanisms for quality-aware packet scheduling and scalable streaming. The remaining obstacles to deployment of concurrent multipath schemes are identified, and the challenges in realising HEVC based concurrent multipath streaming are highlighted.

A Forward GPS Multipath Simulator Based on the Vegetation Radiative Transfer Equation Model.

Science.gov (United States)

Wu, Xuerui; Jin, Shuanggen; Xia, Junming

2017-06-05

Global Navigation Satellite Systems (GNSS) have been widely used in navigation, positioning and timing. Nowadays, the multipath errors may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), i.e., GPS-Multipath Reflectometry (GPS-MR). However, bistatic scattering properties and the relation between GPS observables and geophysical parameters are not clear, e.g., vegetation. In this paper, a new element on bistatic scattering properties of vegetation is incorporated into the traditional GPS-MR model. This new element is the first-order radiative transfer equation model. The new forward GPS multipath simulator is able to explicitly link the vegetation parameters with GPS multipath observables (signal-to-noise-ratio (SNR), code pseudorange and carrier phase observables). The trunk layer and its corresponding scattering mechanisms are ignored since GPS-MR is not suitable for high forest monitoring due to the coherence of direct and reflected signals. Based on this new model, the developed simulator can present how the GPS signals (L1 and L2 carrier frequencies, C/A, P(Y) and L2C modulations) are transmitted (scattered and absorbed) through vegetation medium and received by GPS receivers. Simulation results show that the wheat will decrease the amplitudes of GPS multipath observables (SNR, phase and code), if we increase the vegetation moisture contents or the scatters sizes (stem or leaf). Although the Specular-Ground component dominates the total specular scattering, vegetation covered ground soil moisture has almost no effects on the final multipath signatures. Our simulated results are consistent with previous results for environmental parameter detections by GPS-MR.

Multipath detection with the combination of SNR measurements – Example from urban environment

Directory of Open Access Journals (Sweden)

Špánik Peter

2017-12-01

Full Text Available Multipath is one of the most severe station-dependent error sources in both static and kinematic positioning. Relatively new and simple detection technique using the Signal-to-Noise (SNR measurements on three frequencies will be presented based on idea of Strode and Groves. Exploitation of SNR measurements is benefi cial especially for their unambiguous character. Method is based on the fact that SNR values are closely linked with estimation of pseudo-ranges and phase measurements during signal correlation processing. Due to this connection, combination of SNR values can be used to detect anomalous behavior in received signal, however some kind of calibration in low multipath environment has to be done previously. In case of multipath, phase measurements on different frequencies will not be affected in the same manner. Specular multipath, e.g. from building wall introduces additional path delay which is interpreted differently on each of the used carrier, due to different wavelengths. Experimental results of multipath detection in urban environment will be presented. Originally proposed method is designed to work with three different frequencies in each epoch, thus only utilization of GPS Block II-F and Galileo satellites is possible. Simplification of detection statistics to use only two frequencies is made and results using GPS and GLONASS systems are presented along with results obtained using original formula.

Investigating the Performance of the MPT Multipath Communication Library in IPv4 and IPv6

Directory of Open Access Journals (Sweden)

Béla Almási

2016-01-01

Full Text Available The currently used mobile devices (laptops, tablets, mobile phones contain many built-in network cards for communication (e.g. Wi-Fi, 3G, Bluetooth, etc.. A natural request could be combining the resources of the different network connection possibilities in order to increase the throughput of the communication. Unfortunately the standard IP communication technology does not support it: the communication is restricted to one IP address (i.e. to one interface. The Multipath TCP (MPTCP specification (appeared in January 2013 offers a Transport layer solution of using more than one interface in a TCP communication session. In this paper we investigate a Network layer solution. The MPT multipath communication library opens the multipath communication possibility in the Network layer. Using the MPT library, applications built on the UDP protocol are also able to perform multipath communication. The MPT library was developed by using a full dual-stack technology, which means the MPT based multipath environment can be used both in IPv4 and IPv6. Protocol version change is also possible: an IPv6 based application is able to run in an IPv4 multipath environment, and an IPv4 application can be used in an IPv6 multipath environment. In this paper we give a short overview on the MPT communication library’s working mechanism and detailed numerical examples will be shown to present how the MPT library aggregates the paths’ throughput in IPv4 and IPv6 environments. The main contribution of the paper is to demonstrate the effective throughput aggregation property of the MPT library in IPv6 and in mixed (i.e. protocol version change environments.

ANALYSIS OF MULTIPATH PIXELS IN SAR IMAGES

Directory of Open Access Journals (Sweden)

J. W. Zhao

2016-06-01

Full Text Available As the received radar signal is the sum of signal contributions overlaid in one single pixel regardless of the travel path, the multipath effect should be seriously tackled as the multiple bounce returns are added to direct scatter echoes which leads to ghost scatters. Most of the existing solution towards the multipath is to recover the signal propagation path. To facilitate the signal propagation simulation process, plenty of aspects such as sensor parameters, the geometry of the objects (shape, location, orientation, mutual position between adjacent buildings and the physical parameters of the surface (roughness, correlation length, permittivitywhich determine the strength of radar signal backscattered to the SAR sensor should be given in previous. However, it's not practical to obtain the highly detailed object model in unfamiliar area by field survey as it's a laborious work and time-consuming. In this paper, SAR imaging simulation based on RaySAR is conducted at first aiming at basic understanding of multipath effects and for further comparison. Besides of the pre-imaging simulation, the product of the after-imaging, which refers to radar images is also taken into consideration. Both Cosmo-SkyMed ascending and descending SAR images of Lupu Bridge in Shanghai are used for the experiment. As a result, the reflectivity map and signal distribution map of different bounce level are simulated and validated by 3D real model. The statistic indexes such as the phase stability, mean amplitude, amplitude dispersion, coherence and mean-sigma ratio in case of layover are analyzed with combination of the RaySAR output.

On the Performance Analysis of Digital Communications over Weibull-Gamma Channels

KAUST Repository

Ansari, Imran Shafique

2015-05-01

In this work, the performance analysis of digital communications over a composite Weibull-Gamma (WG) multipath-fading and shadowing channel is presented wherein WG distribution is appropriate for modeling fading environments when multipath is superimposed on shadowing. More specifically, in this work, exact closed-form expressions are derived for the probability density function, the cumulative distribution function, the moment generating function, and the moments of a composite WG channel. Capitalizing on these results, new exact closed-form expressions are offered for the outage probability, the higher- order amount of fading, the average error rate for binary and M-ary modulation schemes, and the ergodic capacity under various types of transmission policies, mostly in terms of Meijer\\'s G functions. These new analytical results were also verified via computer-based Monte-Carlo simulation results. © 2015 IEEE.

SAR Image Simulation of Ship Targets Based on Multi-Path Scattering

Science.gov (United States)

Guo, Y.; Wang, H.; Ma, H.; Li, K.; Xia, Z.; Hao, Y.; Guo, H.; Shi, H.; Liao, X.; Yue, H.

2018-04-01

Synthetic Aperture Radar (SAR) plays an important role in the classification and recognition of ship targets because of its all-weather working ability and fine resolution. In SAR images, besides the sea clutter, the influence of the sea surface on the radar echo is also known as the so-called multipath effect. These multipath effects will generate some extra "pseudo images", which may cause the distortion of the target image and affect the estimation of the characteristic parameters. In this paper,the multipath effect of rough sea surface and its influence on the estimation of ship characteristic parameters are studied. The imaging of the first and the secondary reflection of sea surface is presented . The artifacts not only overlap with the image of the target itself, but may also appear in the sea near the target area. It is difficult to distinguish them, and this artifact has an effect on the length and width of the ship.

Encoding of rat working memory by power of multi-channel local field potentials via sparse non-negative matrix factorization

Institute of Scientific and Technical Information of China (English)

Xu Liu; Tiao-Tiao Liu; Wen-Wen Bai; Hu Yi; Shuang-Yan Li; Xin Tian

2013-01-01

Working memory plays an important role in human cognition.This study investigated how working memory was encoded by the power of multi-channel local field potentials (LFPs) based on sparse nonnegative matrix factorization (SNMF).SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four Sprague-Dawley rats during a memory task in a Y maze,with 10 trials for each rat.Then the power-increased LFP components were selected as working memory-related features and the other components were removed.After that,the inverse operation of SNMF was used to study the encoding of working memory in the timefrequency domain.We demonstrated that theta and gamma power increased significantly during the working memory task.The results suggested that postsynaptic activity was simulated well by the sparse activity model.The theta and gamma bands were meaningful for encoding working memory.

Linear Regression on Sparse Features for Single-Channel Speech Separation

DEFF Research Database (Denmark)

Schmidt, Mikkel N.; Olsson, Rasmus Kongsgaard

2007-01-01

In this work we address the problem of separating multiple speakers from a single microphone recording. We formulate a linear regression model for estimating each speaker based on features derived from the mixture. The employed feature representation is a sparse, non-negative encoding of the speech...... mixture in terms of pre-learned speaker-dependent dictionaries. Previous work has shown that this feature representation by itself provides some degree of separation. We show that the performance is significantly improved when regression analysis is performed on the sparse, non-negative features, both...

Noise-based frequency offset modulation in wideband frequency-selective fading channels

NARCIS (Netherlands)

Meijerink, Arjan; Cotton, S.L.; Bentum, Marinus Jan; Scanlon, W.G.

2009-01-01

A frequency offset modulation scheme using wideband noise carriers is considered. The main advantage of such a scheme is that it enables fast receiver synchronization without channel adaptation, while providing robustness to multipath fading and in-band interference. This is important for low-power

Multipath TCP for user cooperation in wireless networks

CERN Document Server

Zhou, Dizhi

2014-01-01

This brief presents several enhancement modules to Multipath Transmission Control Protocol (MPTCP) in order to support stable and efficient multipath transmission with user cooperation in the Long Term Evolution (LTE) network. The text explains how these enhancements provide a stable aggregate throughput to the upper-layer applications; guarantee a steady goodput, which is the real application-layer perceived throughput; and ensure that the local traffic of the relays is not adversely affected when the relays are forwarding data for the destination. The performance of the proposed solutions is extensively evaluated using various scenarios. The simulation results demonstrate that the proposed modules can achieve a stable aggregate throughput and significantly improve the goodput by 1.5 times on average. The brief also shows that these extensions can well respect the local traffic of the relays and motivate the relay users to provide the relaying service.

Mobile radio channel as a complex medium

DEFF Research Database (Denmark)

Matic, Dusan; Prasad, Ramjee; Kalluri, Dikshitulu K.

2001-01-01

physical phenomena, their implications on the transmitted signal, and how the radio channels are modelled. Special attention is given to the small-scale effects, such as multipath, and Rayleigh and Rice distributions of received signal, as these dominate in the case of indoor communication systems.......Optical fibres have almost unlimited capacity, but can not the address the users desire for mobility and ubiquitous access. The synergy of these two worlds can be seen in the direction of the Radio-over-Fibre. This paper presents to the reader an introduction for the mobile radio channel - basic...

Minimum Probability of Error-Based Equalization Algorithms for Fading Channels

Directory of Open Access Journals (Sweden)

Janos Levendovszky

2007-06-01

Full Text Available Novel channel equalizer algorithms are introduced for wireless communication systems to combat channel distortions resulting from multipath propagation. The novel algorithms are based on newly derived bounds on the probability of error (PE and guarantee better performance than the traditional zero forcing (ZF or minimum mean square error (MMSE algorithms. The new equalization methods require channel state information which is obtained by a fast adaptive channel identification algorithm. As a result, the combined convergence time needed for channel identification and PE minimization still remains smaller than the convergence time of traditional adaptive algorithms, yielding real-time equalization. The performance of the new algorithms is tested by extensive simulations on standard mobile channels.

Spatial Dynamic Wideband Modeling of the MIMO Satellite-to-Earth Channel

OpenAIRE

Lehner, Andreas; Steingass, Alexander

2014-01-01

A novel MIMO (multiple input multiple output) satellite channel model that allows the generation of associated channel impulse response (CIR) time series depending on the movement profile of a land mobile terminal is presented in this paper. Based on high precise wideband measurements in L-band the model reproduces the correlated shadowing and multipath conditions in rich detail. The model includes time and space variant echo signals appearing and disappearing in dependence on the receive ...

Heterogeneous all-solid multicore fiber based multipath Michelson interferometer for high temperature sensing.

Science.gov (United States)

Duan, Li; Zhang, Peng; Tang, Ming; Wang, Ruoxu; Zhao, Zhiyong; Fu, Songnian; Gan, Lin; Zhu, Benpeng; Tong, Weijun; Liu, Deming; Shum, Perry Ping

2016-09-05

A compact high temperature sensor utilizing a multipath Michelson interferometer (MI) structure based on weak coupling multicore fiber (MCF) is proposed and experimentally demonstrated. The device is fabricated by program-controlled tapering the spliced region between single mode fiber (SMF) and a segment of MCF. After that, a spherical reflective structure is formed by arc-fusion splicing the end face of MCF. Theoretical analysis has been implemented for this specific multipath MI structure; beam propagation method based simulation and corresponding experiments were performed to investigate the effect of taper and spherical end face on system's performance. Benefiting from the multipath interferences and heterogeneous structure between the center core and surrounding cores of the all-solid MCF, an enhanced temperature sensitivity of 165 pm/°C up to 900°C and a high-quality interference spectrum with 25 dB fringe visibility were achieved.

Mitigating Receiver’s Buffer Blocking by Delay Aware Packet Scheduling in Multipath Data Transfer

OpenAIRE

Sarwar, Golam; Boreli, Roksana; Lochin, Emmanuel; Mifdaoui, Ahlem; Smith, Guillaume

2013-01-01

Reliable in-order multi-path data transfer under asymmetric heterogeneous network conditions has known problems related to receiver's buffer blocking, caused by out of order packet arrival. Consequently, the aggregate capacity from multiple paths, which theoretically should be available to and achievable by the multi-path transport protocol, is practically severely underutilized. Several mitigation techniques have been proposed to address this issue mostly by using various packet retransmissi...

Multipath interference test method for distributed amplifiers

Science.gov (United States)

Okada, Takahiro; Aida, Kazuo

2005-12-01

A method for testing distributed amplifiers is presented; the multipath interference (MPI) is detected as a beat spectrum between the multipath signal and the direct signal using a binary frequency shifted keying (FSK) test signal. The lightwave source is composed of a DFB-LD that is directly modulated by a pulse stream passing through an equalizer, and emits the FSK signal of the frequency deviation of about 430MHz at repetition rate of 80-100 kHz. The receiver consists of a photo-diode and an electrical spectrum analyzer (ESA). The base-band power spectrum peak appeared at the frequency of the FSK frequency deviation can be converted to amount of MPI using a calibration chart. The test method has improved the minimum detectable MPI as low as -70 dB, compared to that of -50 dB of the conventional test method. The detailed design and performance of the proposed method are discussed, including the MPI simulator for calibration procedure, computer simulations for evaluating the error caused by the FSK repetition rate and the fiber length under test and experiments on singlemode fibers and distributed Raman amplifier.

Evaluation of 5G Modulation Candidates WCP-COQAM, GFDM-OQAM, and FBMC-OQAM in Low-Band Highly Dispersive Wireless Channels

Directory of Open Access Journals (Sweden)

Aitor Lizeaga

2017-01-01

Full Text Available We analyse some of the candidates for modulations for 5G: FBMC-OQAM, GFDM-OQAM, and WCP-COQAM. Unlike most of the related bibliographies, which are oriented to mobile communications, our research is focused on 5G in cognitive radio based industrial wireless communications. According to the ultrareliability and low-latency requirements of industrial communications, we simulate the aforementioned modulations in low-band transmissions (carrier frequencies below 6 GHz and a bandwidth narrower than 100 MHz through large indoor spaces and severe multipath channels that emulate industrial halls. Moreover, we give detailed information about WCP-COQAM and how the windowing affects the protection against multipath effect and reduces spectral efficiency compared to GFDM-OQAM. We also compare the aforementioned filtered multicarrier techniques and OFDM in terms of robustness against multipath channels, power spectral density, and spectral efficiency. Based on these results, we aim at providing an approximate idea about the suitability of 5G MCM candidates for industrial wireless communications based on CR.

PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks

Directory of Open Access Journals (Sweden)

Mansour Sheikhan

2012-06-01

Full Text Available Mobile ad-hoc network (MANET is a dynamic collection of mobile computers without the need for any existing infrastructure. Nodes in a MANET act as hosts and routers. Designing of robust routing algorithms for MANETs is a challenging task. Disjoint multipath routing protocols address this problem and increase the reliability, security and lifetime of network. However, selecting an optimal multipath is an NP-complete problem. In this paper, Hopfield neural network (HNN which its parameters are optimized by particle swarm optimization (PSO algorithm is proposed as multipath routing algorithm. Link expiration time (LET between each two nodes is used as the link reliability estimation metric. This approach can find either node-disjoint or link-disjoint paths in singlephase route discovery. Simulation results confirm that PSO-HNN routing algorithm has better performance as compared to backup path set selection algorithm (BPSA in terms of the path set reliability and number of paths in the set.

MIMO Channel Model with Propagation Mechanism and the Properties of Correlation and Eigenvalue in Mobile Environments

Directory of Open Access Journals (Sweden)

Yuuki Kanemiyo

2012-01-01

Full Text Available This paper described a spatial correlation and eigenvalue in a multiple-input multiple-output (MIMO channel. A MIMO channel model with a multipath propagation mechanism was proposed and showed the channel matrix. The spatial correlation coefficient formula −,′−′( between MIMO channel matrix elements was derived for the model and was expressed as a directive wave term added to the product of mobile site correlation −′( and base site correlation −′( without LOS path, which are calculated independently of each other. By using −,′−′(, it is possible to create the channel matrix element with a fixed correlation value estimated by −,′−′( for a given multipath condition and a given antenna configuration. Furthermore, the correlation and the channel matrix eigenvalue were simulated, and the simulated and theoretical correlation values agreed well. The simulated eigenvalue showed that the average of the first eigenvalue λ1 hardly depends on the correlation −,′−′(, but the others do depend on −,′−′( and approach 1 as −,′−′( decreases. Moreover, as the path moves into LOS, the 1 state with mobile movement becomes more stable than the 1 of NLOS path.

Characteristics of BeiDou Navigation Satellite System Multipath and Its Mitigation Method Based on Kalman Filter and Rauch-Tung-Striebel Smoother.

Science.gov (United States)

Zhang, Qiuzhao; Yang, Wei; Zhang, Shubi; Liu, Xin

2018-01-12

Global Navigation Satellite System (GNSS) carrier phase measurement for short baseline meets the requirements of deformation monitoring of large structures. However, the carrier phase multipath effect is the main error source with double difference (DD) processing. There are lots of methods to deal with the multipath errors of Global Position System (GPS) carrier phase data. The BeiDou navigation satellite System (BDS) multipath mitigation is still a research hotspot because the unique constellation design of BDS makes it different to mitigate multipath effects compared to GPS. Multipath error periodically repeats for its strong correlation to geometry of satellites, reflective surface and antenna which is also repetitive. We analyzed the characteristics of orbital periods of BDS satellites which are consistent with multipath repeat periods of corresponding satellites. The results show that the orbital periods and multipath periods for BDS geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites are about one day but the periods of MEO satellites are about seven days. The Kalman filter (KF) and Rauch-Tung-Striebel Smoother (RTSS) was introduced to extract the multipath models from single difference (SD) residuals with traditional sidereal filter (SF). Wavelet filter and Empirical mode decomposition (EMD) were also used to mitigate multipath effects. The experimental results show that the three filters methods all have obvious effect on improvement of baseline accuracy and the performance of KT-RTSS method is slightly better than that of wavelet filter and EMD filter. The baseline vector accuracy on east, north and up (E, N, U) components with KF-RTSS method were improved by 62.8%, 63.6%, 62.5% on day of year 280 and 57.3%, 53.4%, 55.9% on day of year 281, respectively.

Characteristics of BeiDou Navigation Satellite System Multipath and Its Mitigation Method Based on Kalman Filter and Rauch-Tung-Striebel Smoother

Directory of Open Access Journals (Sweden)

Qiuzhao Zhang

2018-01-01

Full Text Available Global Navigation Satellite System (GNSS carrier phase measurement for short baseline meets the requirements of deformation monitoring of large structures. However, the carrier phase multipath effect is the main error source with double difference (DD processing. There are lots of methods to deal with the multipath errors of Global Position System (GPS carrier phase data. The BeiDou navigation satellite System (BDS multipath mitigation is still a research hotspot because the unique constellation design of BDS makes it different to mitigate multipath effects compared to GPS. Multipath error periodically repeats for its strong correlation to geometry of satellites, reflective surface and antenna which is also repetitive. We analyzed the characteristics of orbital periods of BDS satellites which are consistent with multipath repeat periods of corresponding satellites. The results show that the orbital periods and multipath periods for BDS geostationary earth orbit (GEO and inclined geosynchronous orbit (IGSO satellites are about one day but the periods of MEO satellites are about seven days. The Kalman filter (KF and Rauch-Tung-Striebel Smoother (RTSS was introduced to extract the multipath models from single difference (SD residuals with traditional sidereal filter (SF. Wavelet filter and Empirical mode decomposition (EMD were also used to mitigate multipath effects. The experimental results show that the three filters methods all have obvious effect on improvement of baseline accuracy and the performance of KT-RTSS method is slightly better than that of wavelet filter and EMD filter. The baseline vector accuracy on east, north and up (E, N, U components with KF-RTSS method were improved by 62.8%, 63.6%, 62.5% on day of year 280 and 57.3%, 53.4%, 55.9% on day of year 281, respectively.

Experimental Investigation of Some Effects of Multipath Propagation on a Line-of-Sight Path at 14 GHz

DEFF Research Database (Denmark)

Stephansen, E.; Mogensen, G.

1979-01-01

A microwave line-of-sight propagation experiment is carried out in Denmark at frequencies around 14 GHz. Results from long term measurements of multipath propagation are presented. The multipath fade durations are shown to be log-normally distributed. The level dependence of the probability of fa...

Energy-Efficient Channel Coding Strategy for Underwater Acoustic Networks

Directory of Open Access Journals (Sweden)

Grasielli Barreto

2017-03-01

Full Text Available Underwater acoustic networks (UAN allow for efficiently exploiting and monitoring the sub-aquatic environment. These networks are characterized by long propagation delays, error-prone channels and half-duplex communication. In this paper, we address the problem of energy-efficient communication through the use of optimized channel coding parameters. We consider a two-layer encoding scheme employing forward error correction (FEC codes and fountain codes (FC for UAN scenarios without feedback channels. We model and evaluate the energy consumption of different channel coding schemes for a K-distributed multipath channel. The parameters of the FEC encoding layer are optimized by selecting the optimal error correction capability and the code block size. The results show the best parameter choice as a function of the link distance and received signal-to-noise ratio.

Development and testing of a new ray-tracing approach to GNSS carrier-phase multipath modelling

Science.gov (United States)

Lau, Lawrence; Cross, Paul

2007-11-01

Multipath is one of the most important error sources in Global Navigation Satellite System (GNSS) carrier-phase-based precise relative positioning. Its theoretical maximum is a quarter of the carrier wavelength (about 4.8 cm for the Global Positioning System (GPS) L1 carrier) and, although it rarely reaches this size, it must clearly be mitigated if millimetre-accuracy positioning is to be achieved. In most static applications, this may be accomplished by averaging over a sufficiently long period of observation, but in kinematic applications, a modelling approach must be used. This paper is concerned with one such approach: the use of ray-tracing to reconstruct the error and therefore remove it. In order to apply such an approach, it is necessary to have a detailed understanding of the signal transmitted from the satellite, the reflection process, the antenna characteristics and the way that the reflected and direct signal are processed within the receiver. This paper reviews all of these and introduces a formal ray-tracing method for multipath estimation based on precise knowledge of the satellite reflector antenna geometry and of the reflector material and antenna characteristics. It is validated experimentally using GPS signals reflected from metal, water and a brick building, and is shown to be able to model most of the main multipath characteristics. The method will have important practical applications for correcting for multipath in well-constrained environments (such as at base stations for local area GPS networks, at International GNSS Service (IGS) reference stations, and on spacecraft), and it can be used to simulate realistic multipath errors for various performance analyses in high-precision positioning.

Receiver-Based Ad Hoc On Demand Multipath Routing Protocol for Mobile Ad Hoc Networks.

Science.gov (United States)

Al-Nahari, Abdulaziz; Mohamad, Mohd Murtadha

2016-01-01

Decreasing the route rediscovery time process in reactive routing protocols is challenging in mobile ad hoc networks. Links between nodes are continuously established and broken because of the characteristics of the network. Finding multiple routes to increase the reliability is also important but requires a fast update, especially in high traffic load and high mobility where paths can be broken as well. The sender node keeps re-establishing path discovery to find new paths, which makes for long time delay. In this paper we propose an improved multipath routing protocol, called Receiver-based ad hoc on demand multipath routing protocol (RB-AOMDV), which takes advantage of the reliability of the state of the art ad hoc on demand multipath distance vector (AOMDV) protocol with less re-established discovery time. The receiver node assumes the role of discovering paths when finding data packets that have not been received after a period of time. Simulation results show the delay and delivery ratio performances are improved compared with AOMDV.

Over-the-Horizon Radar Multipath and Multisensor Track Fusion Algorithm Development

National Research Council Canada - National Science Library

Sarunic, P

2001-01-01

.... However the data is often subject to ambiguity and uncertainty due to the complexities of the HF signal propagation environment, which give rise to multipath OTHR tracks, as well as ambiguities...

Low-sampling-rate M-ary multiple access UWB communications in multipath channels

KAUST Repository

Alkhodary, Mohammad T.

2015-08-31

The desirable characteristics of ultra-wideband (UWB) technology are challenged by formidable sampling frequency, performance degradation in the presence of multi-user interference, and complexity of the receiver due to the channel estimation process. In this paper, a low-rate-sampling technique is used to implement M-ary multiple access UWB communications, in both the detection and channel estimation stages. A novel approach is used for multiple-access-interference (MAI) cancelation for the purpose of channel estimation. Results show reasonable performance of the proposed receiver for different number of users operating many times below Nyquist rate.

Low-sampling-rate M-ary multiple access UWB communications in multipath channels

KAUST Repository

Alkhodary, Mohammad T.; Ballal, Tarig; Al-Naffouri, Tareq Y.; Muqaibel, Ali H.

2015-01-01

The desirable characteristics of ultra-wideband (UWB) technology are challenged by formidable sampling frequency, performance degradation in the presence of multi-user interference, and complexity of the receiver due to the channel estimation process. In this paper, a low-rate-sampling technique is used to implement M-ary multiple access UWB communications, in both the detection and channel estimation stages. A novel approach is used for multiple-access-interference (MAI) cancelation for the purpose of channel estimation. Results show reasonable performance of the proposed receiver for different number of users operating many times below Nyquist rate.

Performance Analysis of Wavelet Channel Coding in COST207-based Channel Models on Simulated Radio-over-Fiber Systems at the W-Band

DEFF Research Database (Denmark)

Cavalcante, Lucas Costa Pereira; Silveira, Luiz F. Q.; Rommel, Simon

2016-01-01

Millimeter wave communications based on photonic technologies have gained increased attention to provide optic fiber-like capacity in wireless environments. However, the new hybrid fiber-wireless channel represents new challenges in terms of signal transmission performance analysis. Traditionally......, such systems use diversity schemes in combination with digital signal processing (DSP) techniques to overcome effects such as fading and inter-symbol interference (ISI). Wavelet Channel Coding (WCC) has emerged as a technique to minimize the fading effects of wireless channels, which is a mayor challenge...... in systems operating in the millimeter wave regime. This work takes the WCC one step beyond by performance evaluation in terms of bit error probability, over time-varying, frequency-selective multipath Rayleigh fading channels. The adopted propagation model follows the COST207 norm, the main international...

Towards suppression of all harmonics in a polyphase multipath transmitter

NARCIS (Netherlands)

Subhan, S.; Klumperink, Eric A.M.; Nauta, Bram

2011-01-01

This work proposes a direct conversion transmitter architecture intended for cognitive radio applications. The architecture is based on the poly-phase multipath technique, which has been shown to cancel out many of the harmonics, sidebands and nonlinearity contributions of a power up-converter using

D/A Resolution Impact on a Poly-phase Multipath Transmitter

NARCIS (Netherlands)

Subhan, S.; Klumperink, Eric A.M.; Nauta, Bram

2008-01-01

In recent publications the Poly-phase multipath technique has been shown to produce a clean output spectrum for a power upconverter (PU) architecture. The technique utilizes frequency independent phase shifts before and after a nonlinear element to cancel out the harmonics and sidebands due to the

Determination of scattering center of multipath signals using geometric optics and Fresnel zone concepts

Directory of Open Access Journals (Sweden)

Kamil Yavuz Kapusuz

2014-06-01

Full Text Available In this study, a method for determining scattering center (or center of scattering points of a multipath is proposed, provided that the direction of arrival of the multipath is known by the receiver. The method is based on classical electromagnetic wave principles in order to determine scattering center over irregular terrain. Geometrical optics (GO along with Fresnel zone concept is employed, as the receiver, the transmitter positions and irregular terrain data are assumed to be provided. The proposed method could be used at UHF bands, especially, operations of radars and electronic warfare applications.

Assessment of multipath and shadowing effects on UHF band in ...

African Journals Online (AJOL)

In this work, the multi-path and shadowing effects on signal impairment were investigated through the use of empirical and semi-empirical path loss models analysis in built-up environments. Electromagnetic field strength measurements were conducted using four television transmitters at UHF bands along four major routes ...

Channel Equalization Using Multilayer Perceptron Networks

Directory of Open Access Journals (Sweden)

Saba Baloch

2012-07-01

Full Text Available In most digital communication systems, bandwidth limited channel along with multipath propagation causes ISI (Inter Symbol Interference to occur. This phenomenon causes distortion of the given transmitted symbol due to other transmitted symbols. With the help of equalization ISI can be reduced. This paper presents a solution to the ISI problem by performing blind equalization using ANN (Artificial Neural Networks. The simulated network is a multilayer feedforward Perceptron ANN, which has been trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network. This paper presents a very effective method for blind channel equalization, being more efficient than the pre-existing algorithms. The obtained results show a visible reduction in the noise content.

SparseM: A Sparse Matrix Package for R *

Directory of Open Access Journals (Sweden)

Roger Koenker

2003-02-01

Full Text Available SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices. Significant performance improvements in memory utilization and computational speed are possible for applications involving large sparse matrices.

Multidimensional Rank Reduction Estimator for Parametric MIMO Channel Models

Directory of Open Access Journals (Sweden)

Marius Pesavento

2004-08-01

Full Text Available A novel algebraic method for the simultaneous estimation of MIMO channel parameters from channel sounder measurements is developed. We consider a parametric multipath propagation model with P discrete paths where each path is characterized by its complex path gain, its directions of arrival and departure, time delay, and Doppler shift. This problem is treated as a special case of the multidimensional harmonic retrieval problem. While the well-known ESPRIT-type algorithms exploit shift-invariance between specific partitions of the signal matrix, the rank reduction estimator (RARE algorithm exploits their internal Vandermonde structure. A multidimensional extension of the RARE algorithm is developed, analyzed, and applied to measurement data recorded with the RUSK vector channel sounder in the 2 GHz band.

Robust and Secure Watermarking Using Sparse Information of Watermark for Biometric Data Protection

Directory of Open Access Journals (Sweden)

Rohit M Thanki

2016-08-01

Full Text Available Biometric based human authentication system is used for security purpose in many organizations in the present world. This biometric authentication system has several vulnerable points. Two of vulnerable points are protection of biometric templates at system database and protection of biometric templates at communication channel between two modules of biometric authentication systems. In this paper proposed a robust watermarking scheme using the sparse information of watermark biometric to secure vulnerable point like protection of biometric templates at the communication channel of biometric authentication systems. A compressive sensing theory procedure is used for generation of sparse information on watermark biometric data using detail wavelet coefficients. Then sparse information of watermark biometric data is embedded into DCT coefficients of host biometric data. This proposed scheme is robust to common signal processing and geometric attacks like JPEG compression, adding noise, filtering, and cropping, histogram equalization. This proposed scheme has more advantages and high quality measures compared to existing schemes in the literature.

iSAP: Interactive Sparse Astronomical Data Analysis Packages

Science.gov (United States)

Fourt, O.; Starck, J.-L.; Sureau, F.; Bobin, J.; Moudden, Y.; Abrial, P.; Schmitt, J.

2013-03-01

iSAP consists of three programs, written in IDL, which together are useful for spherical data analysis. MR/S (MultiResolution on the Sphere) contains routines for wavelet, ridgelet and curvelet transform on the sphere, and applications such denoising on the sphere using wavelets and/or curvelets, Gaussianity tests and Independent Component Analysis on the Sphere. MR/S has been designed for the PLANCK project, but can be used for many other applications. SparsePol (Polarized Spherical Wavelets and Curvelets) has routines for polarized wavelet, polarized ridgelet and polarized curvelet transform on the sphere, and applications such denoising on the sphere using wavelets and/or curvelets, Gaussianity tests and blind source separation on the Sphere. SparsePol has been designed for the PLANCK project. MS-VSTS (Multi-Scale Variance Stabilizing Transform on the Sphere), designed initially for the FERMI project, is useful for spherical mono-channel and multi-channel data analysis when the data are contaminated by a Poisson noise. It contains routines for wavelet/curvelet denoising, wavelet deconvolution, multichannel wavelet denoising and deconvolution.

WEAMR — A Weighted Energy Aware Multipath Reliable Routing Mechanism for Hotline-Based WSNs

Directory of Open Access Journals (Sweden)

Ki-Hyung Kim

2013-05-01

Full Text Available Reliable source to sink communication is the most important factor for an efficient routing protocol especially in domains of military, healthcare and disaster recovery applications. We present weighted energy aware multipath reliable routing (WEAMR, a novel energy aware multipath routing protocol which utilizes hotline-assisted routing to meet such requirements for mission critical applications. The protocol reduces the number of average hops from source to destination and provides unmatched reliability as compared to well known reactive ad hoc protocols i.e., AODV and AOMDV. Our protocol makes efficient use of network paths based on weighted cost calculation and intelligently selects the best possible paths for data transmissions. The path cost calculation considers end to end number of hops, latency and minimum energy node value in the path. In case of path failure path recalculation is done efficiently with minimum latency and control packets overhead. Our evaluation shows that our proposal provides better end-to-end delivery with less routing overhead and higher packet delivery success ratio compared to AODV and AOMDV. The use of multipath also increases overall life time of WSN network using optimum energy available paths between sender and receiver in WDNs.

Proposal for automated transformations on single-photon multipath qudits

Science.gov (United States)

Baldijão, R. D.; Borges, G. F.; Marques, B.; Solís-Prosser, M. A.; Neves, L.; Pádua, S.

2017-09-01

We propose a method for implementing automated state transformations on single-photon multipath qudits encoded in a one-dimensional transverse spatial domain. It relies on transferring the encoding from this domain to the orthogonal one by applying a spatial phase modulation with diffraction gratings, merging all the initial propagation paths by using a stable interferometric network, and filtering out the unwanted diffraction orders. The automation feature is attained by utilizing a programmable phase-only spatial light modulator (SLM) where properly designed diffraction gratings displayed on its screen will implement the desired transformations, including, among others, projections, permutations, and random operations. We discuss the losses in the process which is, in general, inherently nonunitary. Some examples of transformations are presented and, considering a realistic scenario, we analyze how they will be affected by the pixelated structure of the SLM screen. The method proposed here enables one to implement much more general transformations on multipath qudits than is possible with a SLM alone operating in the diagonal basis of which-path states. Therefore, it will extend the range of applicability for this encoding in high-dimensional quantum information and computing protocols as well as fundamental studies in quantum theory.

A Novel Probability Model for Suppressing Multipath Ghosts in GPR and TWI Imaging: A Numerical Study

Directory of Open Access Journals (Sweden)

Tan Yun-hua

2015-10-01

Full Text Available A novel concept for suppressing the problem of multipath ghosts in Ground Penetrating Radar (GPR and Through-Wall Imaging (TWI is presented. Ghosts (i.e., false targets mainly arise from the use of the Born or single-scattering approximations that lead to linearized imaging algorithms; however, these approximations neglect the effect of multiple scattering (or multipath between the electromagnetic wavefield and the object under investigation. In contrast to existing methods of suppressing multipath ghosts, the proposed method models for the first time the reflectivity of the probed objects as a probability function up to a normalized factor and introduces the concept of random subaperture by randomly picking up measurement locations from the entire aperture. Thus, the final radar image is a joint probability distribution that corresponds to radar images derived from multiple random subapertures. Finally, numerical experiments are used to demonstrate the performance of the proposed methodology in GPR and TWI imaging.

H∞ Channel Estimation for DS-CDMA Systems: A Partial Difference Equation Approach

Directory of Open Access Journals (Sweden)

Wei Wang

2013-01-01

Full Text Available In the communications literature, a number of different algorithms have been proposed for channel estimation problems with the statistics of the channel noise and observation noise exactly known. In practical systems, however, the channel parameters are often estimated using training sequences which lead to the statistics of the channel noise difficult to obtain. Moreover, the received signals are corrupted not only by the ambient noises but also by multiple-access interferences, so the statistics of observation noises is also difficult to obtain. In this paper, we will investigate the H∞ channel estimation problem for direct-sequence code-division multiple-access (DS-CDMA communication systems with time-varying multipath fading channels. The channel estimator is designed by applying a partial difference equation approach together with the innovation analysis theory. This method can give a sufficient and necessary condition for the existence of an H∞ channel estimator.

Acoustic multipath arrivals in the horizontal plane due to approaching nonlinear internal waves.

Science.gov (United States)

Badiey, Mohsen; Katsnelson, Boris G; Lin, Ying-Tsong; Lynch, James F

2011-04-01

Simultaneous measurements of acoustic wave transmissions and a nonlinear internal wave packet approaching an along-shelf acoustic path during the Shallow Water 2006 experiment are reported. The incoming internal wave packet acts as a moving frontal layer reflecting (or refracting) sound in the horizontal plane. Received acoustic signals are filtered into acoustic normal mode arrivals. It is shown that a horizontal multipath interference is produced. This has previously been called a horizontal Lloyd's mirror. The interference between the direct path and the refracted path depends on the mode number and frequency of the acoustic signal. A mechanism for the multipath interference is shown. Preliminary modeling results of this dynamic interaction using vertical modes and horizontal parabolic equation models are in good agreement with the observed data.

MultiPaths Revisited - A novel approach using OpenFlow-enabled devices

CERN Document Server

Al-Shabibi, Ali; Martin, Brian

2011-06-11

This thesis presents novel approaches enhancing the performance of computer networks using multipaths. Our enhancements take the form of congestion-aware routing protocols. We present three protocols called MultiRoute, Step-Route, and finally PathRoute. Each of these protocols leverage both local and remote congestion statistics and build different representations (or views) of the network congestion by using an innovative representation of congestion for router-router links. These congestion statistics are then distributed via an aggregation protocol to other routers in the network. For many years, multipath routing protocols have only been used in simple situations, such as Link Aggregation and/or networks where paths of equal cost (and therefore equal delay) exist. But, paths of unequal costs are often discarded to the benefit of shortest path only routing because it is known that paths of unequal length present different delays and therefore cause out of order packets which cause catastrophic network per...

Quantified, Interactive Simulation of AMCW ToF Camera Including Multipath Effects.

Science.gov (United States)

Bulczak, David; Lambers, Martin; Kolb, Andreas

2017-12-22

In the last decade, Time-of-Flight (ToF) range cameras have gained increasing popularity in robotics, automotive industry, and home entertainment. Despite technological developments, ToF cameras still suffer from error sources such as multipath interference or motion artifacts. Thus, simulation of ToF cameras, including these artifacts, is important to improve camera and algorithm development. This paper presents a physically-based, interactive simulation technique for amplitude modulated continuous wave (AMCW) ToF cameras, which, among other error sources, includes single bounce indirect multipath interference based on an enhanced image-space approach. The simulation accounts for physical units down to the charge level accumulated in sensor pixels. Furthermore, we present the first quantified comparison for ToF camera simulators. We present bidirectional reference distribution function (BRDF) measurements for selected, purchasable materials in the near-infrared (NIR) range, craft real and synthetic scenes out of these materials and quantitatively compare the range sensor data.

Quantified, Interactive Simulation of AMCW ToF Camera Including Multipath Effects

Directory of Open Access Journals (Sweden)

David Bulczak

2017-12-01

Full Text Available In the last decade, Time-of-Flight (ToF range cameras have gained increasing popularity in robotics, automotive industry, and home entertainment. Despite technological developments, ToF cameras still suffer from error sources such as multipath interference or motion artifacts. Thus, simulation of ToF cameras, including these artifacts, is important to improve camera and algorithm development. This paper presents a physically-based, interactive simulation technique for amplitude modulated continuous wave (AMCW ToF cameras, which, among other error sources, includes single bounce indirect multipath interference based on an enhanced image-space approach. The simulation accounts for physical units down to the charge level accumulated in sensor pixels. Furthermore, we present the first quantified comparison for ToF camera simulators. We present bidirectional reference distribution function (BRDF measurements for selected, purchasable materials in the near-infrared (NIR range, craft real and synthetic scenes out of these materials and quantitatively compare the range sensor data.

A Study of an Iterative Channel Estimation Scheme of FS-FBMC System

Directory of Open Access Journals (Sweden)

YongJu Won

2017-01-01

Full Text Available A filter bank multicarrier on offset-quadrature amplitude modulation (FBMC/OQAM system is an alternative multicarrier modulation scheme that does not need cyclic prefix (CP even in the presence of a multipath fading channel by the properties of prototype filter. The FBMC/OQAM system can be implemented either by using the poly-phase network with fast fourier transform (PPN-FFT or by using the extended FFT on a frequency-spreading (FS domain. In this paper, we propose an iterative channel estimation scheme for each sub channel of a FBMC/OQAM system over a frequency-spreading domain. The proposed scheme first estimates the channel using the received pilot signal in the subchannel domain and interpolates the estimated channel to fine frequency-spreading domain. Then the channel compensated FS domain pilot is despread again to modify the channel state information (CSI estimation. Computer simulation shows that the proposed method outperforms the conventional FBMC/OQAM channel estimator in a frequency selective channel.

ITS Multi-path Communications Access Decision Scheme

Directory of Open Access Journals (Sweden)

Miroslav Svitek

2008-02-01

Full Text Available Intelligent Transport Systems (ITS require widely spread and guarantied quality communications services. Method of ITS decomposition to set of subsystems and quantification of communications subsystems parameters is introduced. Due to typical complexity of the IST solution and mobility as the typical system elements property idea of communications systems with multipath multivendor structures is adopted. Resolution of seamless switching within a set of available wireless access solutions is presented. CALM based system or specifically designed and configured L3/L2 switching can be relevant solution for multi-path access communication system. These systems meet requirements of the seamless secure communications functionality within even extensive cluster of moving objects. Competent decision processes based on precisely quantified system requirements and each performance indicator tolerance range must be implemented to keep service up and running with no influence of continuously changing conditions in time and served space. Method of different paths service quality evaluation and selection of the best possible active communications access path is introduced. Proposed approach is based on Kalman filtering, which separates reasonable part of noise and also allows prediction of the individual parameters near future behavior. Presented classification algorithm applied on filtered measured data combined with deterministic parameters is trained using training data, i.e. combination of parameters vectors line and relevant decisions. Quality of classification is dependent on the size and quality of the training sets. This method is studied within projects e-Ident, DOTEK and SRATVU which are elaborating results of project CAMNA.

A Novel Load Balancing Scheme for Multipath Routing Protocol in MANET

Directory of Open Access Journals (Sweden)

Kokilamani Mounagurusamy

2016-09-01

Full Text Available The recent advancements in information and communication technology create a great demand for multipath routing protocols. In MANET, nodes can be arbitrarily located and can move freely at any given time. The topology of MANET can change rapidly and unpredictably. Because wireless link capacities are usually limited, congestion is possible in MANETs. Hence, balancing the load in a MANET is important since nodes with high load will deplete their batteries quickly, thereby increasing the probability of disconnecting or partitioning the network. To overcome these, the multipath protocol should be aware of load at route discovery phase. The main objective of the proposed article is to balance the load on a node and to extend the lifetime of the node due to the congestion, energy depletion and link failures. This article describes a novel load and congestion aware scheme called Path Efficient Ad-hoc On-demand Multipath Distance Vector (PE-AOMDV protocol to increase the performance of routing process in MANET in terms of congestion, end-to-end delay and load balancing. A new threshold value and a counter variable are introduced to limit the number of communication paths passing over a node in route discovery phase. For every new request the counter variable is incremented by one and the threshold value is compared to see whether the maximum number of connections has been reached or not. The proposed method is network simulator ns-2 and it is found that there is a significant improvement in the proposed scheme. It reduces the energy consumption, average end-to-end delay and normalized routing overhead. Also the proposed scheme increases packet delivery ratio, throughput and minimizes routing overheads.

Rate Control for Network-Coded Multipath Relaying with Time-Varying Connectivity

Science.gov (United States)

2010-12-10

Armen Babikyan, Nathaniel M. Jones, Thomas H. Shake, and Andrew P. Worthen MIT Lincoln Laboratory 244 Wood Street Lexington, MA 02420 DDRE, 1777...delay U U U U SAR 11 Zach Sweet 781-981-5997 1 Rate Control for Network-Coded Multipath Relaying with Time-Varying Connectivity Brooke Shrader, Armen

An Optimization Algorithm for Multipath Parallel Allocation for Service Resource in the Simulation Task Workflow

Directory of Open Access Journals (Sweden)

Zhiteng Wang

2014-01-01

Full Text Available Service oriented modeling and simulation are hot issues in the field of modeling and simulation, and there is need to call service resources when simulation task workflow is running. How to optimize the service resource allocation to ensure that the task is complete effectively is an important issue in this area. In military modeling and simulation field, it is important to improve the probability of success and timeliness in simulation task workflow. Therefore, this paper proposes an optimization algorithm for multipath service resource parallel allocation, in which multipath service resource parallel allocation model is built and multiple chains coding scheme quantum optimization algorithm is used for optimization and solution. The multiple chains coding scheme quantum optimization algorithm is to extend parallel search space to improve search efficiency. Through the simulation experiment, this paper investigates the effect for the probability of success in simulation task workflow from different optimization algorithm, service allocation strategy, and path number, and the simulation result shows that the optimization algorithm for multipath service resource parallel allocation is an effective method to improve the probability of success and timeliness in simulation task workflow.

A Multipath Routing Protocol Based on Bloom Filter for Multihop Wireless Networks

Directory of Open Access Journals (Sweden)

Junwei Jin

2016-01-01

Full Text Available On-demand multipath routing in a wireless ad hoc network is effective in achieving load balancing over the network and in improving the degree of resilience to mobility. In this paper, the salvage capable opportunistic node-disjoint multipath routing (SNMR protocol is proposed, which forms multiple routes for data transmission and supports packet salvaging with minimum overhead. The proposed mechanism constructs a primary path and a node-disjoint backup path together with alternative paths for the intermediate nodes in the primary path. It can be achieved by considering the reverse route back to the source stored in the route cache and the primary path information compressed by a Bloom filter. Our protocol presents higher capability in packet salvaging and lower overhead in forming multiple routes. Simulation results show that SNMR outperforms the compared protocols in terms of packet delivery ratio, normalized routing load, and throughput.

SAR antenna design for ambiguity and multipath suppression

DEFF Research Database (Denmark)

Christensen, Erik Lintz; Dich, Mikael

1993-01-01

A high resolution airborne synthetic aperture radar (SAR) has been developed at the Electromagnetics Institute (EMI) for remote sensing applications. The paper considers the radiation of antennas for a SAR system from a systems perspective. The basic specifications of an idealised antenna...... are obtained from the required swath and the azimuth footprint needed for the SAR processing. The radiation from a real antenna causes unwanted signal returns that lead to intensity variations (multipath) and ghost echoes (ambiguity). Additional specifications are deduced by considering these signals...

An adaptive weighting algorithm for accurate radio tomographic image in the environment with multipath and WiFi interference

OpenAIRE

Wang, M.; Wang, Zhonglei; Bu, X.; Ding, E.

2017-01-01

Radio frequency device-free localization based on wireless sensor network has proved its feasibility in buildings. With this technique, a target can be located relying on the changes of received signal strengths caused by the moving object. However, the accuracy of many such systems deteriorates seriously in the environment with WiFi and the multipath interference. State-of-the-art methods do not efficiently solve the WiFi and multipath interference problems at the same time. In this article,...

Multipath diffuse routing over heterogeneous mesh networks of web devices and sensors

NARCIS (Netherlands)

Vitale, G.; Stassen, M.L.A.; Colak, S.B.; Pronk, V.; Macq, B.; Quisquater, J.-.J.

2002-01-01

In this paper we present a new data flow algorithm based on physical diffusion for dense device networks needed for future ubiquitous communications. This diffuse data routing concept is based on multi-path signal propagation aided with adaptive beam-forming methods. Adaptation for beam-forming at

Low Complexity Sparse Bayesian Learning for Channel Estimation Using Generalized Mean Field

DEFF Research Database (Denmark)

Pedersen, Niels Lovmand; Manchón, Carles Navarro; Fleury, Bernard Henri

2014-01-01

We derive low complexity versions of a wide range of algorithms for sparse Bayesian learning (SBL) in underdetermined linear systems. The proposed algorithms are obtained by applying the generalized mean field (GMF) inference framework to a generic SBL probabilistic model. In the GMF framework, we...

Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique

Directory of Open Access Journals (Sweden)

Rakesh Kumar Sahu

2018-04-01

Full Text Available In Mobile Ad-hoc networks (MANET, power conservation and utilization is an acute problem and has received significant attention from academics and industry in recent years. Nodes in MANET function on battery power, which is a rare and limited energy resource. Hence, its conservation and utilization should be done judiciously for the effective functioning of the network. In this paper, a novel protocol namely Energy Reduction Multipath Routing Protocol for MANET using Recoil Technique (AOMDV-ER is proposed, which conserves the energy along with optimal network lifetime, routing overhead, packet delivery ratio and throughput. It performs better than any other AODV based algorithms, as in AOMDV-ER the nodes transmit packets to their destination smartly by using a varying recoil off time technique based on their geographical location. This concept reduces the number of transmissions, which results in the improvement of network lifetime. In addition, the local level route maintenance reduces the additional routing overhead. Lastly, the prediction based link lifetime of each node is estimated which helps in reducing the packet loss in the network. This protocol has three subparts: an optimal route discovery algorithm amalgamation with the residual energy and distance mechanism; a coordinated recoiled nodes algorithm which eliminates the number of transmissions in order to reduces the data redundancy, traffic redundant, routing overhead, end to end delay and enhance the network lifetime; and a last link reckoning and route maintenance algorithm to improve the packet delivery ratio and link stability in the network. The experimental results show that the AOMDV-ER protocol save at least 16% energy consumption, 12% reduction in routing overhead, significant achievement in network lifetime and packet delivery ratio than Ad hoc on demand multipath distance vector routing protocol (AOMDV, Ad hoc on demand multipath distance vector routing protocol life

Efficient channel estimation in massive MIMO systems - a distributed approach

KAUST Repository

Al-Naffouri, Tareq Y.

2016-01-01

We present two efficient algorithms for distributed estimation of channels in massive MIMO systems. The two cases of 1) generic, and 2) sparse channels is considered. The algorithms estimate the impulse response for each channel observed

3D Imaging of Dead Sea Area Using Weighted Multipath Summation: A Case Study

Directory of Open Access Journals (Sweden)

Shemer Keydar

2013-01-01

Full Text Available The formation of sinkholes along the Dead Sea is caused by the rapid decline of the Dead Sea level, as a possible result of human extensive activity. According to one of the geological models, the sinkholes in several sites are clustered along a narrow coastal strip developing along lineaments representing faults in NNW direction. In order to understand the relationship between a developing sinkhole and its tectonic environment, a high-resolution (HR three-dimensional (3D seismic reflection survey was carried out at the western shoreline of the Dead Sea. A recently developed 3D imaging approach was applied to this 3D dataset. Imaging of subsurface is performed by a spatial summation of seismic waves along time surfaces using recently proposed multipath summation with proper weights. The multipath summation is performed by stacking the target waves along all possible time surfaces having a common apex at the given point. This approach does not require any explicit information on parameters since the involved multipath summation is performed for all possible parameters values within a wide specified range. The results from processed 3D time volume show subhorizontal coherent reflectors at approximate depth of 50–80 m which incline on closer location to the exposed sinkhole and suggest a possible linkage between revealed fault and the sinkholes.

Reduced-Rank Chip-Level MMSE Equalization for the 3G CDMA Forward Link with Code-Multiplexed Pilot

Directory of Open Access Journals (Sweden)

Goldstein J Scott

2002-01-01

Full Text Available This paper deals with synchronous direct-sequence code-division multiple access (CDMA transmission using orthogonal channel codes in frequency selective multipath, motivated by the forward link in 3G CDMA systems. The chip-level minimum mean square error (MMSE estimate of the (multiuser synchronous sum signal transmitted by the base, followed by a correlate and sum, has been shown to perform very well in saturated systems compared to a Rake receiver. In this paper, we present the reduced-rank, chip-level MMSE estimation based on the multistage nested Wiener filter (MSNWF. We show that, for the case of a known channel, only a small number of stages of the MSNWF is needed to achieve near full-rank MSE performance over a practical single-to-noise ratio (SNR range. This holds true even for an edge-of-cell scenario, where two base stations are contributing near equal-power signals, as well as for the single base station case. We then utilize the code-multiplexed pilot channel to train the MSNWF coefficients and show that adaptive MSNWF operating in a very low rank subspace performs slightly better than full-rank recursive least square (RLS and significantly better than least mean square (LMS. An important advantage of the MSNWF is that it can be implemented in a lattice structure, which involves significantly less computation than RLS. We also present structured MMSE equalizers that exploit the estimate of the multipath arrival times and the underlying channel structure to project the data vector onto a much lower dimensional subspace. Specifically, due to the sparseness of high-speed CDMA multipath channels, the channel vector lies in the subspace spanned by a small number of columns of the pulse shaping filter convolution matrix. We demonstrate that the performance of these structured low-rank equalizers is much superior to unstructured equalizers in terms of convergence speed and error rates.

A tone-aided dual vestigial sideband system for digital communications on fading channels

Science.gov (United States)

Hladik, Stephen M.; Saulnier, Gary J.; Rafferty, William

1989-01-01

A spectrally efficient tone-aided dual vestigial sideband (TA/DVSB) system for digital data communications on fading channels is presented and described analytically. This PSK (phase-shift-keying) system incorporates a feed-forward, tone-aided demodulation technique to compensate for Doppler frequency shift and channel- induced, multipath fading. In contrast to other tone-in-band-type systems, receiver synchronization is derived from the complete data VSBs. Simulation results for the Rician fading channel are presented. These results demonstrate the receiver's ability to mitigate performance degradation due to fading and to obtain proper data carrier synchronization, suggesting that the proposed TA/DVSB system has promise for this application. Simulated BER (bit-error rate) data indicate that the TA/DVSB system effectively alleviates the channel distortions of the land mobile satellite application.

Orthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological Structures With the Ensemble Kalman Filter

KAUST Repository

Sana, Furrukh; Katterbauer, Klemens; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim

2016-01-01

Estimating the locations and the structures of subsurface channels holds significant importance for forecasting the subsurface flow and reservoir productivity. These channels exhibit high permeability and are easily contrasted from the low-permeability rock formations in their surroundings. This enables formulating the flow channels estimation problem as a sparse field recovery problem. The ensemble Kalman filter (EnKF) is a widely used technique for the estimation and calibration of subsurface reservoir model parameters, such as permeability. However, the conventional EnKF framework does not provide an efficient mechanism to incorporate prior information on the wide varieties of subsurface geological structures, and often fails to recover and preserve flow channel structures. Recent works in the area of compressed sensing (CS) have shown that estimating in a sparse domain, using algorithms such as the orthogonal matching pursuit (OMP), may significantly improve the estimation quality when dealing with such problems. We propose two new, and computationally efficient, algorithms combining OMP with the EnKF to improve the estimation and recovery of the subsurface geological channels. Numerical experiments suggest that the proposed algorithms provide efficient mechanisms to incorporate and preserve structural information in the EnKF and result in significant improvements in recovering flow channel structures.

Orthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological Structures With the Ensemble Kalman Filter

KAUST Repository

Sana, Furrukh

2016-02-23

Estimating the locations and the structures of subsurface channels holds significant importance for forecasting the subsurface flow and reservoir productivity. These channels exhibit high permeability and are easily contrasted from the low-permeability rock formations in their surroundings. This enables formulating the flow channels estimation problem as a sparse field recovery problem. The ensemble Kalman filter (EnKF) is a widely used technique for the estimation and calibration of subsurface reservoir model parameters, such as permeability. However, the conventional EnKF framework does not provide an efficient mechanism to incorporate prior information on the wide varieties of subsurface geological structures, and often fails to recover and preserve flow channel structures. Recent works in the area of compressed sensing (CS) have shown that estimating in a sparse domain, using algorithms such as the orthogonal matching pursuit (OMP), may significantly improve the estimation quality when dealing with such problems. We propose two new, and computationally efficient, algorithms combining OMP with the EnKF to improve the estimation and recovery of the subsurface geological channels. Numerical experiments suggest that the proposed algorithms provide efficient mechanisms to incorporate and preserve structural information in the EnKF and result in significant improvements in recovering flow channel structures.

Opportunistic relaying in multipath and slow fading channel: Relay selection and optimal relay selection period

KAUST Repository

Sungjoon Park,

2011-11-01

In this paper we present opportunistic relay communication strategies of decode and forward relaying. The channel that we are considering includes pathloss, shadowing, and fast fading effects. We find a simple outage probability formula for opportunistic relaying in the channel, and validate the results by comparing it with the exact outage probability. Also, we suggest a new relay selection algorithm that incorporates shadowing. We consider a protocol of broadcasting the channel gain of the previously selected relay. This saves resources in slow fading channel by reducing collisions in relay selection. We further investigate the optimal relay selection period to maximize the throughput while avoiding selection overhead. © 2011 IEEE.

Point-source reconstruction with a sparse light-sensor array for optical TPC readout

International Nuclear Information System (INIS)

Rutter, G; Richards, M; Bennieston, A J; Ramachers, Y A

2011-01-01

A reconstruction technique for sparse array optical signal readout is introduced and applied to the generic challenge of large-area readout of a large number of point light sources. This challenge finds a prominent example in future, large volume neutrino detector studies based on liquid argon. It is concluded that the sparse array option may be ruled out for reasons of required number of channels when compared to a benchmark derived from charge readout on wire-planes. Smaller-scale detectors, however, could benefit from this technology.

Broadband Microwave Wireless Power Transfer for Weak-Signal and Multipath Environments

Science.gov (United States)

Barton, Richard J.

2014-01-01

In this paper, we study the potential benefits of using relatively broadband wireless power transmission WPT strategies in both weak-signal and multipath environments where traditional narrowband strategies can be very inefficient. The paper is primarily a theoretical and analytical treatment of the problem that attempts to derive results that are widely applicable to many different WPT applications, including space solar power SSP.

Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.

Science.gov (United States)

She, Huajun; Chen, Rong-Rong; Liang, Dong; DiBella, Edward V R; Ying, Leslie

2014-02-01

To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Copyright © 2013 Wiley Periodicals, Inc.

Semi-supervised sparse coding

KAUST Repository

Wang, Jim Jing-Yan; Gao, Xin

2014-01-01

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

Semi-supervised sparse coding

KAUST Repository

Wang, Jim Jing-Yan

2014-07-06

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

Analysis of the Bias on the Beidou GEO Multipath Combinations

Science.gov (United States)

Ning, Yafei; Yuan, Yunbin; Chai, Yanju; Huang, Yong

2016-01-01

The Beidou navigation satellite system is a very important sensor for positioning in the Asia-Pacific region. The Beidou inclined geosynchronous orbit (IGSO) and medium Earth orbit (MEO) satellites have been analysed in some studies previously conducted by other researchers; this paper seeks to gain more insight regarding the geostationary earth orbit (GEO) satellites. Employing correlation analysis, Fourier transformation and wavelet decomposition, we validate whether there is a systematic bias in their multipath combinations. These biases can be observed clearly in satellites C01, C02 and C04 and have a great correlation with time series instead of elevation, being significantly different from those of the Beidou IGSO and MEO satellites. We propose a correction model to mitigate this bias based on its daily periodicity characteristic. After the model has been applied, the performance of the positioning estimations of the eight stations distributed in the Asia-Pacific region is evaluated and compared. The results show that residuals of multipath series behaves random noise; for the single point positioning (SPP) and precise point positioning (PPP) approaches, the positioning accuracy in the upward direction can be improved by 8 cm and 6 mm, respectively, and by 2 cm and 4 mm, respectively, for the horizontal component. PMID:27509503

Analysis of the Bias on the Beidou GEO Multipath Combinations

Directory of Open Access Journals (Sweden)

Yafei Ning

2016-08-01

Full Text Available The Beidou navigation satellite system is a very important sensor for positioning in the Asia-Pacific region. The Beidou inclined geosynchronous orbit (IGSO and medium Earth orbit (MEO satellites have been analysed in some studies previously conducted by other researchers; this paper seeks to gain more insight regarding the geostationary earth orbit (GEO satellites. Employing correlation analysis, Fourier transformation and wavelet decomposition, we validate whether there is a systematic bias in their multipath combinations. These biases can be observed clearly in satellites C01, C02 and C04 and have a great correlation with time series instead of elevation, being significantly different from those of the Beidou IGSO and MEO satellites. We propose a correction model to mitigate this bias based on its daily periodicity characteristic. After the model has been applied, the performance of the positioning estimations of the eight stations distributed in the Asia-Pacific region is evaluated and compared. The results show that residuals of multipath series behaves random noise; for the single point positioning (SPP and precise point positioning (PPP approaches, the positioning accuracy in the upward direction can be improved by 8 cm and 6 mm, respectively, and by 2 cm and 4 mm, respectively, for the horizontal component.

Performance analysis of power-efficient adaptive interference cancelation in fading channels

KAUST Repository

Radaydeh, Redha Mahmoud Mesleh; Alouini, Mohamed-Slim

2010-01-01

This paper analyzes the performance of a -steering scheme for highly correlated receive antennas in the presence of statistically unordered co-channel interferers over multipath fading channels. An adaptive activation of receive antennas according to the interfering signals fading conditions is considered in the analysis. Analytical expressions for various system performance measures, including the outage probability, average error probability of different signaling schemes, and raw moments of the combined signal-to-interference-plus-noise ratio (SINR) are obtained in exact forms. Numerical and simulation results for the performance-complexity tradeoff of this scheme is presented and then compared with that of full-size arbitrary interference cancelation and no cancelation scenarios. ©2010 IEEE.

Performance analysis of power-efficient adaptive interference cancelation in fading channels

KAUST Repository

Radaydeh, Redha Mahmoud Mesleh

2010-12-01

This paper analyzes the performance of a -steering scheme for highly correlated receive antennas in the presence of statistically unordered co-channel interferers over multipath fading channels. An adaptive activation of receive antennas according to the interfering signals fading conditions is considered in the analysis. Analytical expressions for various system performance measures, including the outage probability, average error probability of different signaling schemes, and raw moments of the combined signal-to-interference-plus-noise ratio (SINR) are obtained in exact forms. Numerical and simulation results for the performance-complexity tradeoff of this scheme is presented and then compared with that of full-size arbitrary interference cancelation and no cancelation scenarios. ©2010 IEEE.

Performance of Generalized Multicarrier DS-CDMA over Nakagami-$m$ Fading Channels

OpenAIRE

Yang, L-L.; Hanzo, L.

2002-01-01

A class of generalized multicarrier direct sequence code-division multiple-access (MC DS-CDMA) schemes is defined and its performance is considered over multipath Nakagamifading channels. The spacing between two adjacent subcarriers of the generalized MC DS-CDMA is a variable, allowing us to gain insight into the effects of the spacing on the bit error rate (BER) performance of MC DS-CDMA systems. This generalized MC DS-CDMA scheme includes the subclasses of multitone DS-CDMA and orthogonal M...

In Defense of Sparse Tracking: Circulant Sparse Tracker

KAUST Repository

Zhang, Tianzhu; Bibi, Adel Aamer; Ghanem, Bernard

2016-01-01

Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.

In Defense of Sparse Tracking: Circulant Sparse Tracker

KAUST Repository

Zhang, Tianzhu

2016-12-13

Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.

Transmit selection for imperfect threshold-based receive MRC in Rayleigh fading channels

KAUST Repository

Radaydeh, Redha Mahmoud Mesleh

2010-01-01

The performance of multiple-antenna diversity systems in which the receiver combines signal replicas per thresholdbased maximal ratio combining (MRC) and the transmitter uses only a single antenna according to receive combined signal strength is studied. The impact of imperfect channel estimation is considered when the received signal replicas undergo independent and flat multipath fading. The analysis is applicable for arbitrary transmit antenna selection when the multiple-antenna channels experience identically distributed and non-identically distributed Rayleigh fading conditions. New closed-form expressions for the combined SNR statistics and some performance measures are presented. The system models adopted herein and the presented analytical results can be used to study the performance of different system architectures under various channel conditions when the implementation complexity is of interest. © 2009 IEEE.

Background Traffic-Based Retransmission Algorithm for Multimedia Streaming Transfer over Concurrent Multipaths

Directory of Open Access Journals (Sweden)

Yuanlong Cao

2012-01-01

Full Text Available The content-rich multimedia streaming will be the most attractive services in the next-generation networks. With function of distribute data across multipath end-to-end paths based on SCTP's multihoming feature, concurrent multipath transfer SCTP (CMT-SCTP has been regarded as the most promising technology for the efficient multimedia streaming transmission. However, the current researches on CMT-SCTP mainly focus on the algorithms related to the data delivery performance while they seldom consider the background traffic factors. Actually, background traffic of realistic network environments has an important impact on the performance of CMT-SCTP. In this paper, we firstly investigate the effect of background traffic on the performance of CMT-SCTP based on a close realistic simulation topology with reasonable background traffic in NS2, and then based on the localness nature of background flow, a further improved retransmission algorithm, named RTX_CSI, is proposed to reach more benefits in terms of average throughput and achieve high users' experience of quality for multimedia streaming services.

UMDR: Multi-Path Routing Protocol for Underwater Ad Hoc Networks with Directional Antenna

Science.gov (United States)

Yang, Jianmin; Liu, Songzuo; Liu, Qipei; Qiao, Gang

2018-01-01

This paper presents a new routing scheme for underwater ad hoc networks based on directional antennas. Ad hoc networks with directional antennas have become a hot research topic because of space reuse may increase networks capacity. At present, researchers have applied traditional self-organizing routing protocols (such as DSR, AODV) [1] [2] on this type of networks, and the routing scheme is based on the shortest path metric. However, such routing schemes often suffer from long transmission delays and frequent link fragmentation along the intermediate nodes of the selected route. This is caused by a unique feature of directional transmission, often called as “deafness”. In this paper, we take a different approach to explore the advantages of space reuse through multipath routing. This paper introduces the validity of the conventional routing scheme in underwater ad hoc networks with directional antennas, and presents a special design of multipath routing algorithm for directional transmission. The experimental results show a significant performance improvement in throughput and latency.

Performance of adaptive MS-GSC with co-channel interference

KAUST Repository

Daghfous, Mohamed A.

2011-06-01

Minimum selection generalized selection combining(MS-GSC) scheme has been proposed as a generalized power-saving variant of the conventional generalized selection combining(GSC) scheme. Previous analysis of the performance of MS-GSC has focused on interference-free environments. This paper aims to investigate the performance of adaptive signal-to-noise ratio (SNR)-based MS-GSC in the presence of co-channel interference over multipath fading channels. The adaptation thresholds are selected to enhance either the spectral efficiency or power efficiency of discrete-time rectangular signaling system. New closed-form expressions for the statistics of combined signal-to-interference-plus-noise ratio (SINR) are presented, which are then used to obtain analytical formulations for various performance measures. Numerical and simulation comparisons for the performance of the adaptation scheme are provided. © 2011 IEEE.

Evaluation of coherence interference in optical wireless communication through multiscattering channels.

Science.gov (United States)

Kedar, Debbie; Arnon, Shlomi

2006-05-10

Optical wireless communication has been the subject of much research in recent years because of the increasing interest in laser satellite-ground links and urban optical wireless communication. The major sources of performance degradation have been identified as the spatial, angular, and temporal spread of the propagating beam when the propagation channel is multiscattering, resulting in reduced power reception and intersignal interference, as well as turbulence-induced scintillations and noise due to receiver circuitry and background illumination. However, coherence effects due to multipath interference caused by a scattering propagation channel do not appear to have been treated in detail in the scientific literature. We attempt a theoretical analysis of coherence interference in optical wireless communication through scattering channels and try to quantify the resultant performance degradation for different media. We conclude that coherence interference is discernible in optical wireless communication through scattering channels and is highly dependent on the microscopic nature of the propagation medium.

High Order Tensor Formulation for Convolutional Sparse Coding

KAUST Repository

Bibi, Adel Aamer

2017-12-25

Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images independently. However, learning multidimensional dictionaries and sparse codes for the reconstruction of multi-dimensional data is very important, as it examines correlations among all the data jointly. This provides more capacity for the learned dictionaries to better reconstruct data. In this paper, we propose a generic and novel formulation for the CSC problem that can handle an arbitrary order tensor of data. Backed with experimental results, our proposed formulation can not only tackle applications that are not possible with standard CSC solvers, including colored video reconstruction (5D- tensors), but it also performs favorably in reconstruction with much fewer parameters as compared to naive extensions of standard CSC to multiple features/channels.

Multipath ultrasonic gas flow-meter based on multiple reference waves.

Science.gov (United States)

Zhou, Hongliang; Ji, Tao; Wang, Ruichen; Ge, Xiaocheng; Tang, Xiaoyu; Tang, Shizhen

2018-01-01

Several technologies can be used in ultrasonic gas flow-meters, such as transit-time, Doppler, cross-correlation and etc. In applications, the approach based on measuring transit-time has demonstrated its advantages and become more popular. Among those techniques which can be applied to determine time-of-flight (TOF) of ultrasonic waves, including threshold detection, cross correlation algorithm and other digital signal processing algorithms, cross correlation algorithm has more advantages when the received ultrasonic signal is severely disturbed by the noise. However, the reference wave for cross correlation computation has great influence on the precise measurement of TOF. In the applications of the multipath flow-meters, selection of the reference wave becomes even more complicated. Based on the analysis of the impact factors that will introduce noise and waveform distortion of ultrasonic waves, an averaging method is proposed to determine the reference wave in this paper. In the multipath ultrasonic gas flow-meter, the analysis of each path of ultrasound needs its own reference wave. In case study, a six-path ultrasonic gas flow-meter has been designed and tested with air flow through the pipeline. The results demonstrate that the flow rate accuracy and the repeatability of the TOF are significantly improved by using averaging reference wave, compared with that using random reference wave. Copyright © 2017 Elsevier B.V. All rights reserved.

Design and Performance Analysis of MISO-ORM-DCSK System over Rayleigh Fading Channels

Directory of Open Access Journals (Sweden)

Gang Zhang

2016-01-01

Full Text Available A novel chaotic communication system, named Orthogonality-based Reference Modulated-Differential Chaos Shift Keying (ORM-DCSK, is proposed to enhance the performance of RM-DCSK. By designing an orthogonal chaotic generator (OCG, the intrasignal interference components in RM-DCSK are eliminated. Also, the signal frame format is expanded so the average bit energy is reduced. As a result, the proposed system has less interference in decision variables. Furthermore, to investigate the bit error rate (BER performance over Rayleigh fading channels, the MISO-ORM-DCSK is studied. The BER expressions of the new system are derived and analyzed over AWGN channel and multipath Rayleigh fading channel. All simulation results not only show that the proposed system can obtain significant improvement but also verify the analysis in theory.

High frequency source localization in a shallow ocean sound channel using frequency difference matched field processing.

Science.gov (United States)

Worthmann, Brian M; Song, H C; Dowling, David R

2015-12-01

Matched field processing (MFP) is an established technique for source localization in known multipath acoustic environments. Unfortunately, in many situations, particularly those involving high frequency signals, imperfect knowledge of the actual propagation environment prevents accurate propagation modeling and source localization via MFP fails. For beamforming applications, this actual-to-model mismatch problem was mitigated through a frequency downshift, made possible by a nonlinear array-signal-processing technique called frequency difference beamforming [Abadi, Song, and Dowling (2012). J. Acoust. Soc. Am. 132, 3018-3029]. Here, this technique is extended to conventional (Bartlett) MFP using simulations and measurements from the 2011 Kauai Acoustic Communications MURI experiment (KAM11) to produce ambiguity surfaces at frequencies well below the signal bandwidth where the detrimental effects of mismatch are reduced. Both the simulation and experimental results suggest that frequency difference MFP can be more robust against environmental mismatch than conventional MFP. In particular, signals of frequency 11.2 kHz-32.8 kHz were broadcast 3 km through a 106-m-deep shallow ocean sound channel to a sparse 16-element vertical receiving array. Frequency difference MFP unambiguously localized the source in several experimental data sets with average peak-to-side-lobe ratio of 0.9 dB, average absolute-value range error of 170 m, and average absolute-value depth error of 10 m.

Practical and Simple Wireless Channel Models for Use in Multipolarized Antenna Systems

Directory of Open Access Journals (Sweden)

KwangHyun Jeon

2014-01-01

Full Text Available The next-generation wireless systems are expected to support data rates of more than 100 Mbps in outdoor environments. In order to support such large payloads, a polarized antenna may be employed as one of the candidate technologies. Recently, the third generation partnership standards bodies (3GPP/3GPP2 have defined a cross-polarized channel model in SCM-E for MIMO systems; however, this model is quite complex since it considers a great many channel-related parameters. Furthermore, the SCM-E channel model combines the channel coefficients of all the polarization links into one complex output, making it impossible to exploit the MIMO spatial multiplexing or diversity gains in the case of employing polarized antenna at transmitter and receiver side. In this paper, we present practical and simple 2D and 3D multipolarized multipath channel models, which take into account both the cross-polarization discrimination (XPD and the Rician factor. After verifying the proposed channel models, the BER and PER performances and throughput using the EGC and MRC combining techniques are evaluated in multipolarized antenna systems.

Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing

Directory of Open Access Journals (Sweden)

Majid Shakhsi Dastgahian

2016-11-01

Full Text Available Millimeter-wave communication (mmWC is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS and mobile sets (MS. Unlike the conventional MIMO systems, Millimeter-wave (mmW systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.

Evaluation of multiple-channel OFDM based airborne ultrasonic communications.

Science.gov (United States)

Jiang, Wentao; Wright, William M D

2016-09-01

Orthogonal frequency division multiplexing (OFDM) modulation has been extensively used in both wired and wireless communication systems. The use of OFDM technology allows very high spectral efficiency data transmission without using complex equalizers to correct the effect of a frequency-selective channel. This work investigated OFDM methods in an airborne ultrasonic communication system, using commercially available capacitive ultrasonic transducers operating at 50kHz to transmit information through the air. Conventional modulation schemes such as binary phase shift keying (BPSK) and quadrature amplitude modulation (QAM) were used to modulate sub-carrier signals, and the performances were evaluated in an indoor laboratory environment. Line-of-sight (LOS) transmission range up to 11m with no measurable errors was achieved using BPSK at a data rate of 45kb/s and a spectral efficiency of 1b/s/Hz. By implementing a higher order modulation scheme (16-QAM), the system data transfer rate was increased to 180kb/s with a spectral efficiency of 4b/s/Hz at attainable transmission distances up to 6m. Diffraction effects were incorporated into a model of the ultrasonic channel that also accounted for beam spread and attenuation in air. The simulations were a good match to the measured signals and non-LOS signals could be demodulated successfully. The effects of multipath interference were also studied in this work. By adding cyclic prefix (CP) to the OFDM symbols, the bit error rate (BER) performance was significantly improved in a multipath environment. Copyright © 2016 Elsevier B.V. All rights reserved.

A Multipath Technique for Cancelling Harmonics and Sidebands in a Wideband Power Upconverter

NARCIS (Netherlands)

Shrestha, R.; Mensink, E.; Klumperink, Eric A.M.; Wienk, Gerhardus J.M.; Nauta, Bram

2006-01-01

Switching mixers are power-efficient but produce unwanted harmonics and sidebands. A multipath technique to clean up the spectrum using digital circuits and mixers, but no filters, is applied to a 0.13µm CMOS power upconverter. The circuit delivers 8mW from dc to 2.4GHz with 11% drain efficiency,

Fault-Aware Resource Allocation for Heterogeneous Data Sources with Multipath Routing

Directory of Open Access Journals (Sweden)

Xiaomei Zhang

2017-01-01

Full Text Available With the rapid development of cloud computing and big data, diverse types of traffic generated from heterogeneous data sources are delivered throughout communication networks, which consist of various node kinds such as digital sensors and smart actuators, and different applications. Due to the shared medium, communication networks are vulnerable to misbehaving nodes, and it is a crucial aspect to maintain an acceptable level of service degradation. This paper studies the fault-aware resource allocation problem by exploiting multipath routing and dynamic rate assignment for heterogeneous sources. We estimate the impacts of faults and formulate the resource allocation as a lossy network flow optimization problem based on these estimates. The traditional flow optimization solutions focus on homogeneous traffic. In our work, we model the performance of heterogeneous applications as a relaxed utility function and develop an effective utility framework of rate control for heterogeneous sources with multipath routing in presence of misbehaving nodes. We design a distributed algorithm to decide the routing strategy and obtain the rate assignments on the available paths in a lossy utility fair manner. Extensive performance evaluations corroborate the significant performance of our algorithm in effective utility and utility fairness in the presence of misbehaving nodes.

Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

Directory of Open Access Journals (Sweden)

David Overbye

2005-06-01

Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

Interferometric interpolation of sparse marine data

KAUST Repository

Hanafy, Sherif M.

2013-10-11

We present the theory and numerical results for interferometrically interpolating 2D and 3D marine surface seismic profiles data. For the interpolation of seismic data we use the combination of a recorded Green\\'s function and a model-based Green\\'s function for a water-layer model. Synthetic (2D and 3D) and field (2D) results show that the seismic data with sparse receiver intervals can be accurately interpolated to smaller intervals using multiples in the data. An up- and downgoing separation of both recorded and model-based Green\\'s functions can help in minimizing artefacts in a virtual shot gather. If the up- and downgoing separation is not possible, noticeable artefacts will be generated in the virtual shot gather. As a partial remedy we iteratively use a non-stationary 1D multi-channel matching filter with the interpolated data. Results suggest that a sparse marine seismic survey can yield more information about reflectors if traces are interpolated by interferometry. Comparing our results to those of f-k interpolation shows that the synthetic example gives comparable results while the field example shows better interpolation quality for the interferometric method. © 2013 European Association of Geoscientists & Engineers.

Structural Sparse Tracking

KAUST Repository

Zhang, Tianzhu

2015-06-01

Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.

Optimum Combining for Rapidly Fading Channels in Ad Hoc Networks

Directory of Open Access Journals (Sweden)

Sonia Furman

2003-10-01

Full Text Available Research and technology in wireless communication systems such as radar and cellular networks have successfully implemented alternative design approaches that utilize antenna array techniques such as optimum combining, to mitigate the degradation effects of multipath in rapid fading channels. In ad hoc networks, these methods have not yet been exploited primarily due to the complexity inherent in the network's architecture. With the high demand for improved signal link quality, devices configured with omnidirectional antennas can no longer meet the growing need for link quality and spectrum efficiency. This study takes an empirical approach to determine an optimum combining antenna array based on 3 variants of interelement spacing. For rapid fading channels, the simulation results show that the performance in the network of devices retrofitted with our antenna arrays consistently exceeded those with an omnidirectional antenna. Further, with the optimum combiner, the performance increased by over 60% compared to that of an omnidirectional antenna in a rapid fading channel.

GPS/Galileo Multipath Detection and Mitigation Using Closed-Form Solutions

Directory of Open Access Journals (Sweden)

Khaled Rouabah

2009-01-01

Full Text Available We propose an efficient method for the detection of Line of Sight (LOS and Multipath (MP signals in global navigation satellite systems (GNSSs which is based on the use of virtual MP mitigation (VMM technique. By using the proposed method, the MP signals' delay and coefficient amplitudes can be efficiently estimated. According to the computer simulation results, it is obvious that our proposed method is a solution for obtaining high performance in the estimation and mitigation of MP signals and thus it results in a high accuracy in GNSS positioning.

Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing

Directory of Open Access Journals (Sweden)

Liantao Wu

2015-08-01

Full Text Available Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy transmission results using the CS-based reconstruction method at the receiving end. The impacts of packet lengths on transmission efficiency under different channel conditions have been discussed, and interleaving is incorporated to mitigate the impact of burst data loss. Extensive simulations and experiments have been conducted and compared to the traditional automatic repeat request (ARQ interpolation technique, and very favorable results have been observed in terms of both accuracy of the reconstructed signals and the transmission energy consumption. Furthermore, the packet length effect provides useful insights for using compressed sensing for efficient sparse signal transmission via lossy links.

Wind Noise Reduction using Non-negative Sparse Coding

DEFF Research Database (Denmark)

Schmidt, Mikkel N.; Larsen, Jan; Hsiao, Fu-Tien

2007-01-01

We introduce a new speaker independent method for reducing wind noise in single-channel recordings of noisy speech. The method is based on non-negative sparse coding and relies on a wind noise dictionary which is estimated from an isolated noise recording. We estimate the parameters of the model ...... and discuss their sensitivity. We then compare the algorithm with the classical spectral subtraction method and the Qualcomm-ICSI-OGI noise reduction method. We optimize the sound quality in terms of signal-to-noise ratio and provide results on a noisy speech recognition task....

CHANNEL ESTIMATION TECHNIQUE

DEFF Research Database (Denmark)

2015-01-01

A method includes determining a sequence of first coefficient estimates of a communication channel based on a sequence of pilots arranged according to a known pilot pattern and based on a receive signal, wherein the receive signal is based on the sequence of pilots transmitted over the communicat......A method includes determining a sequence of first coefficient estimates of a communication channel based on a sequence of pilots arranged according to a known pilot pattern and based on a receive signal, wherein the receive signal is based on the sequence of pilots transmitted over...... the communication channel. The method further includes determining a sequence of second coefficient estimates of the communication channel based on a decomposition of the first coefficient estimates in a dictionary matrix and a sparse vector of the second coefficient estimates, the dictionary matrix including...... filter characteristics of at least one known transceiver filter arranged in the communication channel....

A Perspective for Time-Varying Channel Compensation with Model-Based Adaptive Passive Time-Reversal

Directory of Open Access Journals (Sweden)

Lussac P. MAIA

2015-06-01

Full Text Available Underwater communications mainly rely on acoustic propagation which is strongly affected by frequency-dependent attenuation, shallow water multipath propagation and significant Doppler spread/shift induced by source-receiver-surface motion. Time-reversal based techniques offer a low complexity solution to decrease interferences caused by multipath, but a complete equalization cannot be reached (it saturates when maximize signal to noise ratio and these techniques in conventional form are quite sensible to channel variations along the transmission. Acoustic propagation modeling in high frequency regime can yield physical-based information that is potentially useful to channel compensation methods as the passive time-reversal (pTR, which is often employed in Digital Acoustic Underwater Communications (DAUC systems because of its low computational cost. Aiming to overcome the difficulties of pTR to solve time-variations in underwater channels, it is intended to insert physical knowledge from acoustic propagation modeling in the pTR filtering. Investigation is being done by the authors about the influence of channel physical parameters on propagation of coherent acoustic signals transmitted through shallow water waveguides and received in a vertical line array of sensors. Time-variant approach is used, as required to model high frequency acoustic propagation on realistic scenarios, and applied to a DAUC simulator containing an adaptive passive time-reversal receiver (ApTR. The understanding about the effects of changes in physical features of the channel over the propagation can lead to design ApTR filters which could help to improve the communications system performance. This work presents a short extension and review of the paper 12, which tested Doppler distortion induced by source-surface motion and ApTR compensation for a DAUC system on a simulated time-variant channel, in the scope of model-based equalization. Environmental focusing approach

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

Science.gov (United States)

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Performance of sparse graph codes on a four-dimensional CDMA System in AWGN and multipath fading

CSIR Research Space (South Africa)

Vlok, JD

2007-09-01

Full Text Available (bit) = 1× 10−5. Index Terms—Block Turbo codes (BTC), complex spread- ing sequences (CSS), channel modelling, log-likelihood ratio (LLR), low-density parity-check (LDPC) codes, multi-layered- modulation (MLM), multi-dimensional (MD), repeat...~ ~ ~ ~ 1,3 −1 1,2 −1 1,2 1,2 1,3 1,3 2,3 2,3 −1 Fig. 4. Three-dimensional block turbo decoder structure The output of SISO module m is a 3D cube ΛE,m;m = 1, 2, 3, containing the extrinsic log-likelihood ratio (LLR) of each data bit xk...

LBMR: Load-Balanced Multipath Routing for Wireless Data-Intensive Transmission in Real-Time Medical Monitoring.

Science.gov (United States)

Tseng, Chinyang Henry

2016-05-31

In wireless networks, low-power Zigbee is an excellent network solution for wireless medical monitoring systems. Medical monitoring generally involves transmission of a large amount of data and easily causes bottleneck problems. Although Zigbee's AODV mesh routing provides extensible multi-hop data transmission to extend network coverage, it originally does not, and needs to support some form of load balancing mechanism to avoid bottlenecks. To guarantee a more reliable multi-hop data transmission for life-critical medical applications, we have developed a multipath solution, called Load-Balanced Multipath Routing (LBMR) to replace Zigbee's routing mechanism. LBMR consists of three main parts: Layer Routing Construction (LRC), a Load Estimation Algorithm (LEA), and a Route Maintenance (RM) mechanism. LRC assigns nodes into different layers based on the node's distance to the medical data gateway. Nodes can have multiple next-hops delivering medical data toward the gateway. All neighboring layer-nodes exchange flow information containing current load, which is the used by the LEA to estimate future load of next-hops to the gateway. With LBMR, nodes can choose the neighbors with the least load as the next-hops and thus can achieve load balancing and avoid bottlenecks. Furthermore, RM can detect route failures in real-time and perform route redirection to ensure routing robustness. Since LRC and LEA prevent bottlenecks while RM ensures routing fault tolerance, LBMR provides a highly reliable routing service for medical monitoring. To evaluate these accomplishments, we compare LBMR with Zigbee's AODV and another multipath protocol, AOMDV. The simulation results demonstrate LBMR achieves better load balancing, less unreachable nodes, and better packet delivery ratio than either AODV or AOMDV.

BIFOCAL STEREO FOR MULTIPATH PERSON RE-IDENTIFICATION

Directory of Open Access Journals (Sweden)

G. Blott

2017-11-01

Full Text Available This work presents an approach for the task of person re-identification by exploiting bifocal stereo cameras. Present monocular person re-identification approaches show a decreasing working distance, when increasing the image resolution to obtain a higher reidentification performance. We propose a novel 3D multipath bifocal approach, containing a rectilinear lens with larger focal length for long range distances and a fish eye lens of a smaller focal length for the near range. The person re-identification performance is at least on par with 2D re-identification approaches but the working distance of the approach is increased and on average 10% more re-identification performance can be achieved in the overlapping field of view compared to a single camera. In addition, the 3D information is exploited from the overlapping field of view to solve potential 2D ambiguities.

Simulated performance of an acoustic modem using phase-modulated signals in a time-varying, shallow-water environment

DEFF Research Database (Denmark)

Bjerrum-Niese, Christian; Jensen, Leif Bjørnø

1996-01-01

and dynamic multipath channel. Multipath arrivals at the receiver cause phase distortion and fading of the signal envelope. Yet, for extreme ratios of range to depth, the delays of multipath arrivals decrease, and the channel impulse response coherently contributes energy to the signal at short delays......Underwater acoustic modems using coherent modulation, such as phase-shift keying, have proven to efficiently exploit the bandlimited underwater acoustical communication channel. However, the performance of an acoustic modem, given as maximum range and data and error rate, is limited in the complex...... relative to the first arrival, while longer delays give rise to intersymbol interference. Following this, the signal-to-multipath ratio (SMR) is introduced. It is claimed that the SMR determines the performance rather than the signal-to-noise ratio (SNR). Using a ray model including temporal variations...

Compressive MIMO Beamforming of Data Collected in a Refractive Environment

Science.gov (United States)

Wagner, Mark; Nannuru, Santosh; Gerstoft, Peter

2017-12-01

The phenomenon of ducting is caused by abnormal atmospheric refractivity patterns and is known to allow electromagnetic waves to propagate over the horizon with unusually low propagation loss. It is unknown what effect ducting has on multiple input multiple output (MIMO) channels, particularly its effect on multipath propagation in MIMO channels. A high-accuracy angle-of-arrival and angle-of-departure estimation technique for MIMO communications, which we will refer to as compressive MIMO beamforming, was tested on simulated data then applied to experimental data taken from an over the horizon MIMO test bed located in a known ducting hot spot in Southern California. The multipath channel was estimated from the receiver data recorded over a period of 18 days, and an analysis was performed on the recorded data. The goal is to observe the evolution of the MIMO multipath channel as atmospheric ducts form and dissipate to gain some understanding of the behavior of channels in a refractive environment. This work is motivated by the idea that some multipath characteristics of MIMO channels within atmospheric ducts could yield important information about the duct.

Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.

Science.gov (United States)

Yuan, Shasha; Zhou, Weidong; Wu, Qi; Zhang, Yanli

2016-05-01

Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.

Multiple Speech Source Separation Using Inter-Channel Correlation and Relaxed Sparsity

Directory of Open Access Journals (Sweden)

Maoshen Jia

2018-01-01

Full Text Available In this work, a multiple speech source separation method using inter-channel correlation and relaxed sparsity is proposed. A B-format microphone with four spatially located channels is adopted due to the size of the microphone array to preserve the spatial parameter integrity of the original signal. Specifically, we firstly measure the proportion of overlapped components among multiple sources and find that there exist many overlapped time-frequency (TF components with increasing source number. Then, considering the relaxed sparsity of speech sources, we propose a dynamic threshold-based separation approach of sparse components where the threshold is determined by the inter-channel correlation among the recording signals. After conducting a statistical analysis of the number of active sources at each TF instant, a form of relaxed sparsity called the half-K assumption is proposed so that the active source number in a certain TF bin does not exceed half the total number of simultaneously occurring sources. By applying the half-K assumption, the non-sparse components are recovered by regarding the extracted sparse components as a guide, combined with vector decomposition and matrix factorization. Eventually, the final TF coefficients of each source are recovered by the synthesis of sparse and non-sparse components. The proposed method has been evaluated using up to six simultaneous speech sources under both anechoic and reverberant conditions. Both objective and subjective evaluations validated that the perceptual quality of the separated speech by the proposed approach outperforms existing blind source separation (BSS approaches. Besides, it is robust to different speeches whilst confirming all the separated speeches with similar perceptual quality.

Learning Joint-Sparse Codes for Calibration-Free Parallel MR Imaging.

Science.gov (United States)

Wang, Shanshan; Tan, Sha; Gao, Yuan; Liu, Qiegen; Ying, Leslie; Xiao, Taohui; Liu, Yuanyuan; Liu, Xin; Zheng, Hairong; Liang, Dong

2018-01-01

The integration of compressed sensing and parallel imaging (CS-PI) has shown an increased popularity in recent years to accelerate magnetic resonance (MR) imaging. Among them, calibration-free techniques have presented encouraging performances due to its capability in robustly handling the sensitivity information. Unfortunately, existing calibration-free methods have only explored joint-sparsity with direct analysis transform projections. To further exploit joint-sparsity and improve reconstruction accuracy, this paper proposes to Learn joINt-sparse coDes for caliBration-free parallEl mR imaGing (LINDBERG) by modeling the parallel MR imaging problem as an - - minimization objective with an norm constraining data fidelity, Frobenius norm enforcing sparse representation error and the mixed norm triggering joint sparsity across multichannels. A corresponding algorithm has been developed to alternatively update the sparse representation, sensitivity encoded images and K-space data. Then, the final image is produced as the square root of sum of squares of all channel images. Experimental results on both physical phantom and in vivo data sets show that the proposed method is comparable and even superior to state-of-the-art CS-PI reconstruction approaches. Specifically, LINDBERG has presented strong capability in suppressing noise and artifacts while reconstructing MR images from highly undersampled multichannel measurements.

A Unified Approach to the Analysis of Multicarrier DS-CDMA over Nakagami-$M$ Fading Channels

OpenAIRE

Yang, L-L.; Hanzo, L.

2001-01-01

A class of unified multicarrier DS-CDMA (MC DS-CDMA) schemes is defined and its performance is considered over multipath Nakagami-$M$ fading channels. The spacing between two adjacent subcarriers of the unified MC DS-CDMA scheme is a variable, allowing us to gain insight into the effects of the spacing on the bit error rate (BER) performance of MC DS-CDMA systems. This unified MC DS-CDMA scheme includes the subclasses of multitone DS-CDMA and orthogonal MC DS-CDMA as special cases. The optimu...

Efficient channel estimation in massive MIMO systems - a distributed approach

KAUST Repository

Al-Naffouri, Tareq Y.

2016-01-21

We present two efficient algorithms for distributed estimation of channels in massive MIMO systems. The two cases of 1) generic, and 2) sparse channels is considered. The algorithms estimate the impulse response for each channel observed by the antennas at the receiver (base station) in a coordinated manner by sharing minimal information among neighboring antennas. Simulations demonstrate the superior performance of the proposed methods as compared to other methods.

Intercomparison of two BRDF models in the estimation of the directional emissivity in MIR channel from MSG1-SEVIRI data.

Science.gov (United States)

Jiang, Geng-Ming; Li, Zhao-Liang

2008-11-10

This work intercompared two Bi-directional Reflectance Distribution Function (BRDF) models, the modified Minnaert's model and the RossThick-LiSparse-R model, in the estimation of the directional emissivity in Middle Infra-Red (MIR) channel from the data acquired by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard the first Meteosat Second Generation (MSG1). The bi-directional reflectances in SEVIRI channel 4 (3.9 microm) were estimated from the combined MIR and Thermal Infra-Red (TIR) data and then were used to estimate the directional emissivity in this channel with aid of the BRDF models. The results show that: (1) Both models can relatively well describe the non-Lambertian reflective behavior of land surfaces in SEVIRI channel 4; (2) The RossThick-LiSparse-R model is better than the modified Minnaert's model in modeling the bi-directional reflectances, and the directional emissivities modeled by the modified Minnaert's model are always lower than the ones obtained by the RossThick-LiSparse-R model with averaged emissivity differences of approximately 0.01 and approximately 0.04 over the vegetated and bare areas, respectively. The use of the RossThick-LiSparse-R model in the estimation of the directional emissivity in MIR channel is recommended.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint

Directory of Open Access Journals (Sweden)

Zhi Gao

2018-05-01

Full Text Available Light detection and ranging (LiDAR sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs and unmanned aerial vehicles (UAVs to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.

Science.gov (United States)

Gao, Zhi; Lao, Mingjie; Sang, Yongsheng; Wen, Fei; Ramesh, Bharath; Zhai, Ruifang

2018-05-06

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

KAUST Repository

Wang, Jim Jing-Yan

2017-06-28

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

Evaluation of Diversity Antenna Designs Using Ray Tracing, Measured Radiation Patterns, and MIMO Channel Measurements

Directory of Open Access Journals (Sweden)

Pal Arindam

2007-01-01

Full Text Available This paper presents an evaluation of the MIMO performance of three candidate antenna array designs, each embedded within a PDA footprint, using indoor wideband channel measurements at 5.2 GHz alongside channel simulations. A channel model which employs the plane-wave approximation was used to combine the embedded antenna radiation patterns of the candidate devices obtained from far-field pattern measurements and multipath component parameters from an indoor ray-tracer. The 4-element candidate arrays were each constructed using a different type of antenna element, and despite the diverse element directivities, pattern characteristics, and polarization purities, all three devices were constructed to fully exploit diversity in polarization, space, and angle. Thus, low correlation and high information theoretic capacity was observed in each case. A good match between the model and the measurements is also demonstrated, especially for MIMO subsets of identically or orthogonally polarized linear slot antennas. The interdependencies between the channel XPD, directional spread and pathloss, and the respective impact on channel capacity are also discussed in this paper.

Evaluation of Diversity Antenna Designs Using Ray Tracing, Measured Radiation Patterns, and MIMO Channel Measurements

Directory of Open Access Journals (Sweden)

Arindam Pal

2007-01-01

Full Text Available This paper presents an evaluation of the MIMO performance of three candidate antenna array designs, each embedded within a PDA footprint, using indoor wideband channel measurements at 5.2 GHz alongside channel simulations. A channel model which employs the plane-wave approximation was used to combine the embedded antenna radiation patterns of the candidate devices obtained from far-field pattern measurements and multipath component parameters from an indoor ray-tracer. The 4-element candidate arrays were each constructed using a different type of antenna element, and despite the diverse element directivities, pattern characteristics, and polarization purities, all three devices were constructed to fully exploit diversity in polarization, space, and angle. Thus, low correlation and high information theoretic capacity was observed in each case. A good match between the model and the measurements is also demonstrated, especially for 2×2 MIMO subsets of identically or orthogonally polarized linear slot antennas. The interdependencies between the channel XPD, directional spread and pathloss, and the respective impact on channel capacity are also discussed in this paper.

Methylmercury determination using a hyphenated high performance liquid chromatography ultraviolet cold vapor multipath atomic absorption spectrometry system

Energy Technology Data Exchange (ETDEWEB)

Campos, Reinaldo C. [Department of Chemistry, Pontifical Catholic University of Rio de Janeiro, Rua Marques de S Vicente 225, 22453-900 Rio de Janeiro (Brazil)], E-mail: rccampos@puc-rio.br; Goncalves, Rodrigo A.; Brandao, Geisamanda P.; Azevedo, Marlo S. [Department of Chemistry, Pontifical Catholic University of Rio de Janeiro, Rua Marques de S Vicente 225, 22453-900 Rio de Janeiro (Brazil); Oliveira, Fabiana; Wasserman, Julio [Institut of Geosciences, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, s/n, 24.210-340, Niteroi, Rio de Janeiro (Brazil)

2009-06-15

The present work investigates the use of a multipath cell atomic absorption mercury detector for mercury speciation analysis in a hyphenated high performance liquid chromatography assembly. The multipath absorption cell multiplies the optical path while energy losses are compensated by a very intense primary source. Zeeman-effect background correction compensates for non-specific absorption. For the separation step, the mobile phase consisted in a 0.010% m/v mercaptoethanol solution in 5% methanol (pH = 5), a C{sub 18} column was used as stationary phase, and post column treatment was performed by UV irradiation (60 deg. C, 13 W). The eluate was then merged with 3 mol L{sup -1} HCl, reduction was performed by a NaBH{sub 4} solution, and the Hg vapor formed was separated at the gas-liquid separator and carried through a desiccant membrane to the detector. The detector was easily attached to the system, since an external gas flow to the gas-liquid separator was provided. A multivariate approach was used to optimize the procedure and peak area was used for measurement. Instrumental limits of detection of 0.05 {mu}g L{sup -1} were obtained for ionic (Hg{sup 2+}) and HgCH{sub 3}{sup +}, for an injection volume of 200 {mu}L. The multipath atomic absorption spectrometer proved to be a competitive mercury detector in hyphenated systems in relation to the most commonly used atomic fluorescence and inductively coupled plasma mass spectrometric detectors. Preliminary application studies were performed for the determination of methyl mercury in sedi0011men.

Methylmercury determination using a hyphenated high performance liquid chromatography ultraviolet cold vapor multipath atomic absorption spectrometry system

International Nuclear Information System (INIS)

Campos, Reinaldo C.; Goncalves, Rodrigo A.; Brandao, Geisamanda P.; Azevedo, Marlo S.; Oliveira, Fabiana; Wasserman, Julio

2009-01-01

The present work investigates the use of a multipath cell atomic absorption mercury detector for mercury speciation analysis in a hyphenated high performance liquid chromatography assembly. The multipath absorption cell multiplies the optical path while energy losses are compensated by a very intense primary source. Zeeman-effect background correction compensates for non-specific absorption. For the separation step, the mobile phase consisted in a 0.010% m/v mercaptoethanol solution in 5% methanol (pH = 5), a C 18 column was used as stationary phase, and post column treatment was performed by UV irradiation (60 deg. C, 13 W). The eluate was then merged with 3 mol L -1 HCl, reduction was performed by a NaBH 4 solution, and the Hg vapor formed was separated at the gas-liquid separator and carried through a desiccant membrane to the detector. The detector was easily attached to the system, since an external gas flow to the gas-liquid separator was provided. A multivariate approach was used to optimize the procedure and peak area was used for measurement. Instrumental limits of detection of 0.05 μg L -1 were obtained for ionic (Hg 2+ ) and HgCH 3 + , for an injection volume of 200 μL. The multipath atomic absorption spectrometer proved to be a competitive mercury detector in hyphenated systems in relation to the most commonly used atomic fluorescence and inductively coupled plasma mass spectrometric detectors. Preliminary application studies were performed for the determination of methyl mercury in sediments.

Analytical Approach to Target Detection and Localization at High-Frequency Bands Using Multipath Propagation

Science.gov (United States)

2016-04-25

ElectroMagnetic, Multipath propagation, Reflection-diffraction, SAR signal processing 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18...protection, and traffic surveillance, etc. With these above reasons, we are motivated to introduce a new approach to the target detection and...coherent integrating the backscattering signal , we propose a 3D propagation model that is useful not only in explaining the mechanisms of wave

Groundwater flow into underground openings in fractured crystalline rocks: an interpretation based on long channels

Science.gov (United States)

Black, John H.; Woodman, Nicholas D.; Barker, John A.

2017-03-01

Rethinking an old tracer experiment in fractured crystalline rock suggests a concept of groundwater flow in sparse networks of long channels that is supported by results from an innovative lattice network model. The model, HyperConv, can vary the mean length of `strings' of connected bonds, and the gaps between them, using two independent probability functions. It is found that networks of long channels are able to percolate at lower values of (bond) density than networks of short channels. A general relationship between mean channel length, mean gap length and probability of percolation has been developed which incorporates the well-established result for `classical' lattice network models as a special case. Using parameters appropriate to a 4-m diameter drift located 360 m below surface at Stripa Mine Underground Research Laboratory in Sweden, HyperConv is able to reproduce values of apparent positive skin, as observed in the so-called Macropermeability Experiment, but only when mean channel length exceeds 10 m. This implies that such channel systems must cross many fracture intersections without bifurcating. A general relationship in terms of flow dimension is suggested. Some initial investigations using HyperConv show that the commonly observed feature, `compartmentalization', only occurs when channel density is just above the percolation threshold. Such compartments have been observed at Kamaishi Experimental Mine (Japan) implying a sparse flow network. It is suggested that compartments and skin are observable in the field, indicate sparse channel systems, and could form part of site characterization for deep nuclear waste repositories.

Channel Deviation-Based Power Control in Body Area Networks.

Science.gov (United States)

Van, Son Dinh; Cotton, Simon L; Smith, David B

2018-05-01

Internet enabled body area networks (BANs) will form a core part of future remote health monitoring and ambient assisted living technology. In BAN applications, due to the dynamic nature of human activity, the off-body BAN channel can be prone to deep fading caused by body shadowing and multipath fading. Using this knowledge, we present some novel practical adaptive power control protocols based on the channel deviation to simultaneously prolong the lifetime of wearable devices and reduce outage probability. The proposed schemes are both flexible and relatively simple to implement on hardware platforms with constrained resources making them inherently suitable for BAN applications. We present the key algorithm parameters used to dynamically respond to the channel variation. This allows the algorithms to achieve a better energy efficiency and signal reliability in everyday usage scenarios such as those in which a person undertakes many different activities (e.g., sitting, walking, standing, etc.). We also profile their performance against traditional, optimal, and other existing schemes for which it is demonstrated that not only does the outage probability reduce significantly, but the proposed algorithms also save up to average transmit power compared to the competing schemes.

Statistical Modeling of Ultrawideband Body-Centric Wireless Channels Considering Room Volume

Directory of Open Access Journals (Sweden)

Miyuki Hirose

2012-01-01

Full Text Available This paper presents the results of a statistical modeling of onbody ultrawideband (UWB radio channels for wireless body area network (WBAN applications. Measurements were conducted in five different rooms. A measured delay profile can be divided into two domains; in the first domain (04 ns has multipath components that are dominant and dependent on room volume. The first domain was modeled with a conventional power decay law model, and the second domain with a modified Saleh-Valenzuela model considering the room volume. Realizations of the impulse responses are presented based on the composite model and compared with the measured average power delay profiles.

Location aware event driven multipath routing in Wireless Sensor Networks: Agent based approach

Directory of Open Access Journals (Sweden)

A.V. Sutagundar

2013-03-01

Full Text Available Wireless Sensor Networks (WSNs demand reliable and energy efficient paths for critical information delivery to sink node from an event occurrence node. Multipath routing facilitates reliable data delivery in case of critical information. This paper proposes an event triggered multipath routing in WSNs by employing a set of static and mobile agents. Every sensor node is assumed to know the location information of the sink node and itself. The proposed scheme works as follows: (1 Event node computes the arbitrary midpoint between an event node and the sink node by using location information. (2 Event node establishes a shortest path from itself to the sink node through the reference axis by using a mobile agent with the help of location information; the mobile agent collects the connectivity information and other parameters of all the nodes on the way and provides the information to the sink node. (3 Event node finds the arbitrary location of the special (middle intermediate nodes (above/below reference axis by using the midpoint location information given in step 1. (4 Mobile agent clones from the event node and the clones carry the event type and discover the path passing through special intermediate nodes; the path above/below reference axis looks like an arc. While migrating from one sensor node to another along the traversed path, each mobile agent gathers the node information (such as node id, location information, residual energy, available bandwidth, and neighbors connectivity and delivers to the sink node. (5 The sink node constructs a partial topology, connecting event and sink node by using the connectivity information delivered by the mobile agents. Using the partial topology information, sink node finds the multipath and path weight factor by using link efficiency, energy ratio, and hop distance. (6 The sink node selects the number of paths among the available paths based upon the criticalness of an event, and (7 if the event is non

Investigation of Range Profiles from a Simplified Ship on Rough Sea Surface and Its Multipath Imaging Mechanisms

Directory of Open Access Journals (Sweden)

Siyuan He

2012-01-01

Full Text Available The range profiles of a two-dimension (2 D perfect electric conductor (PEC ship on a wind-driven rough sea surface are derived by performing an inverse discrete Fourier transform (IDFT on the wide band backscattered field. The rough sea surface is assuming to be a PEC surface. The back scattered field is computed based on EM numerical simulation when the frequencies are sampled between 100 MHz and 700 MHz. Considering the strong coupling interactions between the ship and sea, the complicated multipath effect to the range profile characteristics is fully analyzed based on the multipath imaging mechanisms. The coupling mechanisms could be explained by means of ray theory prediction and numerical extraction of the coupling currents. The comparison of the range profile locations between ray theory prediction and surface current simulation is implemented and analyzed in this paper. Finally, the influence of different sea states on the radar target signatures has been examined and discussed.

Opportunistic relaying in multipath and slow fading channel: Relay selection and optimal relay selection period

KAUST Repository

Sungjoon Park,; Stark, Wayne E.

2011-01-01

In this paper we present opportunistic relay communication strategies of decode and forward relaying. The channel that we are considering includes pathloss, shadowing, and fast fading effects. We find a simple outage probability formula

Multipath colourimetric assay for copper(II) ions utilizing MarR functionalized gold nanoparticles

Science.gov (United States)

Wang, Yulong; Wang, Limin; Su, Zhenhe; Xue, Juanjuan; Dong, Jinbo; Zhang, Cunzheng; Hua, Xiude; Wang, Minghua; Liu, Fengquan

2017-02-01

We use the multiple antibiotic resistance regulator (MarR), as a highly selective biorecognition elements in a multipath colourimetric sensing strategy for the fast detection of Cu2+ in water samples. The colourimetric assay is based on the aggregation of MarR-coated gold nanoparticles in the presence of Cu2+ ions, which induces a red-to-purple colour change of the solution. The colour variation in the gold nanoparticle aggregation process can be used for qualitative and quantitative detection of Cu2+ by the naked eye, and with UV-vis and smartphone-based approaches. The three analysis techniques used in the multipath colourimetric assay complement each other and provide greater flexibility for differing requirements and conditions, making the assay highly applicable for Cu2+ detection. Under optimal conditions, the Cu2+ concentration was quantified in less than 5 min with limits of detection for the naked eye, UV-vis and smartphone-based approaches of 1 μM, 405 nM and 61 nM, respectively. Moreover, the sensing system exhibited excellent selectivity and practical application for Cu2+ detection in real water samples. Thus, our strategy has great potential for application in on-site monitoring of Cu2+, and the unique response of MarR towards copper ions may provide a new approach to Cu2+ sensing.

Sparse structure regularized ranking

KAUST Repository

Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

2014-01-01

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse

Sparse structure regularized ranking

KAUST Repository

Wang, Jim Jing-Yan

2014-04-17

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

Robust and Secure Watermarking Using Sparse Information of Watermark for Biometric Data Protection

OpenAIRE

Rohit M Thanki; Ved Vyas Dwivedi; Komal Borisagar

2016-01-01

Biometric based human authentication system is used for security purpose in many organizations in the present world. This biometric authentication system has several vulnerable points. Two of vulnerable points are protection of biometric templates at system database and protection of biometric templates at communication channel between two modules of biometric authentication systems. In this paper proposed a robust watermarking scheme using the sparse information of watermark biometric to sec...

QoE-Driven Energy-Aware Multipath Content Delivery Approach for MPTCP-Based Mobile Phones

Institute of Scientific and Technical Information of China (English)

Yuanlong Cao; Shengyang Chen; Qinghua Liu; Yi Zuo; Hao Wang; Minghe Huang

2017-01-01

Mobile phones equipped with multiple wireless interfaces can increase their goodput performance by making use of concurrent transmissions over multiple paths,enabled by the Multipath TCP (MPTCP).However,utilizing MPTCP for data delivery may generally result in higher energy consumption,while the battery power of a mobile phone is limited.Thus,how to optimize the energy usage becomes very crucial and urgent.In this paper,we propose MPTCP-QE,a novel quality of experience (QoE)-driven energy-aware multipath content delivery approach for MPTCP-based mobile phones.The main idea of MPTCP-QE is described as follows:it first provides an application rate-aware energy-efficient subflow management strategy to tradeoff throughput performance and energy consumption for mobile phones;then uses an available bandwidth-aware congestion window fast recovery strategy to make a sender avoid unnecessary slow-start and utilize wireless resource quickly;and further introduces a novel receiver-driven energy-efficient SACK strategy to help a receiver possible to detect SACK loss timely and trigger loss recovery in a more energy-efficient way.The simulation results show that with the MPTCP-QE,the energy usage is enhanced while the performance level is maintained compared to existing MPTCP solutions.

Turbulent flows over sparse canopies

Science.gov (United States)

Sharma, Akshath; García-Mayoral, Ricardo

2018-04-01

Turbulent flows over sparse and dense canopies exerting a similar drag force on the flow are investigated using Direct Numerical Simulations. The dense canopies are modelled using a homogeneous drag force, while for the sparse canopy, the geometry of the canopy elements is represented. It is found that on using the friction velocity based on the local shear at each height, the streamwise velocity fluctuations and the Reynolds stress within the sparse canopy are similar to those from a comparable smooth-wall case. In addition, when scaled with the local friction velocity, the intensity of the off-wall peak in the streamwise vorticity for sparse canopies also recovers a value similar to a smooth-wall. This indicates that the sparse canopy does not significantly disturb the near-wall turbulence cycle, but causes its rescaling to an intensity consistent with a lower friction velocity within the canopy. In comparison, the dense canopy is found to have a higher damping effect on the turbulent fluctuations. For the case of the sparse canopy, a peak in the spectral energy density of the wall-normal velocity, and Reynolds stress is observed, which may indicate the formation of Kelvin-Helmholtz-like instabilities. It is also found that a sparse canopy is better modelled by a homogeneous drag applied on the mean flow alone, and not the turbulent fluctuations.

Modeling of Non-WSSUS Double-Rayleigh Fading Channels for Vehicular Communications

Directory of Open Access Journals (Sweden)

Carlos A. Gutiérrez

2017-01-01

Full Text Available This paper deals with the modeling of nonstationary time-frequency (TF dispersive multipath fading channels for vehicle-to-vehicle (V2V communication systems. As a main contribution, the paper presents a novel geometry-based statistical channel model that facilitates the analysis of the nonstationarities of V2V fading channels arising at a small-scale level due to the time-varying nature of the propagation delays. This new geometrical channel model has been formulated following the principles of plane wave propagation (PWP and assuming that the transmitted signal reaches the receiver antenna through double interactions with multiple interfering objects (IOs randomly located in the propagation area. As a consequence of such interactions, the first-order statistics of the channel model’s envelope are shown to follow a worse-than-Rayleigh distribution; specifically, they follow a double-Rayleigh distribution. General expressions are derived for the envelope and phase distributions, four-dimensional (4D TF correlation function (TF-CF, and TF-dependent delay and Doppler profiles of the proposed channel model. Such expressions are valid regardless of the underlying geometry of the propagation area. Furthermore, a closed-form solution of the 4D TF-CF is presented for the particular case of the geometrical two-ring scattering model. The obtained results provide new theoretical insights into the correlation and spectral properties of small-scale nonstationary V2V double-Rayleigh fading channels.

Discriminative sparse coding on multi-manifolds

KAUST Repository

Wang, J.J.-Y.; Bensmail, H.; Yao, N.; Gao, Xin

2013-01-01

Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.

Discriminative sparse coding on multi-manifolds

KAUST Repository

Wang, J.J.-Y.

2013-09-26

Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin

2015-04-03

© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

Sparse distributed memory overview

Science.gov (United States)

Raugh, Mike

1990-01-01

The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

In-place sparse suffix sorting

DEFF Research Database (Denmark)

Prezza, Nicola

2018-01-01

information regarding the lexicographical order of a size-b subset of all n text suffixes is often needed. Such information can be stored space-efficiently (in b words) in the sparse suffix array (SSA). The SSA and its relative sparse LCP array (SLCP) can be used as a space-efficient substitute of the sparse...... suffix tree. Very recently, Gawrychowski and Kociumaka [11] showed that the sparse suffix tree (and therefore SSA and SLCP) can be built in asymptotically optimal O(b) space with a Monte Carlo algorithm running in O(n) time. The main reason for using the SSA and SLCP arrays in place of the sparse suffix...... tree is, however, their reduced space of b words each. This leads naturally to the quest for in-place algorithms building these arrays. Franceschini and Muthukrishnan [8] showed that the full suffix array can be built in-place and in optimal running time. On the other hand, finding sub-quadratic in...

Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

Science.gov (United States)

Moody, Daniela; Wohlberg, Brendt

2018-01-02

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

Design and implementation of channel estimation for low-voltage power line communication systems based on OFDM

International Nuclear Information System (INIS)

Zhao Huidong; Hei Yong; Qiao Shushan; Ye Tianchun

2012-01-01

An optimized channel estimation algorithm based on a time-spread structure in OFDM low-voltage power line communication (PLC) systems is proposed to achieve a lower bit error rate (BER). This paper optimizes the best maximum multi-path delay of the linear minimum mean square error (LMMSE) algorithm in time-domain spread OFDM systems. Simulation results indicate that the BER of the improved method is lower than that of conventional LMMSE algorithm, especially when the signal-to-noise ratio (SNR) is lower than 0 dB. Both the LMMSE algorithm and the proposed algorithm are implemented and fabricated in CSMC 0.18 μm technology. This paper analyzes and compares the hardware complexity and performance of the two algorithms. Measurements indicate that the proposed channel estimator has better performance than the conventional estimator.

Discrete Sparse Coding.

Science.gov (United States)

Exarchakis, Georgios; Lücke, Jörg

2017-11-01

Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions. We consider the general case in which the latents (while being sparse) can take on any value of a finite set of possible values and in which we learn the prior probability of any value from data. This approach can be applied to any data generated by discrete causes, and it can be applied as an approximation of continuous causes. As the prior probabilities are learned, the approach then allows for estimating the prior shape without assuming specific functional forms. To efficiently train the parameters of our probabilistic generative model, we apply a truncated expectation-maximization approach (expectation truncation) that we modify to work with a general discrete prior. We evaluate the performance of the algorithm by applying it to a variety of tasks: (1) we use artificial data to verify that the algorithm can recover the generating parameters from a random initialization, (2) use image patches of natural images and discuss the role of the prior for the extraction of image components, (3) use extracellular recordings of neurons to present a novel method of analysis for spiking neurons that includes an intuitive discretization strategy, and (4) apply the algorithm on the task of encoding audio waveforms of human speech. The diverse set of numerical experiments presented in this letter suggests that discrete sparse coding algorithms can scale efficiently to work with realistic data sets and provide novel statistical quantities to describe the structure of the data.

Low Complexity Submatrix Divided MMSE Sparse-SQRD Detection for MIMO-OFDM with ESPAR Antenna Receiver

Directory of Open Access Journals (Sweden)

Diego Javier Reinoso Chisaguano

2013-01-01

Full Text Available Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM with an electronically steerable passive array radiator (ESPAR antenna receiver can improve the bit error rate performance and obtains additional diversity gain without increasing the number of Radio Frequency (RF front-end circuits. However, due to the large size of the channel matrix, the computational cost required for the detection process using Vertical-Bell Laboratories Layered Space-Time (V-BLAST detection is too high to be implemented. Using the minimum mean square error sparse-sorted QR decomposition (MMSE sparse-SQRD algorithm for the detection process the average computational cost can be considerably reduced but is still higher compared with a conventional MIMOOFDM system without ESPAR antenna receiver. In this paper, we propose to use a low complexity submatrix divided MMSE sparse-SQRD algorithm for the detection process of MIMOOFDM with ESPAR antenna receiver. The computational cost analysis and simulation results show that on average the proposed scheme can further reduce the computational cost and achieve a complexity comparable to the conventional MIMO-OFDM detection schemes.

Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks.

Science.gov (United States)

Robinson, Y Harold; Rajaram, M

2015-01-01

Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.

Analysis of BeiDou Satellite Measurements with Code Multipath and Geometry-Free Ionosphere-Free Combinations

Directory of Open Access Journals (Sweden)

Qile Zhao

2016-01-01

Full Text Available Using GNSS observable from some stations in the Asia-Pacific area, the carrier-to-noise ratio (CNR and multipath combinations of BeiDou Navigation Satellite System (BDS, as well as their variations with time and/or elevation were investigated and compared with those of GPS and Galileo. Provided the same elevation, the CNR of B1 observables is the lowest among the three BDS frequencies, while B3 is the highest. The code multipath combinations of BDS inclined geosynchronous orbit (IGSO and medium Earth orbit (MEO satellites are remarkably correlated with elevation, and the systematic “V” shape trends could be eliminated through between-station-differencing or modeling correction. Daily periodicity was found in the geometry-free ionosphere-free (GFIF combinations of both BDS geostationary Earth orbit (GEO and IGSO satellites. The variation range of carrier phase GFIF combinations of GEO satellites is −2.0 to 2.0 cm. The periodicity of carrier phase GFIF combination could be significantly mitigated through between-station differencing. Carrier phase GFIF combinations of BDS GEO and IGSO satellites might also contain delays related to satellites. Cross-correlation suggests that the GFIF combinations’ time series of some GEO satellites might vary according to their relative geometries with the sun.

Analysis of BeiDou Satellite Measurements with Code Multipath and Geometry-Free Ionosphere-Free Combinations.

Science.gov (United States)

Zhao, Qile; Wang, Guangxing; Liu, Zhizhao; Hu, Zhigang; Dai, Zhiqiang; Liu, Jingnan

2016-01-20

Using GNSS observable from some stations in the Asia-Pacific area, the carrier-to-noise ratio (CNR) and multipath combinations of BeiDou Navigation Satellite System (BDS), as well as their variations with time and/or elevation were investigated and compared with those of GPS and Galileo. Provided the same elevation, the CNR of B1 observables is the lowest among the three BDS frequencies, while B3 is the highest. The code multipath combinations of BDS inclined geosynchronous orbit (IGSO) and medium Earth orbit (MEO) satellites are remarkably correlated with elevation, and the systematic "V" shape trends could be eliminated through between-station-differencing or modeling correction. Daily periodicity was found in the geometry-free ionosphere-free (GFIF) combinations of both BDS geostationary Earth orbit (GEO) and IGSO satellites. The variation range of carrier phase GFIF combinations of GEO satellites is -2.0 to 2.0 cm. The periodicity of carrier phase GFIF combination could be significantly mitigated through between-station differencing. Carrier phase GFIF combinations of BDS GEO and IGSO satellites might also contain delays related to satellites. Cross-correlation suggests that the GFIF combinations' time series of some GEO satellites might vary according to their relative geometries with the sun.

Solving Sparse Polynomial Optimization Problems with Chordal Structure Using the Sparse, Bounded-Degree Sum-of-Squares Hierarchy

NARCIS (Netherlands)

Marandi, Ahmadreza; de Klerk, Etienne; Dahl, Joachim

The sparse bounded degree sum-of-squares (sparse-BSOS) hierarchy of Weisser, Lasserre and Toh [arXiv:1607.01151,2016] constructs a sequence of lower bounds for a sparse polynomial optimization problem. Under some assumptions, it is proven by the authors that the sequence converges to the optimal

Extended Kalman Filter Channel Estimation for Line-of-Sight Detection in WCDMA Mobile Positioning

Directory of Open Access Journals (Sweden)

Abdelmonaem Lakhzouri

2003-12-01

Full Text Available In mobile positioning, it is very important to estimate correctly the delay between the transmitter and the receiver. When the receiver is in line-of-sight (LOS condition with the transmitter, the computation of the mobile position in two dimensions becomes straightforward. In this paper, the problem of LOS detection in WCDMA for mobile positioning is considered, together with joint estimation of the delays and channel coefficients. These are very challenging topics in multipath fading channels because LOS component is not always present, and when it is present, it might be severely affected by interfering paths spaced at less than one chip distance (closely spaced paths. The extended Kalman filter (EKF is used to estimate jointly the delays and complex channel coefficients. The decision whether the LOS component is present or not is based on statistical tests to determine the distribution of the channel coefficient corresponding to the first path. The statistical test-based techniques are practical, simple, and of low computation complexity, which is suitable for WCDMA receivers. These techniques can provide an accurate decision whether LOS component is present or not.

Providing Formative Assessment to Students Solving Multipath Engineering Problems with Complex Arrangements of Interacting Parts: An Intelligent Tutor Approach

Science.gov (United States)

Steif, Paul S.; Fu, Luoting; Kara, Levent Burak

2016-01-01

Problems faced by engineering students involve multiple pathways to solution. Students rarely receive effective formative feedback on handwritten homework. This paper examines the potential for computer-based formative assessment of student solutions to multipath engineering problems. In particular, an intelligent tutor approach is adopted and…

Real-Time Emulation of Nonstationary Channels in Safety-Relevant Vehicular Scenarios

Directory of Open Access Journals (Sweden)

Golsa Ghiaasi

2018-01-01

Full Text Available This paper proposes and discusses the architecture for a real-time vehicular channel emulator capable of reproducing the input/output behavior of nonstationary time-variant radio propagation channels in safety-relevant vehicular scenarios. The vehicular channel emulator architecture aims at a hardware implementation which requires minimal hardware complexity for emulating channels with the varying delay-Doppler characteristics of safety-relevant vehicular scenarios. The varying delay-Doppler characteristics require real-time updates to the multipath propagation model for each local stationarity region. The vehicular channel emulator is used for benchmarking the packet error performance of commercial off-the-shelf (COTS vehicular IEEE 802.11p modems and a fully software-defined radio-based IEEE 802.11p modem stack. The packet error ratio (PER estimated from temporal averaging over a single virtual drive and the packet error probability (PEP estimated from ensemble averaging over repeated virtual drives are evaluated and compared for the same vehicular scenario. The proposed architecture is realized as a virtual instrument on National Instruments™ LabVIEW. The National Instrument universal software radio peripheral with reconfigurable input-output (USRP-Rio 2953R is used as the software-defined radio platform for implementation; however, the results and considerations reported are of general purpose and can be applied to other platforms. Finally, we discuss the PER performance of the modem for two categories of vehicular channel models: a vehicular nonstationary channel model derived for urban single lane street crossing scenario of the DRIVEWAY’09 measurement campaign and the stationary ETSI models.

Multi-path transportation futures study : vehicle characterization and scenario analyses.

Energy Technology Data Exchange (ETDEWEB)

Plotkin, S. E.; Singh, M. K.; Energy Systems; TA Engineering; ORNL

2009-12-03

Projecting the future role of advanced drivetrains and fuels in the light vehicle market is inherently difficult, given the uncertainty (and likely volatility) of future oil prices, inadequate understanding of likely consumer response to new technologies, the relative infancy of several important new technologies with inevitable future changes in their performance and costs, and the importance - and uncertainty - of future government marketplace interventions (e.g., new regulatory standards or vehicle purchase incentives). This Multi-Path Transportation Futures (MP) Study has attempted to improve our understanding of this future role by examining several scenarios of vehicle costs, fuel prices, government subsidies, and other key factors. These are projections, not forecasts, in that they try to answer a series of 'what if' questions without assigning probabilities to most of the basic assumptions.

Parallelized preconditioned BiCGStab solution of sparse linear system equations in F-COBRA-TF

International Nuclear Information System (INIS)

Geemert, Rene van; Glück, Markus; Riedmann, Michael; Gabriel, Harry

2011-01-01

Recently, the in-house development of a preconditioned and parallelized BiCGStab solver has been pursued successfully in AREVA’s advanced sub-channel code F-COBRA-TF. This solver can be run either in a sequential computation mode on a single CPU, or in a parallel computation mode on multiple parallel CPUs. The developed procedure enables the computation of several thousands of successive sparse linear system solutions in F-COBRA-TF with acceptable wall clock run times. The current paper provides general information about F-COBRA-TF in terms of modeling capabilities and application areas, and points out where the relevance arises for the efficient iterative solution of sparse linear systems. Furthermore, the preconditioning and parallelization strategies in the developed BiCGStab iterative solution approach are discussed. The paper is concluded with a number of verification examples. (author)

When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

KAUST Repository

Wang, Jim Jing-Yan; Cui, Xuefeng; Yu, Ge; Guo, Lili; Gao, Xin

2017-01-01

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays

Optimization of Spatiotemporal Apertures in Channel Sounding

DEFF Research Database (Denmark)

Pedersen, Troels; Pedersen, Claus; Yin, Xuefeng

2008-01-01

a spatiotemporal model which can describe parallel as well as switched sounding systems. The proposed model is applicable for arbitrary layouts of the spatial arrays. To simplify the derivations we investigate the special case of linear spatial arrays. However, the results obtained for linear arrays can......In this paper we investigate the impact of the spatio-temporal aperture of a channel sounding system equipped with antenna arrays at the transmitter and receiver on the accuracy of joint estimation of Doppler frequency and bi-direction. The contribution of this work is three-fold. Firstly, we state...... be generalized to arbitrary arrays. Secondly, we give the necessary and sufficient conditions for a spatio-temporal array to yield the minimum Cramér-Rao lower bound in the single-path case and Bayesian Cramér-Rao Lower Bound in the multipath case. The obtained conditions amount to an orthogonality condition...

Sparse Image Reconstruction in Computed Tomography

DEFF Research Database (Denmark)

Jørgensen, Jakob Sauer

In recent years, increased focus on the potentially harmful effects of x-ray computed tomography (CT) scans, such as radiation-induced cancer, has motivated research on new low-dose imaging techniques. Sparse image reconstruction methods, as studied for instance in the field of compressed sensing...... applications. This thesis takes a systematic approach toward establishing quantitative understanding of conditions for sparse reconstruction to work well in CT. A general framework for analyzing sparse reconstruction methods in CT is introduced and two sets of computational tools are proposed: 1...... contributions to a general set of computational characterization tools. Thus, the thesis contributions help advance sparse reconstruction methods toward routine use in...

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu

2015-01-01

predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths

Multipath Suppression with an Absorber for UWB Wind Turbine Blade Deflection Sensing Systems

DEFF Research Database (Denmark)

Zhang, Shuai; Franek, Ondrej; Eggers, Patrick Claus F.

2017-01-01

The deflection of a wind turbine blade can be monitored with an ultra-wideband (UWB) deflection sensing system which consists of one transmitting antenna at the blade tip and two receiving antennas at the blade root. The blade deflection is calculated by two estimated tip-root antenna distances...... verifications of the proposed method are carried out with different full-blade measurements. From all the results, it is found that the proposed technique can efficiently suppress multipath for the in-blade tip antenna, and improve the pulse wave front fidelity, so that the UWB sensing system can also...

Sparse decompositions in 'incoherent' dictionaries

DEFF Research Database (Denmark)

Gribonval, R.; Nielsen, Morten

2003-01-01

a unique sparse representation in such a dictionary. In particular, it is proved that the result of Donoho and Huo, concerning the replacement of a combinatorial optimization problem with a linear programming problem when searching for sparse representations, has an analog for dictionaries that may...

On the Comparative Performance Analysis of Turbo-Coded Non-Ideal Sigle-Carrier and Multi-Carrier Waveforms over Wideband Vogler-Hoffmeyer HF Channels

Directory of Open Access Journals (Sweden)

F. Genc

2014-09-01

Full Text Available The purpose of this paper is to compare the turbo-coded Orthogonal Frequency Division Multiplexing (OFDM and turbo-coded Single Carrier Frequency Domain Equalization (SC-FDE systems under the effects of Carrier Frequency Offset (CFO, Symbol Timing Offset (STO and phase noise in wide-band Vogler-Hoffmeyer HF channel model. In mobile communication systems multi-path propagation occurs. Therefore channel estimation and equalization is additionally necessary. Furthermore a non-ideal local oscillator generally is misaligned with the operating frequency at the receiver. This causes carrier frequency offset. Hence in coded SC-FDE and coded OFDM systems; a very efficient, low complex frequency domain channel estimation and equalization is implemented in this paper. Also Cyclic Prefix (CP based synchronization synchronizes the clock and carrier frequency offset.The simulations show that non-ideal turbo-coded OFDM has better performance with greater diversity than non-ideal turbo-coded SC-FDE system in HF channel.

Data analysis in high-dimensional sparse spaces

DEFF Research Database (Denmark)

Clemmensen, Line Katrine Harder

classification techniques for high-dimensional problems are presented: Sparse discriminant analysis, sparse mixture discriminant analysis and orthogonality constrained support vector machines. The first two introduces sparseness to the well known linear and mixture discriminant analysis and thereby provide low...... are applied to classifications of fish species, ear canal impressions used in the hearing aid industry, microbiological fungi species, and various cancerous tissues and healthy tissues. In addition, novel applications of sparse regressions (also called the elastic net) to the medical, concrete, and food...

A sparse-grid isogeometric solver

KAUST Repository

Beck, Joakim; Sangalli, Giancarlo; Tamellini, Lorenzo

2018-01-01

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90’s in the context of the approximation of high-dimensional PDEs.The tests that we report show that, in accordance to the literature, a sparse-grid construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

A sparse-grid isogeometric solver

KAUST Repository

Beck, Joakim

2018-02-28

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90’s in the context of the approximation of high-dimensional PDEs.The tests that we report show that, in accordance to the literature, a sparse-grid construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

Supervised Transfer Sparse Coding

KAUST Repository

Al-Shedivat, Maruan

2014-07-27

A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

Joint Group Sparse PCA for Compressed Hyperspectral Imaging.

Science.gov (United States)

Khan, Zohaib; Shafait, Faisal; Mian, Ajmal

2015-12-01

A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition.

A Space-Time Signal Decomposition Algorithm for Downlink MIMO DS-CDMA Receivers

Science.gov (United States)

Wang, Yung-Yi; Fang, Wen-Hsien; Chen, Jiunn-Tsair

We propose a dimension reduction algorithm for the receiver of the downlink of direct-sequence code-division multiple access (DS-CDMA) systems in which both the transmitters and the receivers employ antenna arrays of multiple elements. To estimate the high order channel parameters, we develop a layered architecture using dimension-reduced parameter estimation algorithms to estimate the frequency-selective multipath channels. In the proposed architecture, to exploit the space-time geometric characteristics of multipath channels, spatial beamformers and constrained (or unconstrained) temporal filters are adopted for clustered-multipath grouping and path isolation. In conjunction with the multiple access interference (MAI) suppression techniques, the proposed architecture jointly estimates the direction of arrivals, propagation delays, and fading amplitudes of the downlink fading multipaths. With the outputs of the proposed architecture, the signals of interest can then be naturally detected by using path-wise maximum ratio combining. Compared to the traditional techniques, such as the Joint-Angle-and-Delay-Estimation (JADE) algorithm for DOA-delay joint estimation and the space-time minimum mean square error (ST-MMSE) algorithm for signal detection, computer simulations show that the proposed algorithm substantially mitigate the computational complexity at the expense of only slight performance degradation.

Channel Parameter Estimation for Scatter Cluster Model Using Modified MUSIC Algorithm

Directory of Open Access Journals (Sweden)

Jinsheng Yang

2012-01-01

Full Text Available Recently, the scatter cluster models which precisely evaluate the performance of the wireless communication system have been proposed in the literature. However, the conventional SAGE algorithm does not work for these scatter cluster-based models because it performs poorly when the transmit signals are highly correlated. In this paper, we estimate the time of arrival (TOA, the direction of arrival (DOA, and Doppler frequency for scatter cluster model by the modified multiple signal classification (MUSIC algorithm. Using the space-time characteristics of the multiray channel, the proposed algorithm combines the temporal filtering techniques and the spatial smoothing techniques to isolate and estimate the incoming rays. The simulation results indicated that the proposed algorithm has lower complexity and is less time-consuming in the dense multipath environment than SAGE algorithm. Furthermore, the estimations’ performance increases with elements of receive array and samples length. Thus, the problem of the channel parameter estimation of the scatter cluster model can be effectively addressed with the proposed modified MUSIC algorithm.

Diversity Techniques for Single-Carrier Packet Retransmissions over Frequency-Selective Channels

Directory of Open Access Journals (Sweden)

Assimi Abdel-Nasser

2009-01-01

Full Text Available In data packet communication systems over multipath frequency-selective channels, hybrid automatic repeat request (HARQ protocols are usually used in order to ensure data reliability. For single-carrier packet transmission in slow fading environment, an identical retransmission of the same packet, due to a decoding failure, does not fully exploit the available time diversity in retransmission-based HARQ protocols. In this paper, we compare two transmit diversity techniques, namely, cyclic frequency-shift diversity and bit-interleaving diversity. Both techniques can be integrated in the HARQ scheme in order to improve the performance of the joint detector. Their performance in terms of pairwise error probability is investigated using maximum likelihood detection and decoding. The impact of the channel memory and the modulation order on the performance gain is emphasized. In practice, we use low complexity linear filter-based equalization which can be efficiently implemented in the frequency domain. The use of iterative equalization and decoding is also considered. The performance gain in terms of frame error rate and data throughput is evaluated by numerical simulations.

Parallel Sparse Matrix - Vector Product

DEFF Research Database (Denmark)

Alexandersen, Joe; Lazarov, Boyan Stefanov; Dammann, Bernd

This technical report contains a case study of a sparse matrix-vector product routine, implemented for parallel execution on a compute cluster with both pure MPI and hybrid MPI-OpenMP solutions. C++ classes for sparse data types were developed and the report shows how these class can be used...

Multi-threaded Sparse Matrix Sparse Matrix Multiplication for Many-Core and GPU Architectures.

Energy Technology Data Exchange (ETDEWEB)

Deveci, Mehmet [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Trott, Christian Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rajamanickam, Sivasankaran [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2018-01-01

Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix- matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.

Sparse approximation with bases

CERN Document Server

2015-01-01

This book systematically presents recent fundamental results on greedy approximation with respect to bases. Motivated by numerous applications, the last decade has seen great successes in studying nonlinear sparse approximation. Recent findings have established that greedy-type algorithms are suitable methods of nonlinear approximation in both sparse approximation with respect to bases and sparse approximation with respect to redundant systems. These insights, combined with some previous fundamental results, form the basis for constructing the theory of greedy approximation. Taking into account the theoretical and practical demand for this kind of theory, the book systematically elaborates a theoretical framework for greedy approximation and its applications.  The book addresses the needs of researchers working in numerical mathematics, harmonic analysis, and functional analysis. It quickly takes the reader from classical results to the latest frontier, but is written at the level of a graduate course and do...

Efficient convolutional sparse coding

Science.gov (United States)

Wohlberg, Brendt

2017-06-20

Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.

An interactive visualization tool for multi-channel confocal microscopy data in neurobiology research

KAUST Repository

Yong Wan,; Otsuna, H.; Chi-Bin Chien,; Hansen, C.

2009-01-01

; one channel may have dense data, while another has sparse; and there are often structures at several spatial scales: subneuronal domains, neurons, and large groups of neurons (brain regions). Even qualitative analysis can therefore require

Hyperspectral Unmixing with Robust Collaborative Sparse Regression

Directory of Open Access Journals (Sweden)

Chang Li

2016-07-01

Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.

Image fusion using sparse overcomplete feature dictionaries

Science.gov (United States)

Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

2015-10-06

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

Spatial Dynamic Wideband Modeling of the MIMO Satellite-to-Earth Channel

Directory of Open Access Journals (Sweden)

Andreas Lehner

2014-01-01

response (CIR time series depending on the movement profile of a land mobile terminal is presented in this paper. Based on high precise wideband measurements in L-band the model reproduces the correlated shadowing and multipath conditions in rich detail. The model includes time and space variant echo signals appearing and disappearing in dependence on the receive antenna position and movement, and the actual azimuths and elevations to the various signal sources. Attenuation and path delays relative to the hypothetical line of sight (LOS ensure usability for ranging purposes. Parameters for car and pedestrian applications in urban and suburban environments are provided. The channel characteristics are determined independently of the transmitted signal. Therefore the usability, for example, for GPS and GALILEO, as well as wideband communication services from hovering platforms, is given.

Manifold regularization for sparse unmixing of hyperspectral images.

Science.gov (United States)

Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin

2016-01-01

Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

PARTRACK - A particle tracking algorithm for transport and dispersion of solutes in a sparsely fractured rock

International Nuclear Information System (INIS)

Svensson, Urban

2001-04-01

A particle tracking algorithm, PARTRACK, that simulates transport and dispersion in a sparsely fractured rock is described. The main novel feature of the algorithm is the introduction of multiple particle states. It is demonstrated that the introduction of this feature allows for the simultaneous simulation of Taylor dispersion, sorption and matrix diffusion. A number of test cases are used to verify and demonstrate the features of PARTRACK. It is shown that PARTRACK can simulate the following processes, believed to be important for the problem addressed: the split up of a tracer cloud at a fracture intersection, channeling in a fracture plane, Taylor dispersion and matrix diffusion and sorption. From the results of the test cases, it is concluded that PARTRACK is an adequate framework for simulation of transport and dispersion of a solute in a sparsely fractured rock

Enhancing Scalability of Sparse Direct Methods

International Nuclear Information System (INIS)

Li, Xiaoye S.; Demmel, James; Grigori, Laura; Gu, Ming; Xia, Jianlin; Jardin, Steve; Sovinec, Carl; Lee, Lie-Quan

2007-01-01

TOPS is providing high-performance, scalable sparse direct solvers, which have had significant impacts on the SciDAC applications, including fusion simulation (CEMM), accelerator modeling (COMPASS), as well as many other mission-critical applications in DOE and elsewhere. Our recent developments have been focusing on new techniques to overcome scalability bottleneck of direct methods, in both time and memory. These include parallelizing symbolic analysis phase and developing linear-complexity sparse factorization methods. The new techniques will make sparse direct methods more widely usable in large 3D simulations on highly-parallel petascale computers

Regression with Sparse Approximations of Data

DEFF Research Database (Denmark)

Noorzad, Pardis; Sturm, Bob L.

2012-01-01

We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

Sparse adaptive filters for echo cancellation

CERN Document Server

Paleologu, Constantin

2011-01-01

Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati

A new scripting library for modeling flow and transport in fractured rock with channel networks

Science.gov (United States)

Dessirier, Benoît; Tsang, Chin-Fu; Niemi, Auli

2018-02-01

Deep crystalline bedrock formations are targeted to host spent nuclear fuel owing to their overall low permeability. They are however highly heterogeneous and only a few preferential paths pertaining to a small set of dominant rock fractures usually carry most of the flow or mass fluxes, a behavior known as channeling that needs to be accounted for in the performance assessment of repositories. Channel network models have been developed and used to investigate the effect of channeling. They are usually simpler than discrete fracture networks based on rock fracture mappings and rely on idealized full or sparsely populated lattices of channels. This study reexamines the fundamental parameter structure required to describe a channel network in terms of groundwater flow and solute transport, leading to an extended description suitable for unstructured arbitrary networks of channels. An implementation of this formalism in a Python scripting library is presented and released along with this article. A new algebraic multigrid preconditioner delivers a significant speedup in the flow solution step compared to previous channel network codes. 3D visualization is readily available for verification and interpretation of the results by exporting the results to an open and free dedicated software. The new code is applied to three example cases to verify its results on full uncorrelated lattices of channels, sparsely populated percolation lattices and to exemplify the use of unstructured networks to accommodate knowledge on local rock fractures.

Parallel sparse direct solver for integrated circuit simulation

CERN Document Server

Chen, Xiaoming; Yang, Huazhong

2017-01-01

This book describes algorithmic methods and parallelization techniques to design a parallel sparse direct solver which is specifically targeted at integrated circuit simulation problems. The authors describe a complete flow and detailed parallel algorithms of the sparse direct solver. They also show how to improve the performance by simple but effective numerical techniques. The sparse direct solver techniques described can be applied to any SPICE-like integrated circuit simulator and have been proven to be high-performance in actual circuit simulation. Readers will benefit from the state-of-the-art parallel integrated circuit simulation techniques described in this book, especially the latest parallel sparse matrix solution techniques. · Introduces complicated algorithms of sparse linear solvers, using concise principles and simple examples, without complex theory or lengthy derivations; · Describes a parallel sparse direct solver that can be adopted to accelerate any SPICE-like integrated circuit simulato...

Performance analysis of visible light communication using the STBC-OFDM technique for intelligent transportation systems

Science.gov (United States)

Li, Changping; Yi, Ying; Lee, Kyujin; Lee, Kyesan

2014-08-01

Visible light communication (VLC) applied in an intelligent transportation system (ITS) has attracted growing attentions, but it also faces challenges, for example deep path loss and optical multi-path dispersion. In this work, we modelled an actual outdoor optical channel as a Rician channel and further proposed space-time block coding (STBC) orthogonal frequency-division multiplexing (OFDM) technology to reduce the influence of severe optical multi-path dispersion associated with such a mock channel for achieving the effective BER of 10-6 even at a low signal-to-noise ratio (SNR). In this case, the optical signals transmission distance can be extended as long as possible. Through the simulation results of STBC-OFDM and single-input-single-output (SISO) counterparts in bit error rate (BER) performance comparison, we can distinctly observe that the VLC-ITS system using STBC-OFDM technique can obtain a strongly improved BER performance due to multi-path dispersion alleviation.

Biclustering via Sparse Singular Value Decomposition

KAUST Repository

Lee, Mihee

2010-02-16

Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets. © 2010, The International Biometric Society.

Robust Face Recognition Via Gabor Feature and Sparse Representation

Directory of Open Access Journals (Sweden)

Hao Yu-Juan

2016-01-01

Full Text Available Sparse representation based on compressed sensing theory has been widely used in the field of face recognition, and has achieved good recognition results. but the face feature extraction based on sparse representation is too simple, and the sparse coefficient is not sparse. In this paper, we improve the classification algorithm based on the fusion of sparse representation and Gabor feature, and then improved algorithm for Gabor feature which overcomes the problem of large dimension of the vector dimension, reduces the computation and storage cost, and enhances the robustness of the algorithm to the changes of the environment.The classification efficiency of sparse representation is determined by the collaborative representation,we simplify the sparse constraint based on L1 norm to the least square constraint, which makes the sparse coefficients both positive and reduce the complexity of the algorithm. Experimental results show that the proposed method is robust to illumination, facial expression and pose variations of face recognition, and the recognition rate of the algorithm is improved.

Message-Passing Receiver for OFDM Systems over Highly Delay-Dispersive Channels

DEFF Research Database (Denmark)

Barbu, Oana-Elena; Manchón, Carles Navarro; Rom, Christian

2017-01-01

Propagation channels with maximum excess delay exceeding the duration of the cyclic prefix (CP) in OFDM systems cause intercarrier and intersymbol interference which, unless accounted for, degrade the receiver performance. Using tools from Bayesian inference and sparse signal reconstruction, we...... derive an iterative algorithm that estimates an approximate representation of the channel impulse response and the noise variance, estimates and cancels the intrinsic interference and decodes the data over a block of symbols. Simulation results show that the receiver employing our algorithm outperforms...

An Environment-Friendly Multipath Routing Protocol for Underwater Acoustic Sensor Network

Directory of Open Access Journals (Sweden)

Yun Li

2017-01-01

Full Text Available Underwater Acoustic Sensor Network (UASN is a promising technique by facilitating a wide range of aquatic applications. However, routing scheme in UASN is a challenging task because of the characteristics of the nodes mobility, interruption of link, and interference caused by other underwater acoustic systems such as marine mammals. In order to achieve reliable data delivery in UASN, in this work, we present a disjoint multipath disruption-tolerant routing protocol for UASN (ENMR, which incorporates the Hue, Saturation, and Value color space (HSV model to establish routing paths to greedily forward data packets to sink nodes. ENMR applies the mechanism to maintain the network topology. Simulation results show that, compared with the classic underwater routing protocols named PVBF, ENMR can improve packet delivery ratio and reduce network latency while avoiding introducing additional energy consumption.

Sparse Learning with Stochastic Composite Optimization.

Science.gov (United States)

Zhang, Weizhong; Zhang, Lijun; Jin, Zhongming; Jin, Rong; Cai, Deng; Li, Xuelong; Liang, Ronghua; He, Xiaofei

2017-06-01

In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain O(√{log(1/δ)/T}) with δ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately O(log(log(T)/δ)/λT).

Noise-Assisted Concurrent Multipath Traffic Distribution in Ad Hoc Networks

Science.gov (United States)

Murata, Masayuki

2013-01-01

The concept of biologically inspired networking has been introduced to tackle unpredictable and unstable situations in computer networks, especially in wireless ad hoc networks where network conditions are continuously changing, resulting in the need of robustness and adaptability of control methods. Unfortunately, existing methods often rely heavily on the detailed knowledge of each network component and the preconfigured, that is, fine-tuned, parameters. In this paper, we utilize a new concept, called attractor perturbation (AP), which enables controlling the network performance using only end-to-end information. Based on AP, we propose a concurrent multipath traffic distribution method, which aims at lowering the average end-to-end delay by only adjusting the transmission rate on each path. We demonstrate through simulations that, by utilizing the attractor perturbation relationship, the proposed method achieves a lower average end-to-end delay compared to other methods which do not take fluctuations into account. PMID:24319375

Shearlets and Optimally Sparse Approximations

DEFF Research Database (Denmark)

Kutyniok, Gitta; Lemvig, Jakob; Lim, Wang-Q

2012-01-01

Multivariate functions are typically governed by anisotropic features such as edges in images or shock fronts in solutions of transport-dominated equations. One major goal both for the purpose of compression as well as for an efficient analysis is the provision of optimally sparse approximations...... optimally sparse approximations of this model class in 2D as well as 3D. Even more, in contrast to all other directional representation systems, a theory for compactly supported shearlet frames was derived which moreover also satisfy this optimality benchmark. This chapter shall serve as an introduction...... to and a survey about sparse approximations of cartoon-like images by band-limited and also compactly supported shearlet frames as well as a reference for the state-of-the-art of this research field....

Multilevel sparse functional principal component analysis.

Science.gov (United States)

Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S

2014-01-29

We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

A sparse version of IGA solvers

KAUST Repository

Beck, Joakim; Sangalli, Giancarlo; Tamellini, Lorenzo

2017-01-01

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90s in the context of the approximation of high-dimensional PDEs. The tests that we report show that, in accordance to the literature, a sparse grids construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

A sparse version of IGA solvers

KAUST Repository

Beck, Joakim

2017-07-30

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90s in the context of the approximation of high-dimensional PDEs. The tests that we report show that, in accordance to the literature, a sparse grids construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

Language Recognition via Sparse Coding

Science.gov (United States)

2016-09-08

explanation is that sparse coding can achieve a near-optimal approximation of much complicated nonlinear relationship through local and piecewise linear...training examples, where x(i) ∈ RN is the ith example in the batch. Optionally, X can be normalized and whitened before sparse coding for better result...normalized input vectors are then ZCA- whitened [20]. Em- pirically, we choose ZCA- whitening over PCA- whitening , and there is no dimensionality reduction

Sparse seismic imaging using variable projection

NARCIS (Netherlands)

Aravkin, Aleksandr Y.; Tu, Ning; van Leeuwen, Tristan

2013-01-01

We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using

Tunable Sparse Network Coding for Multicast Networks

DEFF Research Database (Denmark)

Feizi, Soheil; Roetter, Daniel Enrique Lucani; Sørensen, Chres Wiant

2014-01-01

This paper shows the potential and key enabling mechanisms for tunable sparse network coding, a scheme in which the density of network coded packets varies during a transmission session. At the beginning of a transmission session, sparsely coded packets are transmitted, which benefits decoding...... complexity. At the end of a transmission, when receivers have accumulated degrees of freedom, coding density is increased. We propose a family of tunable sparse network codes (TSNCs) for multicast erasure networks with a controllable trade-off between completion time performance to decoding complexity...... a mechanism to perform efficient Gaussian elimination over sparse matrices going beyond belief propagation but maintaining low decoding complexity. Supporting simulation results are provided showing the trade-off between decoding complexity and completion time....

Sparse PCA with Oracle Property.

Science.gov (United States)

Gu, Quanquan; Wang, Zhaoran; Liu, Han

In this paper, we study the estimation of the k -dimensional sparse principal subspace of covariance matrix Σ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on the semidefinite relaxation of sparse PCA with novel regularizations. In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank- k , and attains a [Formula: see text] statistical rate of convergence with s being the subspace sparsity level and n the sample size. Compared to existing support recovery results for sparse PCA, our approach does not hinge on the spiked covariance model or the limited correlation condition. As a complement to the first estimator that enjoys the oracle property, we prove that, another estimator within the family achieves a sharper statistical rate of convergence than the standard semidefinite relaxation of sparse PCA, even when the previous assumption on the magnitude of the projection matrix is violated. We validate the theoretical results by numerical experiments on synthetic datasets.

Structural Sparse Tracking

KAUST Repository

Zhang, Tianzhu; Yang, Ming-Hsuan; Ahuja, Narendra; Ghanem, Bernard; Yan, Shuicheng; Xu, Changsheng; Liu, Si

2015-01-01

candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs

Technique detection software for Sparse Matrices

Directory of Open Access Journals (Sweden)

KHAN Muhammad Taimoor

2009-12-01

Full Text Available Sparse storage formats are techniques for storing and processing the sparse matrix data efficiently. The performance of these storage formats depend upon the distribution of non-zeros, within the matrix in different dimensions. In order to have better results we need a technique that suits best the organization of data in a particular matrix. So the decision of selecting a better technique is the main step towards improving the system's results otherwise the efficiency can be decreased. The purpose of this research is to help identify the best storage format in case of reduced storage size and high processing efficiency for a sparse matrix.

Sparse Representations of Hyperspectral Images

KAUST Repository

Swanson, Robin J.

2015-11-23

Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

Sparse Representations of Hyperspectral Images

KAUST Repository

Swanson, Robin J.

2015-01-01

Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

Supervised Convolutional Sparse Coding

KAUST Repository

Affara, Lama Ahmed

2018-04-08

Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

Structure-aware Local Sparse Coding for Visual Tracking

KAUST Repository

Qi, Yuankai

2018-01-24

Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years. Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images. The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited. To address this problem, we propose a structure-aware local sparse coding algorithm which encodes a target candidate using templates with both global and local sparsity constraints. For robust tracking, we show local regions of a candidate region should be encoded only with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we design an effective template update scheme. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against numerous stateof- the-art methods.

Decentralized modal identification using sparse blind source separation

International Nuclear Information System (INIS)

Sadhu, A; Hazra, B; Narasimhan, S; Pandey, M D

2011-01-01

Popular ambient vibration-based system identification methods process information collected from a dense array of sensors centrally to yield the modal properties. In such methods, the need for a centralized processing unit capable of satisfying large memory and processing demands is unavoidable. With the advent of wireless smart sensor networks, it is now possible to process information locally at the sensor level, instead. The information at the individual sensor level can then be concatenated to obtain the global structure characteristics. A novel decentralized algorithm based on wavelet transforms to infer global structure mode information using measurements obtained using a small group of sensors at a time is proposed in this paper. The focus of the paper is on algorithmic development, while the actual hardware and software implementation is not pursued here. The problem of identification is cast within the framework of under-determined blind source separation invoking transformations of measurements to the time–frequency domain resulting in a sparse representation. The partial mode shape coefficients so identified are then combined to yield complete modal information. The transformations are undertaken using stationary wavelet packet transform (SWPT), yielding a sparse representation in the wavelet domain. Principal component analysis (PCA) is then performed on the resulting wavelet coefficients, yielding the partial mixing matrix coefficients from a few measurement channels at a time. This process is repeated using measurements obtained from multiple sensor groups, and the results so obtained from each group are concatenated to obtain the global modal characteristics of the structure

Decentralized modal identification using sparse blind source separation

Science.gov (United States)

Sadhu, A.; Hazra, B.; Narasimhan, S.; Pandey, M. D.

2011-12-01

Popular ambient vibration-based system identification methods process information collected from a dense array of sensors centrally to yield the modal properties. In such methods, the need for a centralized processing unit capable of satisfying large memory and processing demands is unavoidable. With the advent of wireless smart sensor networks, it is now possible to process information locally at the sensor level, instead. The information at the individual sensor level can then be concatenated to obtain the global structure characteristics. A novel decentralized algorithm based on wavelet transforms to infer global structure mode information using measurements obtained using a small group of sensors at a time is proposed in this paper. The focus of the paper is on algorithmic development, while the actual hardware and software implementation is not pursued here. The problem of identification is cast within the framework of under-determined blind source separation invoking transformations of measurements to the time-frequency domain resulting in a sparse representation. The partial mode shape coefficients so identified are then combined to yield complete modal information. The transformations are undertaken using stationary wavelet packet transform (SWPT), yielding a sparse representation in the wavelet domain. Principal component analysis (PCA) is then performed on the resulting wavelet coefficients, yielding the partial mixing matrix coefficients from a few measurement channels at a time. This process is repeated using measurements obtained from multiple sensor groups, and the results so obtained from each group are concatenated to obtain the global modal characteristics of the structure.

Sparse Frequency Waveform Design for Radar-Embedded Communication

Directory of Open Access Journals (Sweden)

Chaoyun Mai

2016-01-01

Full Text Available According to the Tag application with function of covert communication, a method for sparse frequency waveform design based on radar-embedded communication is proposed. Firstly, sparse frequency waveforms are designed based on power spectral density fitting and quasi-Newton method. Secondly, the eigenvalue decomposition of the sparse frequency waveform sequence is used to get the dominant space. Finally the communication waveforms are designed through the projection of orthogonal pseudorandom vectors in the vertical subspace. Compared with the linear frequency modulation waveform, the sparse frequency waveform can further improve the bandwidth occupation of communication signals, thus achieving higher communication rate. A certain correlation exists between the reciprocally orthogonal communication signals samples and the sparse frequency waveform, which guarantees the low SER (signal error rate and LPI (low probability of intercept. The simulation results verify the effectiveness of this method.

Adaptive MANET Multipath Routing Algorithm Based on the Simulated Annealing Approach

Directory of Open Access Journals (Sweden)

Sungwook Kim

2014-01-01

Full Text Available Mobile ad hoc network represents a system of wireless mobile nodes that can freely and dynamically self-organize network topologies without any preexisting communication infrastructure. Due to characteristics like temporary topology and absence of centralized authority, routing is one of the major issues in ad hoc networks. In this paper, a new multipath routing scheme is proposed by employing simulated annealing approach. The proposed metaheuristic approach can achieve greater and reciprocal advantages in a hostile dynamic real world network situation. Therefore, the proposed routing scheme is a powerful method for finding an effective solution into the conflict mobile ad hoc network routing problem. Simulation results indicate that the proposed paradigm adapts best to the variation of dynamic network situations. The average remaining energy, network throughput, packet loss probability, and traffic load distribution are improved by about 10%, 10%, 5%, and 10%, respectively, more than the existing schemes.

Massive Asynchronous Parallelization of Sparse Matrix Factorizations

Energy Technology Data Exchange (ETDEWEB)

Chow, Edmond [Georgia Inst. of Technology, Atlanta, GA (United States)

2018-01-08

Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

Role of vascular potassium channels in the regulation of renal hemodynamics

DEFF Research Database (Denmark)

Sørensen, Charlotte Mehlin; Braunstein, Thomas Hartig; von Holstein-Rathlou, Niels-Henrik

2012-01-01

of one or more classes of K+ channels will lead to a change in hemodynamic resistance and therefore of renal blood flow and glomerular filtration pressure. Through these effects, the activity of renal vascular K+ channels influences renal salt and water excretion, fluid homeostasis, and ultimately blood...... pressure. Four main classes of K+ channels [calcium activated (KCa), inward rectifier (Kir), voltage activated (KV), and ATP sensitive (KATP)] are found in the renal vasculature. Several in vitro experiments have suggested a role for individual classes of K+ channels in the regulation of renal vascular...... function. Results from in vivo experiments are sparse. We discuss the role of the different classes of renal vascular K+ channels and their possible role in the integrated function of the renal microvasculature. Since several pathological conditions, among them hypertension, are associated with alterations...

Glistening-region model for multipath studies

Science.gov (United States)

Groves, Gordon W.; Chow, Winston C.

1998-07-01

The goal is to achieve a model of radar sea reflection with improved fidelity that is amenable to practical implementation. The geometry of reflection from a wavy surface is formulated. The sea surface is divided into two components: the smooth `chop' consisting of the longer wavelengths, and the `roughness' of the short wavelengths. Ordinary geometric reflection from the chop surface is broadened by the roughness. This same representation serves both for forward scatter and backscatter (sea clutter). The `Road-to-Happiness' approximation, in which the mean sea surface is assumed cylindrical, simplifies the reflection geometry for low-elevation targets. The effect of surface roughness is assumed to make the sea reflection coefficient depending on the `Deviation Angle' between the specular and the scattering directions. The `specular' direction is that into which energy would be reflected by a perfectly smooth facet. Assuming that the ocean waves are linear and random allows use of Gaussian statistics, greatly simplifying the formulation by allowing representation of the sea chop by three parameters. An approximation of `low waves' and retention of the sea-chop slope components only through second order provides further simplification. The simplifying assumptions make it possible to take the predicted 2D ocean wave spectrum into account in the calculation of sea-surface radar reflectivity, to provide algorithms for support of an operational system for dealing with target tracking in the presence of multipath. The product will be of use in simulated studies to evaluate different trade-offs in alternative tracking schemes, and will form the basis of a tactical system for ship defense against low flyers.

Underwater Time Service and Synchronization Based on Time Reversal Technique

Science.gov (United States)

Lu, Hao; Wang, Hai-bin; Aissa-El-Bey, Abdeldjalil; Pyndiah, Ramesh

2010-09-01

Real time service and synchronization are very important to many underwater systems. But the time service and synchronization in existence cannot work well due to the multi-path propagation and random phase fluctuation of signals in the ocean channel. The time reversal mirror technique can realize energy concentration through self-matching of the ocean channel and has very good spatial and temporal focusing properties. Based on the TRM technique, we present the Time Reversal Mirror Real Time service and synchronization (TRMRT) method which can bypass the processing of multi-path on the server side and reduce multi-path contamination on the client side. So TRMRT can improve the accuracy of time service. Furthermore, as an efficient and precise method of time service, TRMRT could be widely used in underwater exploration activities and underwater navigation and positioning systems.

Storage of sparse files using parallel log-structured file system

Science.gov (United States)

Bent, John M.; Faibish, Sorin; Grider, Gary; Torres, Aaron

2017-11-07

A sparse file is stored without holes by storing a data portion of the sparse file using a parallel log-structured file system; and generating an index entry for the data portion, the index entry comprising a logical offset, physical offset and length of the data portion. The holes can be restored to the sparse file upon a reading of the sparse file. The data portion can be stored at a logical end of the sparse file. Additional storage efficiency can optionally be achieved by (i) detecting a write pattern for a plurality of the data portions and generating a single patterned index entry for the plurality of the patterned data portions; and/or (ii) storing the patterned index entries for a plurality of the sparse files in a single directory, wherein each entry in the single directory comprises an identifier of a corresponding sparse file.

Sparse reconstruction using distribution agnostic bayesian matching pursuit

KAUST Repository

Masood, Mudassir

2013-11-01

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

Image understanding using sparse representations

CERN Document Server

Thiagarajan, Jayaraman J; Turaga, Pavan; Spanias, Andreas

2014-01-01

Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blin

Sparse regularization for force identification using dictionaries

Science.gov (United States)

Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng

2016-04-01

The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.

Avoiding Biased-Feeding in the Scheduling of Collaborative Multipath TCP.

Directory of Open Access Journals (Sweden)

Meng-Hsun Tsai

Full Text Available Smartphones have become the major communication and portable computing devices that access the Internet through Wi-Fi or mobile networks. Unfortunately, users without a mobile data subscription can only access the Internet at limited locations, such as hotspots. In this paper, we propose a collaborative bandwidth sharing protocol (CBSP built on top of MultiPath TCP (MPTCP. CBSP enables users to buy bandwidth on demand from neighbors (called Helpers and uses virtual interfaces to bind the subflows of MPTCP to avoid modifying the implementation of MPTCP. However, although MPTCP provides the required multi-homing functionality for bandwidth sharing, the current packet scheduling in collaborative MPTCP (e.g., Co-MPTCP leads to the so-called biased-feeding problem. In this problem, the fastest link might always be selected to send packets whenever it has available cwnd, which results in other links not being fully utilized. In this work, we set out to design an algorithm, called Scheduled Window-based Transmission Control (SWTC, to improve the performance of packet scheduling in MPTCP, and we perform extensive simulations to evaluate its performance.

Avoiding Biased-Feeding in the Scheduling of Collaborative Multipath TCP.

Science.gov (United States)

Tsai, Meng-Hsun; Chou, Chien-Ming; Lan, Kun-Chan

2016-01-01

Smartphones have become the major communication and portable computing devices that access the Internet through Wi-Fi or mobile networks. Unfortunately, users without a mobile data subscription can only access the Internet at limited locations, such as hotspots. In this paper, we propose a collaborative bandwidth sharing protocol (CBSP) built on top of MultiPath TCP (MPTCP). CBSP enables users to buy bandwidth on demand from neighbors (called Helpers) and uses virtual interfaces to bind the subflows of MPTCP to avoid modifying the implementation of MPTCP. However, although MPTCP provides the required multi-homing functionality for bandwidth sharing, the current packet scheduling in collaborative MPTCP (e.g., Co-MPTCP) leads to the so-called biased-feeding problem. In this problem, the fastest link might always be selected to send packets whenever it has available cwnd, which results in other links not being fully utilized. In this work, we set out to design an algorithm, called Scheduled Window-based Transmission Control (SWTC), to improve the performance of packet scheduling in MPTCP, and we perform extensive simulations to evaluate its performance.

Sparse inpainting and isotropy

Energy Technology Data Exchange (ETDEWEB)

Feeney, Stephen M.; McEwen, Jason D.; Peiris, Hiranya V. [Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT (United Kingdom); Marinucci, Domenico; Cammarota, Valentina [Department of Mathematics, University of Rome Tor Vergata, via della Ricerca Scientifica 1, Roma, 00133 (Italy); Wandelt, Benjamin D., E-mail: s.feeney@imperial.ac.uk, E-mail: marinucc@axp.mat.uniroma2.it, E-mail: jason.mcewen@ucl.ac.uk, E-mail: h.peiris@ucl.ac.uk, E-mail: wandelt@iap.fr, E-mail: cammarot@axp.mat.uniroma2.it [Kavli Institute for Theoretical Physics, Kohn Hall, University of California, 552 University Road, Santa Barbara, CA, 93106 (United States)

2014-01-01

Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.

Multi-carrier Communications over Time-varying Acoustic Channels

Science.gov (United States)

Aval, Yashar M.

Acoustic communication is an enabling technology for many autonomous undersea systems, such as those used for ocean monitoring, offshore oil and gas industry, aquaculture, or port security. There are three main challenges in achieving reliable high-rate underwater communication: the bandwidth of acoustic channels is extremely limited, the propagation delays are long, and the Doppler distortions are more pronounced than those found in wireless radio channels. In this dissertation we focus on assessing the fundamental limitations of acoustic communication, and designing efficient signal processing methods that cam overcome these limitations. We address the fundamental question of acoustic channel capacity (achievable rate) for single-input-multi-output (SIMO) acoustic channels using a per-path Rician fading model, and focusing on two scenarios: narrowband channels where the channel statistics can be approximated as frequency- independent, and wideband channels where the nominal path loss is frequency-dependent. In each scenario, we compare several candidate power allocation techniques, and show that assigning uniform power across all frequencies for the first scenario, and assigning uniform power across a selected frequency-band for the second scenario, are the best practical choices in most cases, because the long propagation delay renders the feedback information outdated for power allocation based on the estimated channel response. We quantify our results using the channel information extracted form the 2010 Mobile Acoustic Communications Experiment (MACE'10). Next, we focus on achieving reliable high-rate communication over underwater acoustic channels. Specifically, we investigate orthogonal frequency division multiplexing (OFDM) as the state-of-the-art technique for dealing with frequency-selective multipath channels, and propose a class of methods that compensate for the time-variation of the underwater acoustic channel. These methods are based on multiple

Object tracking by occlusion detection via structured sparse learning

KAUST Repository

Zhang, Tianzhu

2013-06-01

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object\\'s track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.

Sparse Vector Distributions and Recovery from Compressed Sensing

DEFF Research Database (Denmark)

Sturm, Bob L.

It is well known that the performance of sparse vector recovery algorithms from compressive measurements can depend on the distribution underlying the non-zero elements of a sparse vector. However, the extent of these effects has yet to be explored, and formally presented. In this paper, I...... empirically investigate this dependence for seven distributions and fifteen recovery algorithms. The two morals of this work are: 1) any judgement of the recovery performance of one algorithm over that of another must be prefaced by the conditions for which this is observed to be true, including sparse vector...... distributions, and the criterion for exact recovery; and 2) a recovery algorithm must be selected carefully based on what distribution one expects to underlie the sensed sparse signal....

Exhaustive Search for Sparse Variable Selection in Linear Regression

Science.gov (United States)

Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato

2018-04-01

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

Secrecy Capacity Analysis over α−μ Fading Channels

KAUST Repository

Lei, Hongjiang

2017-02-15

In this work, we study the secrecy capacity of the classic Wyner’s model over the α − μ fading channels, where α and μ specify the nonlinearity and clustering of fading channels, respectively. The average secrecy capacity (ASC) is derived in closed-form by using the extended generalized bivariate Fox’s Hfunction (EGBFHF). Moreover, the asymptotic analysis of ASC in high signal-to-noise ratio (SNR) regime is conducted. The asymptotic results unveil that the ASC follows the scaling law of Θ(ln p), where p stands for the ratio between the average powers of main channels and eavesdropping channels. Moreover, the ASC can be enhanced by increasing the transmit SNR, while there exists a ceiling of ASC as the SNRs at both sides are improved simultaneously. The accuracy of the analytical results is validated by Monte-Carlo simulations. The numerical results show that rigorous fading channels are beneficial to the secrecy performance, that is, serious nonlinearity (small α) and sparse clustering (small μ) will lead to the improvement of ASC.

Secrecy Capacity Analysis over α−μ Fading Channels

KAUST Repository

Lei, Hongjiang; Ansari, Imran Shafique; Pan, Gaofeng; Alomair, Basel; Alouini, Mohamed-Slim

2017-01-01

In this work, we study the secrecy capacity of the classic Wyner’s model over the α − μ fading channels, where α and μ specify the nonlinearity and clustering of fading channels, respectively. The average secrecy capacity (ASC) is derived in closed-form by using the extended generalized bivariate Fox’s Hfunction (EGBFHF). Moreover, the asymptotic analysis of ASC in high signal-to-noise ratio (SNR) regime is conducted. The asymptotic results unveil that the ASC follows the scaling law of Θ(ln p), where p stands for the ratio between the average powers of main channels and eavesdropping channels. Moreover, the ASC can be enhanced by increasing the transmit SNR, while there exists a ceiling of ASC as the SNRs at both sides are improved simultaneously. The accuracy of the analytical results is validated by Monte-Carlo simulations. The numerical results show that rigorous fading channels are beneficial to the secrecy performance, that is, serious nonlinearity (small α) and sparse clustering (small μ) will lead to the improvement of ASC.

A Sparse Approximate Inverse Preconditioner for Nonsymmetric Linear Systems

Czech Academy of Sciences Publication Activity Database

Benzi, M.; Tůma, Miroslav

1998-01-01

Roč. 19, č. 3 (1998), s. 968-994 ISSN 1064-8275 R&D Projects: GA ČR GA201/93/0067; GA AV ČR IAA230401 Keywords : large sparse systems * interative methods * preconditioning * approximate inverse * sparse linear systems * sparse matrices * incomplete factorizations * conjugate gradient -type methods Subject RIV: BA - General Mathematics Impact factor: 1.378, year: 1998

Sliding-MOMP Based Channel Estimation Scheme for ISDB-T Systems

Directory of Open Access Journals (Sweden)

Ziji Ma

2016-01-01

Full Text Available Compressive sensing based channel estimation has shown its advantage of accurate reconstruction for sparse signal with less pilots for OFDM systems. However, high computational cost requirement of CS method, due to linear programming, significantly restricts its implementation in practical applications. In this paper, we propose a reduced complexity channel estimation scheme of modified orthogonal matching pursuit with sliding windows for ISDB-T (Integrated Services Digital Broadcasting for Terrestrial system. The proposed scheme can reduce the computational cost by limiting the searching region as well as making effective use of the last estimation result. In addition, adaptive tracking strategy with sliding sampling window can improve the robustness of CS based methods to guarantee its accuracy of channel matrix reconstruction, even for fast time-variant channels. The computer simulation demonstrates its impact on improving bit error rate and computational complexity for ISDB-T system.

A Comprehensive Investigation of Advanced Range Telemetry

National Research Council Canada - National Science Library

Rice, Michael

1999-01-01

Data from ARTM channel sounding flights has been analyzed. A three-path model consisting of a line-of-sight path and two reflected paths adequately captures all the essential multipath features of the aeronautical telemetry channel...

Structure-based bayesian sparse reconstruction

KAUST Repository

Quadeer, Ahmed Abdul

2012-12-01

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.

Optimal training sequences for indoor wireless optical communications

International Nuclear Information System (INIS)

Wang, Jun-Bo; Jiao, Yuan; Song, Xiaoyu; Chen, Ming

2012-01-01

Since indoor wireless optical communication (WOC) systems can offer several potential advantages over their radio frequency counterparts, there has been a growing interest in indoor WOC systems. Influenced by the complicated optical propagation environment, there exist multipath propagation phenomena. In order to eliminate the effect of multipath propagation, much attention should be concentrated on the channel estimation in indoor WOC systems. This paper investigates optimal training sequences (TSs) for estimating a channel impulse response in indoor WOC systems. Based on the Cramer–Rao bound (CRB) theorem, an explicit form of search criterion is found. Optimum TSs are obtained and tabulated by computer search for different channel responses and TS lengths. Measured by mean square error (MSE) performance, channel estimation errors are also investigated. Simulation results show that the MSE of the channel estimator at the receiver can be reduced significantly by using the optimized TS set. Moreover, the longer the TS, the better the MSE performance that can be obtained when the channel order is fixed. (paper)

Greedy vs. L1 convex optimization in sparse coding

DEFF Research Database (Denmark)

Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

2015-01-01

Sparse representation has been applied successfully in many image analysis applications, including abnormal event detection, in which a baseline is to learn a dictionary from the training data and detect anomalies from its sparse codes. During this procedure, sparse codes which can be achieved...... solutions. Considering the property of abnormal event detection, i.e., only normal videos are used as training data due to practical reasons, effective codes in classification application may not perform well in abnormality detection. Therefore, we compare the sparse codes and comprehensively evaluate...... their performance from various aspects to better understand their applicability, including computation time, reconstruction error, sparsity, detection...

A Secure Cluster-Based Multipath Routing Protocol for WMSNs

Directory of Open Access Journals (Sweden)

Jamal N. Al-Karaki

2011-04-01

Full Text Available The new characteristics of Wireless Multimedia Sensor Network (WMSN and its design issues brought by handling different traffic classes of multimedia content (video streams, audio, and still images as well as scalar data over the network, make the proposed routing protocols for typical WSNs not directly applicable for WMSNs. Handling real-time multimedia data requires both energy efficiency and QoS assurance in order to ensure efficient utility of different capabilities of sensor resources and correct delivery of collected information. In this paper, we propose a Secure Cluster-based Multipath Routing protocol for WMSNs, SCMR, to satisfy the requirements of delivering different data types and support high data rate multimedia traffic. SCMR exploits the hierarchical structure of powerful cluster heads and the optimized multiple paths to support timeliness and reliable high data rate multimedia communication with minimum energy dissipation. Also, we present a light-weight distributed security mechanism of key management in order to secure the communication between sensor nodes and protect the network against different types of attacks. Performance evaluation from simulation results demonstrates a significant performance improvement comparing with existing protocols (which do not even provide any kind of security feature in terms of average end-to-end delay, network throughput, packet delivery ratio, and energy consumption.

A sparse matrix based full-configuration interaction algorithm

International Nuclear Information System (INIS)

Rolik, Zoltan; Szabados, Agnes; Surjan, Peter R.

2008-01-01

We present an algorithm related to the full-configuration interaction (FCI) method that makes complete use of the sparse nature of the coefficient vector representing the many-electron wave function in a determinantal basis. Main achievements of the presented sparse FCI (SFCI) algorithm are (i) development of an iteration procedure that avoids the storage of FCI size vectors; (ii) development of an efficient algorithm to evaluate the effect of the Hamiltonian when both the initial and the product vectors are sparse. As a result of point (i) large disk operations can be skipped which otherwise may be a bottleneck of the procedure. At point (ii) we progress by adopting the implementation of the linear transformation by Olsen et al. [J. Chem Phys. 89, 2185 (1988)] for the sparse case, getting the algorithm applicable to larger systems and faster at the same time. The error of a SFCI calculation depends only on the dropout thresholds for the sparse vectors, and can be tuned by controlling the amount of system memory passed to the procedure. The algorithm permits to perform FCI calculations on single node workstations for systems previously accessible only by supercomputers

An in-depth study of sparse codes on abnormality detection

DEFF Research Database (Denmark)

Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

2016-01-01

Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality...... are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. The experiment results show that combining OMP codes with maximum coordinate...

Sparse Principal Component Analysis in Medical Shape Modeling

DEFF Research Database (Denmark)

Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus

2006-01-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...

Sparse reconstruction using distribution agnostic bayesian matching pursuit

KAUST Repository

Masood, Mudassir; Al-Naffouri, Tareq Y.

2013-01-01

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics

Wavelet based multicarrier code division multiple access ...

African Journals Online (AJOL)

This paper presents the study on Wavelet transform based Multicarrier Code Division Multiple Access (MC-CDMA) system for a downlink wireless channel. The performance of the system is studied for Additive White Gaussian Noise Channel (AWGN) and slowly varying multipath channels. The bit error rate (BER) versus ...

User's Manual for PCSMS (Parallel Complex Sparse Matrix Solver). Version 1.

Science.gov (United States)

Reddy, C. J.

2000-01-01

PCSMS (Parallel Complex Sparse Matrix Solver) is a computer code written to make use of the existing real sparse direct solvers to solve complex, sparse matrix linear equations. PCSMS converts complex matrices into real matrices and use real, sparse direct matrix solvers to factor and solve the real matrices. The solution vector is reconverted to complex numbers. Though, this utility is written for Silicon Graphics (SGI) real sparse matrix solution routines, it is general in nature and can be easily modified to work with any real sparse matrix solver. The User's Manual is written to make the user acquainted with the installation and operation of the code. Driver routines are given to aid the users to integrate PCSMS routines in their own codes.

Parallel transposition of sparse data structures

DEFF Research Database (Denmark)

Wang, Hao; Liu, Weifeng; Hou, Kaixi

2016-01-01

Many applications in computational sciences and social sciences exploit sparsity and connectivity of acquired data. Even though many parallel sparse primitives such as sparse matrix-vector (SpMV) multiplication have been extensively studied, some other important building blocks, e.g., parallel tr...... transposition in the latest vendor-supplied library on an Intel multicore CPU platform, and the MergeTrans approach achieves on average of 3.4-fold (up to 11.7-fold) speedup on an Intel Xeon Phi many-core processor....

Analysis of F-Canyon Effluents During the Dissolution Cycle with a Fourier Transform Infrared Spectrometer/Multipath Cell

International Nuclear Information System (INIS)

Villa, E.

1999-01-01

Air samples from F-Canyon effluents were collected at the F-Canyon stack and transported to a laboratory at the Savannah River Technology Center (SRTC) for analysis using a Fourier transform infrared spectrometer in conjunction with a multipath cell. Air samples were collected during the decladding and acid cuts of the dissolution of the irradiated aluminum-cladded slugs. The FTIR analyses of the air samples show the presence of NO2, NO, HNO2, N2O, SF6, and 85Kr during the dissolution cycle. The concentration time profiles of these effluents corresponded with expected release rates from the F-Canyon operations

Numerical solution of large sparse linear systems

International Nuclear Information System (INIS)

Meurant, Gerard; Golub, Gene.

1982-02-01

This note is based on one of the lectures given at the 1980 CEA-EDF-INRIA Numerical Analysis Summer School whose aim is the study of large sparse linear systems. The main topics are solving least squares problems by orthogonal transformation, fast Poisson solvers and solution of sparse linear system by iterative methods with a special emphasis on preconditioned conjuguate gradient method [fr

Performance analysis of a finite radon transform in OFDM system under different channel models

Energy Technology Data Exchange (ETDEWEB)

Dawood, Sameer A.; Anuar, M. S.; Fayadh, Rashid A. [School of Computer and Communication Engineering, Universiti Malaysia Perlis (UniMAP) Pauh Putra, 02000 Arau, Parlis (Malaysia); Malek, F.; Abdullah, Farrah Salwani [School of Electrical System Engineering, Universiti Malaysia Perlis (UniMAP) Pauh Putra, 02000 Arau, Parlis (Malaysia)

2015-05-15

In this paper, a class of discrete Radon transforms namely Finite Radon Transform (FRAT) was proposed as a modulation technique in the realization of Orthogonal Frequency Division Multiplexing (OFDM). The proposed FRAT operates as a data mapper in the OFDM transceiver instead of the conventional phase shift mapping and quadrature amplitude mapping that are usually used with the standard OFDM based on Fast Fourier Transform (FFT), by the way that ensure increasing the orthogonality of the system. The Fourier domain approach was found here to be the more suitable way for obtaining the forward and inverse FRAT. This structure resulted in a more suitable realization of conventional FFT- OFDM. It was shown that this application increases the orthogonality significantly in this case due to the use of Inverse Fast Fourier Transform (IFFT) twice, namely, in the data mapping and in the sub-carrier modulation also due to the use of an efficient algorithm in determining the FRAT coefficients called the optimal ordering method. The proposed approach was tested and compared with conventional OFDM, for additive white Gaussian noise (AWGN) channel, flat fading channel, and multi-path frequency selective fading channel. The obtained results showed that the proposed system has improved the bit error rate (BER) performance by reducing inter-symbol interference (ISI) and inter-carrier interference (ICI), comparing with conventional OFDM system.

Performance analysis of a finite radon transform in OFDM system under different channel models

International Nuclear Information System (INIS)

Dawood, Sameer A.; Anuar, M. S.; Fayadh, Rashid A.; Malek, F.; Abdullah, Farrah Salwani

2015-01-01

In this paper, a class of discrete Radon transforms namely Finite Radon Transform (FRAT) was proposed as a modulation technique in the realization of Orthogonal Frequency Division Multiplexing (OFDM). The proposed FRAT operates as a data mapper in the OFDM transceiver instead of the conventional phase shift mapping and quadrature amplitude mapping that are usually used with the standard OFDM based on Fast Fourier Transform (FFT), by the way that ensure increasing the orthogonality of the system. The Fourier domain approach was found here to be the more suitable way for obtaining the forward and inverse FRAT. This structure resulted in a more suitable realization of conventional FFT- OFDM. It was shown that this application increases the orthogonality significantly in this case due to the use of Inverse Fast Fourier Transform (IFFT) twice, namely, in the data mapping and in the sub-carrier modulation also due to the use of an efficient algorithm in determining the FRAT coefficients called the optimal ordering method. The proposed approach was tested and compared with conventional OFDM, for additive white Gaussian noise (AWGN) channel, flat fading channel, and multi-path frequency selective fading channel. The obtained results showed that the proposed system has improved the bit error rate (BER) performance by reducing inter-symbol interference (ISI) and inter-carrier interference (ICI), comparing with conventional OFDM system

Sparse Source EEG Imaging with the Variational Garrote

DEFF Research Database (Denmark)

Hansen, Sofie Therese; Stahlhut, Carsten; Hansen, Lars Kai

2013-01-01

EEG imaging, the estimation of the cortical source distribution from scalp electrode measurements, poses an extremely ill-posed inverse problem. Recent work by Delorme et al. (2012) supports the hypothesis that distributed source solutions are sparse. We show that direct search for sparse solutions...

Experimental study of the helicopter-mobile radioelectrical channel and possible extension to the satellite-mobile channel

Science.gov (United States)

Blanchetiere-Ciarletti, V.; Sylvain, M.; Lemenn, P.

1994-07-01

The use of satellite seems to be an answer to the radioelectrical covering problem for the mobile communications, particularly in the low populated areas. Frequency bands at 1.5 and 2.5 GHz have been dedicated to these future services. Satellite-mobile links will be much more affected by propagation phenomena than the existing links between satellites and fixed stations. The reasons for that are twofold: The probable use of LEO (Low-Earth-Orbit) satellites instead of GEO; such satellites will have to be received at relatively low elevation to limit their number; the use of mobile communication terminals with small and non directive antennas that must work in various environments instead of terrestrian stations located at carefully chosen places and equipped with large diameter paraboloids. These propagation phenomena mainly consist in the fading of the signal level (shadowing of the link), and a frequency selective fading due to multipath propagation. The experience run by C.R.P.E. is aimed at a better understanding of the satellite-mobile propagation channel at fixed frequency as well as on a large band. In this paper, we discuss preliminary results from a series of propagation measurements performed (by lack of any experimental satellite) on an experimental radio link at 1.45 GHz on a of 20 MHz bandwidth between a helicopter flying at a height of 2 km and a mobile receiver. The whole experiment has been run in a rural environment in Brittany (France). In a first part, we illustrate the quality of the data collected during the experiment on a typical case study and give a possible physical interpretation of the observed phenomena. Then we present statistical results concerning the various characteristics (attenuation and delay spreads) of the propagation channel. Finally, we discuss the problem of using a helicopter (flying at a height of 2 km) as a substitute for a satellite at about 1000 km and try to estimate to what extent it is possible to use the data

Low-count PET image restoration using sparse representation

Science.gov (United States)

Li, Tao; Jiang, Changhui; Gao, Juan; Yang, Yongfeng; Liang, Dong; Liu, Xin; Zheng, Hairong; Hu, Zhanli

2018-04-01

In the field of positron emission tomography (PET), reconstructed images are often blurry and contain noise. These problems are primarily caused by the low resolution of projection data. Solving this problem by improving hardware is an expensive solution, and therefore, we attempted to develop a solution based on optimizing several related algorithms in both the reconstruction and image post-processing domains. As sparse technology is widely used, sparse prediction is increasingly applied to solve this problem. In this paper, we propose a new sparse method to process low-resolution PET images. Two dictionaries (D1 for low-resolution PET images and D2 for high-resolution PET images) are learned from a group real PET image data sets. Among these two dictionaries, D1 is used to obtain a sparse representation for each patch of the input PET image. Then, a high-resolution PET image is generated from this sparse representation using D2. Experimental results indicate that the proposed method exhibits a stable and superior ability to enhance image resolution and recover image details. Quantitatively, this method achieves better performance than traditional methods. This proposed strategy is a new and efficient approach for improving the quality of PET images.

X-ray computed tomography using curvelet sparse regularization.

Science.gov (United States)

Wieczorek, Matthias; Frikel, Jürgen; Vogel, Jakob; Eggl, Elena; Kopp, Felix; Noël, Peter B; Pfeiffer, Franz; Demaret, Laurent; Lasser, Tobias

2015-04-01

Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

Aspheric lens based imaging receiver for MIMO visible light communication

Science.gov (United States)

Ju, Qiuqi; Liang, Zhongcheng; Liu, Xueming; Yang, Tingting; Wang, Jin

2014-10-01

Visible light communication (VLC) has been regarded as a promising solution in short-range intelligent communication system. Nowadays, the research is focused on integrating the multi-input multi-output (MIMO) technique in the VLC system, to achieve a larger transmission capacity and stronger transmission reliability. However, one important issue should be addressed due to the use of MIMO technology: the multipath inter-symbol interference. The multipath intersymbol interference comes from the reflection of the signal in the room and channel crosstalk between different channels. In this paper, we propose a novel optical system used in the MIMO VLC system to reduce multipath interference dramatically. Signals from different LEDs can be separated by using parabolic lens plated with reflecting film. This structure can reduce the reflection effect effectively as well. We present the simulation results to observe the distribution of optical power on the imaging plane for various receiving positions and low correlation between all channels. We can find that the optical power density becomes stronger than non-imaging system and the interference is sharply decreased, thus the SNR and BER are also optimized. Analysis about the optical system is given in this paper.

On the Geometric Modeling of the Uplink Channel in a Cellular System

Directory of Open Access Journals (Sweden)

K. B. Baltzis

2008-01-01

Full Text Available To meet the challenges of present and future wireless communications realistic propagation models that consider both spatial and temporal channel characteristics are used. However, the complexity of the complete characterization of the wireless medium has pointed out the importance of approximate but simple approaches. The geometrically based methods are typical examples of low–complexity but adequate solutions. Geometric modeling idealizes the aforementioned wireless propagation environment via a geometric abstraction of the spatial relationships among the transmitter, the receiver, and the scatterers. The paper tries to present an efficient way to simulate mobile channels using geometrical–based stochastic scattering models. In parallel with an overview of the most commonly used propagation models, the basic principles of the method as well the main assumptions made are presented. The study is focused on three well–known proposals used for the description of the Angle–of –Arrival and Time–of–Arrival statistics of the incoming multipaths in the uplink of a cellular communication system. In order to demonstrate the characteristics of these models illustrative examples are given. The physical mechanism and motivations behind them are also included providing us with a better understanding of the physical insight of the propagation medium.

Sparse dictionary for synthetic transmit aperture medical ultrasound imaging.

Science.gov (United States)

Wang, Ping; Jiang, Jin-Yang; Li, Na; Luo, Han-Wu; Li, Fang; Cui, Shi-Gang

2017-07-01

It is possible to recover a signal below the Nyquist sampling limit using a compressive sensing technique in ultrasound imaging. However, the reconstruction enabled by common sparse transform approaches does not achieve satisfactory results. Considering the ultrasound echo signal's features of attenuation, repetition, and superposition, a sparse dictionary with the emission pulse signal is proposed. Sparse coefficients in the proposed dictionary have high sparsity. Images reconstructed with this dictionary were compared with those obtained with the three other common transforms, namely, discrete Fourier transform, discrete cosine transform, and discrete wavelet transform. The performance of the proposed dictionary was analyzed via a simulation and experimental data. The mean absolute error (MAE) was used to quantify the quality of the reconstructions. Experimental results indicate that the MAE associated with the proposed dictionary was always the smallest, the reconstruction time required was the shortest, and the lateral resolution and contrast of the reconstructed images were also the closest to the original images. The proposed sparse dictionary performed better than the other three sparse transforms. With the same sampling rate, the proposed dictionary achieved excellent reconstruction quality.

A sparse electromagnetic imaging scheme using nonlinear landweber iterations

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2015-01-01

Development and use of electromagnetic inverse scattering techniques for imagining sparse domains have been on the rise following the recent advancements in solving sparse optimization problems. Existing techniques rely on iteratively converting

Scalable group level probabilistic sparse factor analysis

DEFF Research Database (Denmark)

Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard

2017-01-01

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...

Fast wavelet based sparse approximate inverse preconditioner

Energy Technology Data Exchange (ETDEWEB)

Wan, W.L. [Univ. of California, Los Angeles, CA (United States)

1996-12-31

Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.

Local posterior concentration rate for multilevel sparse sequences

NARCIS (Netherlands)

Belitser, E.N.; Nurushev, N.

2017-01-01

We consider empirical Bayesian inference in the many normal means model in the situation when the high-dimensional mean vector is multilevel sparse, that is,most of the entries of the parameter vector are some fixed values. For instance, the traditional sparse signal is a particular case (with one

Sparse modeling of spatial environmental variables associated with asthma.

Science.gov (United States)

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Analog system for computing sparse codes

Science.gov (United States)

Rozell, Christopher John; Johnson, Don Herrick; Baraniuk, Richard Gordon; Olshausen, Bruno A.; Ortman, Robert Lowell

2010-08-24

A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.

Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids

KAUST Repository

Buse, Gerrit; Pflü ger, Dirk; Jacob, Riko

2014-01-01

In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated

Spatial Characterization of Radio Propagation Channel in Urban Vehicle-to-Infrastructure Environments to Support WSNs Deployment

Directory of Open Access Journals (Sweden)

Fausto Granda

2017-06-01

Full Text Available Vehicular ad hoc Networks (VANETs enable vehicles to communicate with each other as well as with roadside units (RSUs. Although there is a significant research effort in radio channel modeling focused on vehicle-to-vehicle (V2V, not much work has been done for vehicle-to-infrastructure (V2I using 3D ray-tracing tools. This work evaluates some important parameters of a V2I wireless channel link such as large-scale path loss and multipath metrics in a typical urban scenario using a deterministic simulation model based on an in-house 3D Ray-Launching (3D-RL algorithm at 5.9 GHz. Results show the high impact that the spatial distance; link frequency; placement of RSUs; and factors such as roundabout, geometry and relative position of the obstacles have in V2I propagation channel. A detailed spatial path loss characterization of the V2I channel along the streets and avenues is presented. The 3D-RL results show high accuracy when compared with measurements, and represent more reliably the propagation phenomena when compared with analytical path loss models. Performance metrics for a real test scenario implemented with a VANET wireless sensor network implemented ad-hoc are also described. These results constitute a starting point in the design phase of Wireless Sensor Networks (WSNs radio-planning in the urban V2I deployment in terms of coverage.

Energy Efficient Error-Correcting Coding for Wireless Systems

NARCIS (Netherlands)

Shao, X.

2010-01-01

The wireless channel is a hostile environment. The transmitted signal does not only suffers multi-path fading but also noise and interference from other users of the wireless channel. That causes unreliable communications. To achieve high-quality communications, error correcting coding is required

A Low-Complexity Joint Synchronization and Detection Algorithm for Single-Band DS-CDMA UWB Communications

DEFF Research Database (Denmark)

Christensen, Lars P.B.

2005-01-01

The problem of asynchronous direct-sequence code division multiple access (DS-CDMA) detection over the ultra-wideband (UWB) multipath channel is considered. A joint synchronization, channel-estimation and multi-user detection scheme based on the adaptive linear minimum mean-square error (LMMSE...

Occlusion detection via structured sparse learning for robust object tracking

KAUST Repository

Zhang, Tianzhu

2014-01-01

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios, these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object’s track. This is the case when significant occlusion occurs. To accommodate for nonsparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our proposed tracker consistently outperforms the state-of-the-art trackers.

Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle

Directory of Open Access Journals (Sweden)

Xiangwei Xing

2014-01-01

Full Text Available As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC has attracted much attention in synthetic aperture radar (SAR automatic target recognition (ATR recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA, in which the correlation between the vehicle’s aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle’s aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

Learning sparse generative models of audiovisual signals

OpenAIRE

Monaci, Gianluca; Sommer, Friedrich T.; Vandergheynst, Pierre

2008-01-01

This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that can be positioned independently and arbitrarily in space and time. We design an algorithm capable of learning sets of such audiovi- sual, synchronous, shift-invariant functions by alternatingly solving a coding and a learning pr...

Analysis of synchronous digital-modulation schemes for satellite communication

Science.gov (United States)

Takhar, G. S.; Gupta, S. C.

1975-01-01

The multipath communication channel for space communications is modeled as a multiplicative channel. This paper discusses the effects of multiplicative channel processes on the symbol error rate for quadrature modulation (QM) digital modulation schemes. An expression for the upper bound on the probability of error is derived and numerically evaluated. The results are compared with those obtained for additive channels.

Support agnostic Bayesian matching pursuit for block sparse signals

KAUST Repository

Masood, Mudassir

2013-05-01

A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla

2014-05-04

Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla

2014-01-06

Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays

Science.gov (United States)

Kwon, Daniel W.; Miller, David W.; Sedwick, Raymond J.

2004-01-01

Traditional methods of actuating spacecraft in sparse aperture arrays use propellant as a reaction mass. For formation flying systems, propellant becomes a critical consumable which can be quickly exhausted while maintaining relative orientation. Additional problems posed by propellant include optical contamination, plume impingement, thermal emission, and vibration excitation. For these missions where control of relative degrees of freedom is important, we consider using a system of electromagnets, in concert with reaction wheels, to replace the consumables. Electromagnetic Formation Flight sparse apertures, powered by solar energy, are designed differently from traditional propulsion systems, which are based on V. This paper investigates the design of sparse apertures both inside and outside the Earth's gravity field.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2014-01-01

Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

A comprehensive study of sparse codes on abnormality detection

DEFF Research Database (Denmark)

Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

2017-01-01

Sparse representation has been applied successfully in abnor-mal event detection, in which the baseline is to learn a dic-tionary accompanied by sparse codes. While much empha-sis is put on discriminative dictionary construction, there areno comparative studies of sparse codes regarding abnormal-ity...... detection. We comprehensively study two types of sparsecodes solutions - greedy algorithms and convex L1-norm so-lutions - and their impact on abnormality detection perfor-mance. We also propose our framework of combining sparsecodes with different detection methods. Our comparative ex-periments are carried...

Support agnostic Bayesian matching pursuit for block sparse signals

KAUST Repository

Masood, Mudassir; Al-Naffouri, Tareq Y.

2013-01-01

priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal

Efficient Sequence Detection of Multicarrier Transmissions over Doubly Dispersive Channels

Directory of Open Access Journals (Sweden)

Hwang Sung-Jun

2006-01-01

Full Text Available We propose a high-spectral-efficiency multicarrier system for communication over the doubly dispersive (DD channel which yields very low frame error rate (FER, with quadratic (in the frame length receiver complexity. To accomplish this, we combine a non-(biorthogonal multicarrier modulation (MCM scheme recently proposed by the authors with novel sequence detection (SD and channel estimation (CE algorithms. In particular, our MCM scheme allows us to accurately represent the DD channels otherwise complicated intercarrier interference (ICI and intersymbol interference (ISI response with a relatively small number of coefficients. The SD and CE algorithms then leverage this sparse ICI/ISI structure for low-complexity operation. Our SD algorithm combines a novel adaptive breadth-first search procedure with a new fast MMSE-GDFE preprocessor, while our CE algorithm uses a rank-reduced pilot-aided Wiener technique to estimate only the significant ICI/ISI coefficients.

Selectivity and sparseness in randomly connected balanced networks.

Directory of Open Access Journals (Sweden)

Cengiz Pehlevan

Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS

KAUST Repository

Desmal, Abdulla

2015-07-29

A scheme for efficiently solving the nonlinear electromagnetic inverse scattering problem on sparse investigation domains is described. The proposed scheme reconstructs the (complex) dielectric permittivity of an investigation domain from fields measured away from the domain itself. Least-squares data misfit between the computed scattered fields, which are expressed as a nonlinear function of the permittivity, and the measured fields is constrained by the L0/L1-norm of the solution. The resulting minimization problem is solved using nonlinear Landweber iterations, where at each iteration a thresholding function is applied to enforce the sparseness-promoting L0/L1-norm constraint. The thresholded nonlinear Landweber iterations are applied to several two-dimensional problems, where the ``measured\\'\\' fields are synthetically generated or obtained from actual experiments. These numerical experiments demonstrate the accuracy, efficiency, and applicability of the proposed scheme in reconstructing sparse profiles with high permittivity values.

Statistical multi-path exposure method for assessing the whole-body SAR in a heterogeneous human body model in a realistic environment.

Science.gov (United States)

Vermeeren, Günter; Joseph, Wout; Martens, Luc

2013-04-01

Assessing the whole-body absorption in a human in a realistic environment requires a statistical approach covering all possible exposure situations. This article describes the development of a statistical multi-path exposure method for heterogeneous realistic human body models. The method is applied for the 6-year-old Virtual Family boy (VFB) exposed to the GSM downlink at 950 MHz. It is shown that the whole-body SAR does not differ significantly over the different environments at an operating frequency of 950 MHz. Furthermore, the whole-body SAR in the VFB for multi-path exposure exceeds the whole-body SAR for worst-case single-incident plane wave exposure by 3.6%. Moreover, the ICNIRP reference levels are not conservative with the basic restrictions in 0.3% of the exposure samples for the VFB at the GSM downlink of 950 MHz. The homogeneous spheroid with the dielectric properties of the head suggested by the IEC underestimates the absorption compared to realistic human body models. Moreover, the variation in the whole-body SAR for realistic human body models is larger than for homogeneous spheroid models. This is mainly due to the heterogeneity of the tissues and the irregular shape of the realistic human body model compared to homogeneous spheroid human body models. Copyright © 2012 Wiley Periodicals, Inc.

Fast convolutional sparse coding using matrix inversion lemma

Czech Academy of Sciences Publication Activity Database

Šorel, Michal; Šroubek, Filip

2016-01-01

Roč. 55, č. 1 (2016), s. 44-51 ISSN 1051-2004 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Convolutional sparse coding * Feature learning * Deconvolution networks * Shift-invariant sparse coding Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.337, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/sorel-0459332.pdf

Gaussian mixture probability hypothesis density filter for multipath multitarget tracking in over-the-horizon radar

Science.gov (United States)

Qin, Yong; Ma, Hong; Chen, Jinfeng; Cheng, Li

2015-12-01

Conventional multitarget tracking systems presume that each target can produce at most one measurement per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments. First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.

Structure-based bayesian sparse reconstruction

KAUST Repository

Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.

2012-01-01

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical

Modelling and Comparative Performance Analysis of a Time-Reversed UWB System

Directory of Open Access Journals (Sweden)

Popovski K

2007-01-01

Full Text Available The effects of multipath propagation lead to a significant decrease in system performance in most of the proposed ultra-wideband communication systems. A time-reversed system utilises the multipath channel impulse response to decrease receiver complexity, through a prefiltering at the transmitter. This paper discusses the modelling and comparative performance of a UWB system utilising time-reversed communications. System equations are presented, together with a semianalytical formulation on the level of intersymbol interference and multiuser interference. The standardised IEEE 802.15.3a channel model is applied, and the estimated error performance is compared through simulation with the performance of both time-hopped time-reversed and RAKE-based UWB systems.

Binary Sparse Phase Retrieval via Simulated Annealing

Directory of Open Access Journals (Sweden)

Wei Peng

2016-01-01

Full Text Available This paper presents the Simulated Annealing Sparse PhAse Recovery (SASPAR algorithm for reconstructing sparse binary signals from their phaseless magnitudes of the Fourier transform. The greedy strategy version is also proposed for a comparison, which is a parameter-free algorithm. Sufficient numeric simulations indicate that our method is quite effective and suggest the binary model is robust. The SASPAR algorithm seems competitive to the existing methods for its efficiency and high recovery rate even with fewer Fourier measurements.

Secure Degrees of Freedom of the Gaussian Z Channel with Single Antenna

Directory of Open Access Journals (Sweden)

Xianzhong XIE

2014-03-01

Full Text Available This paper presents the secrecy capacity and the secure degrees of freedom of Gaussian Z channel with single antenna and confidential information. Firstly, we analysis the secrecy capacity and the upper bound of secure degrees of freedom of this channel in theory. Then, we respectively discuss the security pre-coding scheme for real Gaussian channel model and frequency selection channel model. Under the first model, through real interference alignment and cooperative jamming, we obtain the secrecy capacity and secure degrees of freedom, proving that it can reach the upper bound of secure degrees of freedom in theory. While, under the second one, a strong security pre-coding algorithm is proposed, which is based on the fact that sparse matrix has strong hash property. Next, we arrange interference with interference alignment and the receivers process their received signal through zero forcing algorithm. At last, the messages are reconstructed with maximum likelihood decoding, where it shows that the algorithm can asymptotically achieve the optimal secrecy capacity.

Ultrasonic Digital Communication System for a Steel Wall Multipath Channel: Methods and Results

Energy Technology Data Exchange (ETDEWEB)

Murphy, Timothy L. [Rensselaer Polytechnic Inst., Troy, NY (United States)

2005-12-01

As of the development of this thesis, no commercially available products have been identified for the digital communication of instrumented data across a thick ({approx} 6 n.) steel wall using ultrasound. The specific goal of the current research is to investigate the application of methods for digital communication of instrumented data (i.e., temperature, voltage, etc.) across the wall of a steel pressure vessel. The acoustic transmission of data using ultrasonic transducers prevents the need to breach the wall of such a pressure vessel which could ultimately affect its safety or lifespan, or void the homogeneity of an experiment under test. Actual digital communication paradigms are introduced and implemented for the successful dissemination of data across such a wall utilizing solely an acoustic ultrasonic link. The first, dubbed the ''single-hop'' configuration, can communicate bursts of digital data one-way across the wall using the Differential Binary Phase-Shift Keying (DBPSK) modulation technique as fast as 500 bps. The second, dubbed the ''double-hop'' configuration, transmits a carrier into the vessel, modulates it, and retransmits it externally. Using a pulsed carrier with Pulse Amplitude Modulation (PAM), this technique can communicate digital data as fast as 500 bps. Using a CW carrier, Least Mean-Squared (LMS) adaptive interference suppression, and DBPSK, this method can communicate data as fast as 5 kbps. A third technique, dubbed the ''reflected-power'' configuration, communicates digital data by modulating a pulsed carrier by varying the acoustic impedance at the internal transducer-wall interface. The paradigms of the latter two configurations are believed to be unique. All modulation methods are based on the premise that the wall cannot be breached in any way and can therefore be viably implemented with power delivered wirelessly through the acoustic channel using ultrasound. Methods

Confidence of model based shape reconstruction from sparse data

DEFF Research Database (Denmark)

Baka, N.; de Bruijne, Marleen; Reiber, J. H. C.

2010-01-01

Statistical shape models (SSM) are commonly applied for plausible interpolation of missing data in medical imaging. However, when fitting a shape model to sparse information, many solutions may fit the available data. In this paper we derive a constrained SSM to fit noisy sparse input landmarks...

Proportionate Minimum Error Entropy Algorithm for Sparse System Identification

Directory of Open Access Journals (Sweden)

Zongze Wu

2015-08-01

Full Text Available Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE criterion, which is optimal only when the measurement noise is Gaussian. However, this condition does not hold in most real-world environments. In this work, we use the minimum error entropy (MEE criterion, an alternative to the conventional MSE criterion, to develop the proportionate minimum error entropy (PMEE algorithm for sparse system identification, which may achieve much better performance than the MSE based methods especially in heavy-tailed non-Gaussian situations. Moreover, we analyze the convergence of the proposed algorithm and derive a sufficient condition that ensures the mean square convergence. Simulation results confirm the excellent performance of the new algorithm.

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

KAUST Repository

Sicat, Ronell Barrera

2014-12-31

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

Ordering sparse matrices for cache-based systems

International Nuclear Information System (INIS)

Biswas, Rupak; Oliker, Leonid

2001-01-01

The Conjugate Gradient (CG) algorithm is the oldest and best-known Krylov subspace method used to solve sparse linear systems. Most of the coating-point operations within each CG iteration is spent performing sparse matrix-vector multiplication (SPMV). We examine how various ordering and partitioning strategies affect the performance of CG and SPMV when different programming paradigms are used on current commercial cache-based computers. However, a multithreaded implementation on the cacheless Cray MTA demonstrates high efficiency and scalability without any special ordering or partitioning

A flexible framework for sparse simultaneous component based data integration

Directory of Open Access Journals (Sweden)

Van Deun Katrijn

2011-11-01

Full Text Available Abstract 1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins have to be taken into account. 2 Results We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. 3 Conclusion Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such

A flexible framework for sparse simultaneous component based data integration.

Science.gov (United States)

Van Deun, Katrijn; Wilderjans, Tom F; van den Berg, Robert A; Antoniadis, Anestis; Van Mechelen, Iven

2011-11-15

High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform

P-SPARSLIB: A parallel sparse iterative solution package

Energy Technology Data Exchange (ETDEWEB)

Saad, Y. [Univ. of Minnesota, Minneapolis, MN (United States)

1994-12-31

Iterative methods are gaining popularity in engineering and sciences at a time where the computational environment is changing rapidly. P-SPARSLIB is a project to build a software library for sparse matrix computations on parallel computers. The emphasis is on iterative methods and the use of distributed sparse matrices, an extension of the domain decomposition approach to general sparse matrices. One of the goals of this project is to develop a software package geared towards specific applications. For example, the author will test the performance and usefulness of P-SPARSLIB modules on linear systems arising from CFD applications. Equally important is the goal of portability. In the long run, the author wishes to ensure that this package is portable on a variety of platforms, including SIMD environments and shared memory environments.

Enhancing Sensing and Channel Access in Cognitive Radio Networks

KAUST Repository

Hamza, Doha R.

2014-06-18

Cognitive radio technology is a promising technology to solve the wireless spectrum scarcity problem by intelligently allowing secondary, or unlicensed, users access to the primary, licensed, users\\' frequency bands. Cognitive technology involves two main tasks: 1) sensing the wireless medium to assess the presence of the primary users and 2) designing secondary spectrum access techniques that maximize the secondary users\\' benefits while maintaining the primary users\\' privileged status. On the spectrum sensing side, we make two contributions. First, we maximize a utility function representing the secondary throughput while constraining the collision probability with the primary below a certain value. We optimize therein the channel sensing time, the sensing decision threshold, the channel probing time, together with the channel sensing order for wideband primary channels. Second, we design a cooperative spectrum sensing technique termed sensing with equal gain combining whereby cognitive radios simultaneously transmit their sensing results to the fusion center over multipath fading reporting channels. The proposed scheme is shown to outperform orthogonal reporting systems in terms of achievable secondary throughput and to be robust against phase and synchronization errors. On the spectrum access side, we make four contributions. First, we design a secondary scheduling scheme with the goal of minimizing the secondary queueing delay under constraints on the average secondary transmit power and the maximum tolerable primary outage probability. Second, we design another secondary scheduling scheme based on the spectrum sensing results and the primary automatic repeat request feedback. The optimal medium access probabilities are obtained via maximizing the secondary throughput subject to constraints that guarantee quality of service parameters for the primary. Third, we propose a three-message superposition coding scheme to maximize the secondary throughput without

Feature selection and multi-kernel learning for sparse representation on a manifold

KAUST Repository

Wang, Jim Jing-Yan

2014-03-01

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. © 2013 Elsevier Ltd.

Feature selection and multi-kernel learning for sparse representation on a manifold.

Science.gov (United States)

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2014-03-01

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sparse representation, modeling and learning in visual recognition theory, algorithms and applications

CERN Document Server

Cheng, Hong

2015-01-01

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition; describes sparse recovery approaches, robust and efficient sparse represen

Design Patterns for Sparse-Matrix Computations on Hybrid CPU/GPU Platforms

Directory of Open Access Journals (Sweden)

Valeria Cardellini

2014-01-01

Full Text Available We apply object-oriented software design patterns to develop code for scientific software involving sparse matrices. Design patterns arise when multiple independent developments produce similar designs which converge onto a generic solution. We demonstrate how to use design patterns to implement an interface for sparse matrix computations on NVIDIA GPUs starting from PSBLAS, an existing sparse matrix library, and from existing sets of GPU kernels for sparse matrices. We also compare the throughput of the PSBLAS sparse matrix–vector multiplication on two platforms exploiting the GPU with that obtained by a CPU-only PSBLAS implementation. Our experiments exhibit encouraging results regarding the comparison between CPU and GPU executions in double precision, obtaining a speedup of up to 35.35 on NVIDIA GTX 285 with respect to AMD Athlon 7750, and up to 10.15 on NVIDIA Tesla C2050 with respect to Intel Xeon X5650.

An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data

DEFF Research Database (Denmark)

Liu, Weifeng; Vinter, Brian

2014-01-01

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method, breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM algorithm has to handle extra...... irregularity from three aspects: (1) the number of the nonzero entries in the result sparse matrix is unknown in advance, (2) very expensive parallel insert operations at random positions in the result sparse matrix dominate the execution time, and (3) load balancing must account for sparse data in both input....... Load balancing builds on the number of the necessary arithmetic operations on the nonzero entries and is guaranteed in all stages. Compared with the state-of-the-art GPU SpGEMM methods in the CUSPARSE library and the CUSP library and the latest CPU SpGEMM method in the Intel Math Kernel Library, our...

Comparison of Methods for Sparse Representation of Musical Signals

DEFF Research Database (Denmark)

Endelt, Line Ørtoft; la Cour-Harbo, Anders

2005-01-01

by a number of sparseness measures and results are shown on the ℓ1 norm of the coefficients, using a dictionary containing a Dirac basis, a Discrete Cosine Transform, and a Wavelet Packet. Evaluated only on the sparseness Matching Pursuit is the best method, and it is also relatively fast....

Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction

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Dong Zhang

2017-01-01

Full Text Available Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method.

Discussion of CoSA: Clustering of Sparse Approximations

Energy Technology Data Exchange (ETDEWEB)

Armstrong, Derek Elswick [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-03-07

The purpose of this talk is to discuss the possible applications of CoSA (Clustering of Sparse Approximations) to the exploitation of HSI (HyperSpectral Imagery) data. CoSA is presented by Moody et al. in the Journal of Applied Remote Sensing (“Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries”, Vol. 8, 2014) and is based on machine learning techniques.

Low complexity variational bayes iterative reviver for MIMO-OFDM systems

DEFF Research Database (Denmark)

Xiong, Chunlin; Wang, Hua; Zhang, Xiaoying

2009-01-01

A low complexity iterative receiver is proposed in this paper for MIMO-OFDM systems in time-varying multi-path channel based on the variational Bayes (VB) method. According to the VB method, the estimation algorithms of the signal distribution and the channel distribution are derived for the rece...

Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding

Directory of Open Access Journals (Sweden)

Ying Chen

2014-05-01

Full Text Available Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE database. Compared with other widely used methods such as linear support vector machines (SVM, sparse representation-based classifier (SRC, nearest subspace classifier (NSC, K-nearest neighbor (KNN and radial basis function neural networks (RBFNN, the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks.

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification

Directory of Open Access Journals (Sweden)

Lu Bing

2017-01-01

Full Text Available We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL. After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM. Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification.

Science.gov (United States)

Bing, Lu; Wang, Wei

2017-01-01

We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

Science.gov (United States)

Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

2016-02-19

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

Joint sparse representation for robust multimodal biometrics recognition.

Science.gov (United States)

Shekhar, Sumit; Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama

2014-01-01

Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.

Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

KAUST Repository

Zhang, Tianzhu

2014-06-19

Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.

Sparse dictionary learning of resting state fMRI networks.

Science.gov (United States)

Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

2012-07-02

Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

Low-Rank Sparse Coding for Image Classification

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Xu, Changsheng; Ahuja, Narendra

2013-01-01

In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

Low-Rank Sparse Coding for Image Classification

KAUST Repository

Zhang, Tianzhu

2013-12-01

In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

DEFF Research Database (Denmark)

Han, Xixuan; Clemmensen, Line Katrine Harder

2015-01-01

We propose a general technique for obtaining sparse solutions to generalized eigenvalue problems, and call it Regularized Generalized Eigen-Decomposition (RGED). For decades, Fisher's discriminant criterion has been applied in supervised feature extraction and discriminant analysis, and it is for...

A performance study of sparse Cholesky factorization on INTEL iPSC/860

Science.gov (United States)

Zubair, M.; Ghose, M.

1992-01-01

The problem of Cholesky factorization of a sparse matrix has been very well investigated on sequential machines. A number of efficient codes exist for factorizing large unstructured sparse matrices. However, there is a lack of such efficient codes on parallel machines in general, and distributed machines in particular. Some of the issues that are critical to the implementation of sparse Cholesky factorization on a distributed memory parallel machine are ordering, partitioning and mapping, load balancing, and ordering of various tasks within a processor. Here, we focus on the effect of various partitioning schemes on the performance of sparse Cholesky factorization on the Intel iPSC/860. Also, a new partitioning heuristic for structured as well as unstructured sparse matrices is proposed, and its performance is compared with other schemes.

Fast sparsely synchronized brain rhythms in a scale-free neural network.

Science.gov (United States)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For Dsparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of

Fast sparsely synchronized brain rhythms in a scale-free neural network

Science.gov (United States)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D . For small D , full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D sparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of statistically homogeneous

l1- and l2-Norm Joint Regularization Based Sparse Signal Reconstruction Scheme

Directory of Open Access Journals (Sweden)

Chanzi Liu

2016-01-01

Full Text Available Many problems in signal processing and statistical inference involve finding sparse solution to some underdetermined linear system of equations. This is also the application condition of compressive sensing (CS which can find the sparse solution from the measurements far less than the original signal. In this paper, we propose l1- and l2-norm joint regularization based reconstruction framework to approach the original l0-norm based sparseness-inducing constrained sparse signal reconstruction problem. Firstly, it is shown that, by employing the simple conjugate gradient algorithm, the new formulation provides an effective framework to deduce the solution as the original sparse signal reconstruction problem with l0-norm regularization item. Secondly, the upper reconstruction error limit is presented for the proposed sparse signal reconstruction framework, and it is unveiled that a smaller reconstruction error than l1-norm relaxation approaches can be realized by using the proposed scheme in most cases. Finally, simulation results are presented to validate the proposed sparse signal reconstruction approach.

Image fusion via nonlocal sparse K-SVD dictionary learning.

Science.gov (United States)

Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang

2016-03-01

Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

Detection of Pitting in Gears Using a Deep Sparse Autoencoder

Directory of Open Access Journals (Sweden)

Yongzhi Qu

2017-05-01

Full Text Available In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.

MQARR-AODV: A NOVEL MULTIPATH QOS AWARE RELIABLE REVERSE ON-DEMAND DISTANCE VECTOR ROUTING PROTOCOL FOR MOBILE AD-HOC NETWORKS

Directory of Open Access Journals (Sweden)

K.G. Santhiya

2012-12-01

Full Text Available MANET (Mobile Ad-hoc Network is an infra structure less wireless ad-hoc network that does not require any basic central control. The topology of the network changes drastically due to very fast mobility of nodes. So an adaptive routing protocol is needed for routing in MANET. AODV (Ad-hoc On-demand Distance Vector routing is the effective and prominent on-demand Ad-hoc routing protocols. During route establishment phase in traditional AODV, only one route reply message will be sent in the reverse path to establish routing path. The high mobility of nodes may affect the reply messages which lead to the retransmission of route request message by the sender which in turn leads to higher communication delay, power consumption and the reduction in the ratio of packets delivered. Sending multiple route reply messages and establishing multiple paths in a single path discovery will reduce the routing overhead involved in maintaining the connection between source and destination nodes. Multipath routing can render high scalability, end-to-end throughput and provide load balancing in MANET. The new proposed novel Multipath QoS aware reliable routing protocol establishes two routes of maximum node disjoint paths and the data transfer is carried out in the two paths simultaneously. To select best paths, the new proposed protocol uses three parameters Link Eminence, MAC overhead and node residual energy. The experimental values prove that the MQARR-AODV protocol achieves high reliability, stability, low latency and outperforms AODV by the less energy consumption, overhead and delay.

In-Storage Embedded Accelerator for Sparse Pattern Processing

OpenAIRE

Jun, Sang-Woo; Nguyen, Huy T.; Gadepally, Vijay N.; Arvind

2016-01-01

We present a novel architecture for sparse pattern processing, using flash storage with embedded accelerators. Sparse pattern processing on large data sets is the essence of applications such as document search, natural language processing, bioinformatics, subgraph matching, machine learning, and graph processing. One slice of our prototype accelerator is capable of handling up to 1TB of data, and experiments show that it can outperform C/C++ software solutions on a 16-core system at a fracti...

Process Knowledge Discovery Using Sparse Principal Component Analysis

DEFF Research Database (Denmark)

Gao, Huihui; Gajjar, Shriram; Kulahci, Murat

2016-01-01

As the goals of ensuring process safety and energy efficiency become ever more challenging, engineers increasingly rely on data collected from such processes for informed decision making. During recent decades, extracting and interpreting valuable process information from large historical data sets...... SPCA approach that helps uncover the underlying process knowledge regarding variable relations. This approach systematically determines the optimal sparse loadings for each sparse PC while improving interpretability and minimizing information loss. The salient features of the proposed approach...

Massively parallel sparse matrix function calculations with NTPoly

Science.gov (United States)

Dawson, William; Nakajima, Takahito

2018-04-01

We present NTPoly, a massively parallel library for computing the functions of sparse, symmetric matrices. The theory of matrix functions is a well developed framework with a wide range of applications including differential equations, graph theory, and electronic structure calculations. One particularly important application area is diagonalization free methods in quantum chemistry. When the input and output of the matrix function are sparse, methods based on polynomial expansions can be used to compute matrix functions in linear time. We present a library based on these methods that can compute a variety of matrix functions. Distributed memory parallelization is based on a communication avoiding sparse matrix multiplication algorithm. OpenMP task parallellization is utilized to implement hybrid parallelization. We describe NTPoly's interface and show how it can be integrated with programs written in many different programming languages. We demonstrate the merits of NTPoly by performing large scale calculations on the K computer.

Deformable segmentation via sparse representation and dictionary learning.

Science.gov (United States)

Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N

2012-10-01

"Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

Sparseness- and continuity-constrained seismic imaging

Science.gov (United States)

Herrmann, Felix J.

2005-04-01

Non-linear solution strategies to the least-squares seismic inverse-scattering problem with sparseness and continuity constraints are proposed. Our approach is designed to (i) deal with substantial amounts of additive noise (SNR formulating the solution of the seismic inverse problem in terms of an optimization problem. During the optimization, sparseness on the basis and continuity along the reflectors are imposed by jointly minimizing the l1- and anisotropic diffusion/total-variation norms on the coefficients and reflectivity, respectively. [Joint work with Peyman P. Moghaddam was carried out as part of the SINBAD project, with financial support secured through ITF (the Industry Technology Facilitator) from the following organizations: BG Group, BP, ExxonMobil, and SHELL. Additional funding came from the NSERC Discovery Grants 22R81254.

Combinatorial Algorithms for Computing Column Space Bases ThatHave Sparse Inverses

Energy Technology Data Exchange (ETDEWEB)

Pinar, Ali; Chow, Edmond; Pothen, Alex

2005-03-18

This paper presents a combinatorial study on the problem ofconstructing a sparse basis forthe null-space of a sparse, underdetermined, full rank matrix, A. Such a null-space is suitable forsolving solving many saddle point problems. Our approach is to form acolumn space basis of A that has a sparse inverse, by selecting suitablecolumns of A. This basis is then used to form a sparse null-space basisin fundamental form. We investigate three different algorithms forcomputing the column space basis: Two greedy approaches that rely onmatching, and a third employing a divide and conquer strategy implementedwith hypergraph partitioning followed by the greedy approach. We alsodiscuss the complexity of selecting a column basis when it is known thata block diagonal basis exists with a small given block size.

Time Domain Equalizer Design Using Bit Error Rate Minimization for UWB Systems

Directory of Open Access Journals (Sweden)

Syed Imtiaz Husain

2009-01-01

Full Text Available Ultra-wideband (UWB communication systems occupy huge bandwidths with very low power spectral densities. This feature makes the UWB channels highly rich in resolvable multipaths. To exploit the temporal diversity, the receiver is commonly implemented through a Rake. The aim to capture enough signal energy to maintain an acceptable output signal-to-noise ratio (SNR dictates a very complicated Rake structure with a large number of fingers. Channel shortening or time domain equalizer (TEQ can simplify the Rake receiver design by reducing the number of significant taps in the effective channel. In this paper, we first derive the bit error rate (BER of a multiuser and multipath UWB system in the presence of a TEQ at the receiver front end. This BER is then written in a form suitable for traditional optimization. We then present a TEQ design which minimizes the BER of the system to perform efficient channel shortening. The performance of the proposed algorithm is compared with some generic TEQ designs and other Rake structures in UWB channels. It is shown that the proposed algorithm maintains a lower BER along with efficiently shortening the channel.

Image Super-Resolution Algorithm Based on an Improved Sparse Autoencoder

Directory of Open Access Journals (Sweden)

Detian Huang

2018-01-01

Full Text Available Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application’s requirements. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. Firstly, in the training set preprocessing stage, the high- and low-resolution image training sets are constructed, respectively, by using high-frequency information of the training samples as the characterization, and then the zero-phase component analysis whitening technique is utilized to decorrelate the formed joint training set to reduce its redundancy. Secondly, a constructed sparse regularization term is added to the cost function of the traditional sparse autoencoder to further strengthen the sparseness constraint on the hidden layer. Finally, in the dictionary learning stage, the improved sparse autoencoder is adopted to achieve unsupervised dictionary learning to improve the accuracy and stability of the dictionary. Experimental results validate that the proposed algorithm outperforms the existing algorithms both in terms of the subjective visual perception and the objective evaluation indices, including the peak signal-to-noise ratio and the structural similarity measure.

Diagnosis and prognosis of Ostheoarthritis by texture analysis using sparse linear models

DEFF Research Database (Denmark)

Marques, Joselene; Clemmensen, Line Katrine Harder; Dam, Erik

We present a texture analysis methodology that combines uncommitted machine-learning techniques and sparse feature transformation methods in a fully automatic framework. We compare the performances of a partial least squares (PLS) forward feature selection strategy to a hard threshold sparse PLS...... algorithm and a sparse linear discriminant model. The texture analysis framework was applied to diagnosis of knee osteoarthritis (OA) and prognosis of cartilage loss. For this investigation, a generic texture feature bank was extracted from magnetic resonance images of tibial knee bone. The features were...... used as input to the sparse algorithms, which dened the best features to retain in the model. To cope with the limited number of samples, the data was evaluated using 10 fold cross validation (CV). The diagnosis evaluation using sparse PLS reached a generalization area-under-the-ROC curve (AUC) of 0...

Efficient implementations of block sparse matrix operations on shared memory vector machines

International Nuclear Information System (INIS)

Washio, T.; Maruyama, K.; Osoda, T.; Doi, S.; Shimizu, F.

2000-01-01

In this paper, we propose vectorization and shared memory-parallelization techniques for block-type random sparse matrix operations in finite element (FEM) applications. Here, a block corresponds to unknowns on one node in the FEM mesh and we assume that the block size is constant over the mesh. First, we discuss some basic vectorization ideas (the jagged diagonal (JAD) format and the segmented scan algorithm) for the sparse matrix-vector product. Then, we extend these ideas to the shared memory parallelization. After that, we show that the techniques can be applied not only to the sparse matrix-vector product but also to the sparse matrix-matrix product, the incomplete or complete sparse LU factorization and preconditioning. Finally, we report the performance evaluation results obtained on an NEC SX-4 shared memory vector machine for linear systems in some FEM applications. (author)

A Projected Conjugate Gradient Method for Sparse Minimax Problems

DEFF Research Database (Denmark)

Madsen, Kaj; Jonasson, Kristjan

1993-01-01

A new method for nonlinear minimax problems is presented. The method is of the trust region type and based on sequential linear programming. It is a first order method that only uses first derivatives and does not approximate Hessians. The new method is well suited for large sparse problems...... as it only requires that software for sparse linear programming and a sparse symmetric positive definite equation solver are available. On each iteration a special linear/quadratic model of the function is minimized, but contrary to the usual practice in trust region methods the quadratic model is only...... with the method are presented. In fact, we find that the number of iterations required is comparable to that of state-of-the-art quasi-Newton codes....

Identification of MIMO systems with sparse transfer function coefficients

Science.gov (United States)

Qiu, Wanzhi; Saleem, Syed Khusro; Skafidas, Efstratios

2012-12-01

We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output--MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient for systems with sparse transfer function coefficients since they work on state space models. We propose a two-step algorithm where the first step identifies the system order using the subspace principle in a state space format, while the second step estimates coefficients of the transfer functions via L1-norm convex optimization. The proposed algorithm retains good features of subspace methods with improved noise-robustness for sparse systems.

MULTISCALE SPARSE APPEARANCE MODELING AND SIMULATION OF PATHOLOGICAL DEFORMATIONS

Directory of Open Access Journals (Sweden)

Rami Zewail

2017-08-01

Full Text Available Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

An Adaptive Sparse Grid Algorithm for Elliptic PDEs with Lognormal Diffusion Coefficient

KAUST Repository

Nobile, Fabio

2016-03-18

In this work we build on the classical adaptive sparse grid algorithm (T. Gerstner and M. Griebel, Dimension-adaptive tensor-product quadrature), obtaining an enhanced version capable of using non-nested collocation points, and supporting quadrature and interpolation on unbounded sets. We also consider several profit indicators that are suitable to drive the adaptation process. We then use such algorithm to solve an important test case in Uncertainty Quantification problem, namely the Darcy equation with lognormal permeability random field, and compare the results with those obtained with the quasi-optimal sparse grids based on profit estimates, which we have proposed in our previous works (cf. e.g. Convergence of quasi-optimal sparse grids approximation of Hilbert-valued functions: application to random elliptic PDEs). To treat the case of rough permeability fields, in which a sparse grid approach may not be suitable, we propose to use the adaptive sparse grid quadrature as a control variate in a Monte Carlo simulation. Numerical results show that the adaptive sparse grids have performances similar to those of the quasi-optimal sparse grids and are very effective in the case of smooth permeability fields. Moreover, their use as control variate in a Monte Carlo simulation allows to tackle efficiently also problems with rough coefficients, significantly improving the performances of a standard Monte Carlo scheme.

Sparse principal component analysis in medical shape modeling

Science.gov (United States)

Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus

2006-03-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

Inference algorithms and learning theory for Bayesian sparse factor analysis

International Nuclear Information System (INIS)

Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John

2009-01-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Inference algorithms and learning theory for Bayesian sparse factor analysis

Energy Technology Data Exchange (ETDEWEB)

Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)

2009-12-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Universal Regularizers For Robust Sparse Coding and Modeling

OpenAIRE

Ramirez, Ignacio; Sapiro, Guillermo

2010-01-01

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding...

Deep ensemble learning of sparse regression models for brain disease diagnosis.

Science.gov (United States)

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

Science.gov (United States)

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

Composite and Cascaded Generalized-K Fading Channel Modeling and Their Diversity and Performance Analysis

KAUST Repository

Ansari, Imran Shafique

2010-12-01

The introduction of new schemes that are based on the communication among nodes has motivated the use of composite fading models due to the fact that the nodes experience different multipath fading and shadowing statistics, which subsequently determines the required statistics for the performance analysis of different transceivers. The end-to-end signal-to-noise-ratio (SNR) statistics plays an essential role in the determination of the performance of cascaded digital communication systems. In this thesis, a closed-form expression for the probability density function (PDF) of the end-end SNR for independent but not necessarily identically distributed (i.n.i.d.) cascaded generalized-K (GK) composite fading channels is derived. The developed PDF expression in terms of the Meijer-G function allows the derivation of subsequent performance metrics, applicable to different modulation schemes, including outage probability, bit error rate for coherent as well as non-coherent systems, and average channel capacity that provides insights into the performance of a digital communication system operating in N cascaded GK composite fading environment. Another line of research that was motivated by the introduction of composite fading channels is the error performance. Error performance is one of the main performance measures and derivation of its closed-form expression has proved to be quite involved for certain systems. Hence, in this thesis, a unified closed-form expression, applicable to different binary modulation schemes, for the bit error rate of dual-branch selection diversity based systems undergoing i.n.i.d. GK fading is derived in terms of the extended generalized bivariate Meijer G-function.

Color-Space-Based Visual-MIMO for V2X Communication

OpenAIRE

Jai-Eun Kim; Ji-Won Kim; Youngil Park; Ki-Doo Kim

2016-01-01

In this paper, we analyze the applicability of color-space-based, color-independent visual-MIMO for V2X. We aim to achieve a visual-MIMO scheme that can maintain the original color and brightness while performing seamless communication. We consider two scenarios of GCM based visual-MIMO for V2X. One is a multipath transmission using visual-MIMO networking and the other is multi-node V2X communication. In the scenario of multipath transmission, we analyze the channel capacity numerically and w...

Robust Fringe Projection Profilometry via Sparse Representation.

Science.gov (United States)

Budianto; Lun, Daniel P K

2016-04-01

In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment.

Sparse DOA estimation with polynomial rooting

DEFF Research Database (Denmark)

Xenaki, Angeliki; Gerstoft, Peter; Fernandez Grande, Efren

2015-01-01

Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve highresol......Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve...... highresolution imaging. Utilizing the dual optimal variables of the CS optimization problem, it is shown with Monte Carlo simulations that the DOAs are accurately reconstructed through polynomial rooting (Root-CS). Polynomial rooting is known to improve the resolution in several other DOA estimation methods...

A General Sparse Tensor Framework for Electronic Structure Theory.

Science.gov (United States)

Manzer, Samuel; Epifanovsky, Evgeny; Krylov, Anna I; Head-Gordon, Martin

2017-03-14

Linear-scaling algorithms must be developed in order to extend the domain of applicability of electronic structure theory to molecules of any desired size. However, the increasing complexity of modern linear-scaling methods makes code development and maintenance a significant challenge. A major contributor to this difficulty is the lack of robust software abstractions for handling block-sparse tensor operations. We therefore report the development of a highly efficient symbolic block-sparse tensor library in order to provide access to high-level software constructs to treat such problems. Our implementation supports arbitrary multi-dimensional sparsity in all input and output tensors. We avoid cumbersome machine-generated code by implementing all functionality as a high-level symbolic C++ language library and demonstrate that our implementation attains very high performance for linear-scaling sparse tensor contractions.

Low-rank and sparse modeling for visual analysis

CERN Document Server

Fu, Yun

2014-01-01

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applic

Real-time SPARSE-SENSE cardiac cine MR imaging: optimization of image reconstruction and sequence validation.

Science.gov (United States)

Goebel, Juliane; Nensa, Felix; Bomas, Bettina; Schemuth, Haemi P; Maderwald, Stefan; Gratz, Marcel; Quick, Harald H; Schlosser, Thomas; Nassenstein, Kai

2016-12-01

Improved real-time cardiac magnetic resonance (CMR) sequences have currently been introduced, but so far only limited practical experience exists. This study aimed at image reconstruction optimization and clinical validation of a new highly accelerated real-time cine SPARSE-SENSE sequence. Left ventricular (LV) short-axis stacks of a real-time free-breathing SPARSE-SENSE sequence with high spatiotemporal resolution and of a standard segmented cine SSFP sequence were acquired at 1.5 T in 11 volunteers and 15 patients. To determine the optimal iterations, all volunteers' SPARSE-SENSE images were reconstructed using 10-200 iterations, and contrast ratios, image entropies, and reconstruction times were assessed. Subsequently, the patients' SPARSE-SENSE images were reconstructed with the clinically optimal iterations. LV volumetric values were evaluated and compared between both sequences. Sufficient image quality and acceptable reconstruction times were achieved when using 80 iterations. Bland-Altman plots and Passing-Bablok regression showed good agreement for all volumetric parameters. 80 iterations are recommended for iterative SPARSE-SENSE image reconstruction in clinical routine. Real-time cine SPARSE-SENSE yielded comparable volumetric results as the current standard SSFP sequence. Due to its intrinsic low image acquisition times, real-time cine SPARSE-SENSE imaging with iterative image reconstruction seems to be an attractive alternative for LV function analysis. • A highly accelerated real-time CMR sequence using SPARSE-SENSE was evaluated. • SPARSE-SENSE allows free breathing in real-time cardiac cine imaging. • For clinically optimal SPARSE-SENSE image reconstruction, 80 iterations are recommended. • Real-time SPARSE-SENSE imaging yielded comparable volumetric results as the reference SSFP sequence. • The fast SPARSE-SENSE sequence is an attractive alternative to standard SSFP sequences.

Complex sparse spatial filter for decoding mixed frequency and phase coded steady-state visually evoked potentials.

Science.gov (United States)

Morikawa, Naoki; Tanaka, Toshihisa; Islam, Md Rabiul

2018-07-01

Mixed frequency and phase coding (FPC) can achieve the significant increase of the number of commands in steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI). However, the inconsistent phases of the SSVEP over channels in a trial and the existence of non-contributing channels due to noise effects can decrease accurate detection of stimulus frequency. We propose a novel command detection method based on a complex sparse spatial filter (CSSF) by solving ℓ 1 - and ℓ 2,1 -regularization problems for a mixed-coded SSVEP-BCI. In particular, ℓ 2,1 -regularization (aka group sparsification) can lead to the rejection of electrodes that are not contributing to the SSVEP detection. A calibration data based canonical correlation analysis (CCA) and CSSF with ℓ 1 - and ℓ 2,1 -regularization cases were demonstrated for a 16-target stimuli with eleven subjects. The results of statistical test suggest that the proposed method with ℓ 1 - and ℓ 2,1 -regularization significantly achieved the highest ITR. The proposed approaches do not need any reference signals, automatically select prominent channels, and reduce the computational cost compared to the other mixed frequency-phase coding (FPC)-based BCIs. The experimental results suggested that the proposed method can be usable implementing BCI effectively with reduce visual fatigue. Copyright © 2018 Elsevier B.V. All rights reserved.

Security-enhanced phase encryption assisted by nonlinear optical correlation via sparse phase

International Nuclear Information System (INIS)

Chen, Wen; Chen, Xudong; Wang, Xiaogang

2015-01-01

We propose a method for security-enhanced phase encryption assisted by a nonlinear optical correlation via a sparse phase. Optical configurations are established based on a phase retrieval algorithm for embedding an input image and the secret data into phase-only masks. We found that when one or a few phase-only masks generated during data hiding are sparse, it is possible to integrate these sparse masks into those phase-only masks generated during the encoding of the input image. Synthesized phase-only masks are used for the recovery, and sparse distributions (i.e., binary maps) for generating the incomplete phase-only masks are considered as additional parameters for the recovery of secret data. It is difficult for unauthorized receivers to know that a useful phase has been sparsely distributed in the finally generated phase-only masks for secret-data recovery. Only when the secret data are correctly verified can the input image obtained with valid keys be claimed as targeted information. (paper)

Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

Science.gov (United States)

Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

2015-04-01

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

Low-rank sparse learning for robust visual tracking

KAUST Repository

Zhang, Tianzhu

2012-01-01

In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.

MHD electrical boundary layer theory and applications to the performance of channels with partial wraparound electrodes

International Nuclear Information System (INIS)

Zwick, S.A.; Doss, E.D.; West Florida University, Pensacola, FL)

1981-01-01

Analytical methods are developed for calculating the potential and currents near boundary singularities caused by electrode edges or abrupt drops in conductivity or in the induction field. A three-dimensional control volume (finite-difference) model for solving the MHD electrical problems in oblique coordinates has been developed, which accounts for the near-wall singular behavior accurately and can be used with relatively sparse grids. Analyses based on the model indicate that, for practical generator design where the electrode pitch is in the order of 1 to 5 cm and the wall temperature less than 2100 K, the performance of diagonal conducting wall (DCW) channels is always superior to that of channels with insulating sidewalls, although the performance of insulating sidewall channel is better at higher wall temperatures. Sidewall electrode extensions up to a wraparound of about 20% of the channel height are shown to cause an increase in power output. The output of diagonally connected channels remains approximately the same for more than 20% wraparound whereas the power output of Faraday channels drops off with further extensions of the sidewall conductors

Group-sparse representation with dictionary learning for medical image denoising and fusion.

Science.gov (United States)

Li, Shutao; Yin, Haitao; Fang, Leyuan

2012-12-01

Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2015-01-01

A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

KAUST Repository

Desmal, Abdulla

2015-04-13

A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

On the Automatic Parallelization of Sparse and Irregular Fortran Programs

Directory of Open Access Journals (Sweden)

Yuan Lin

1999-01-01

Full Text Available Automatic parallelization is usually believed to be less effective at exploiting implicit parallelism in sparse/irregular programs than in their dense/regular counterparts. However, not much is really known because there have been few research reports on this topic. In this work, we have studied the possibility of using an automatic parallelizing compiler to detect the parallelism in sparse/irregular programs. The study with a collection of sparse/irregular programs led us to some common loop patterns. Based on these patterns new techniques were derived that produced good speedups when manually applied to our benchmark codes. More importantly, these parallelization methods can be implemented in a parallelizing compiler and can be applied automatically.

Split-Bregman-based sparse-view CT reconstruction

Energy Technology Data Exchange (ETDEWEB)

Vandeghinste, Bert; Vandenberghe, Stefaan [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Goossens, Bart; Pizurica, Aleksandra; Philips, Wilfried [Ghent Univ. (Belgium). Image Processing and Interpretation Research Group (IPI); Beenhouwer, Jan de [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Antwerp Univ., Wilrijk (Belgium). The Vision Lab; Staelens, Steven [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Antwerp Univ., Edegem (Belgium). Molecular Imaging Centre Antwerp

2011-07-01

Total variation minimization has been extensively researched for image denoising and sparse view reconstruction. These methods show superior denoising performance for simple images with little texture, but result in texture information loss when applied to more complex images. It could thus be beneficial to use other regularizers within medical imaging. We propose a general regularization method, based on a split-Bregman approach. We show results for this framework combined with a total variation denoising operator, in comparison to ASD-POCS. We show that sparse-view reconstruction and noise regularization is possible. This general method will allow us to investigate other regularizers in the context of regularized CT reconstruction, and decrease the acquisition times in {mu}CT. (orig.)

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation

Directory of Open Access Journals (Sweden)

Zutao Zhang

2016-02-01

Full Text Available In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT is adopted to extract the EEG power spectrum density (PSD. In this step, sparse representation classification combined with k-singular value decomposition (KSVD is firstly introduced in PSD to estimate the driver’s vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

A framework for general sparse matrix-matrix multiplication on GPUs and heterogeneous processors

DEFF Research Database (Denmark)

Liu, Weifeng; Vinter, Brian

2015-01-01

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method (AMG), breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM implementation has to handle...... extra irregularity from three aspects: (1) the number of nonzero entries in the resulting sparse matrix is unknown in advance, (2) very expensive parallel insert operations at random positions in the resulting sparse matrix dominate the execution time, and (3) load balancing must account for sparse data...... memory space and efficiently utilizes the very limited on-chip scratchpad memory. Parallel insert operations of the nonzero entries are implemented through the GPU merge path algorithm that is experimentally found to be the fastest GPU merge approach. Load balancing builds on the number of necessary...

Optimal Couple Projections for Domain Adaptive Sparse Representation-based Classification.

Science.gov (United States)

Zhang, Guoqing; Sun, Huaijiang; Porikli, Fatih; Liu, Yazhou; Sun, Quansen

2017-08-29

In recent years, sparse representation based classification (SRC) is one of the most successful methods and has been shown impressive performance in various classification tasks. However, when the training data has a different distribution than the testing data, the learned sparse representation may not be optimal, and the performance of SRC will be degraded significantly. To address this problem, in this paper, we propose an optimal couple projections for domain-adaptive sparse representation-based classification (OCPD-SRC) method, in which the discriminative features of data in the two domains are simultaneously learned with the dictionary that can succinctly represent the training and testing data in the projected space. OCPD-SRC is designed based on the decision rule of SRC, with the objective to learn coupled projection matrices and a common discriminative dictionary such that the between-class sparse reconstruction residuals of data from both domains are maximized, and the within-class sparse reconstruction residuals of data are minimized in the projected low-dimensional space. Thus, the resulting representations can well fit SRC and simultaneously have a better discriminant ability. In addition, our method can be easily extended to multiple domains and can be kernelized to deal with the nonlinear structure of data. The optimal solution for the proposed method can be efficiently obtained following the alternative optimization method. Extensive experimental results on a series of benchmark databases show that our method is better or comparable to many state-of-the-art methods.

Two-dimensional sparse wavenumber recovery for guided wavefields

Science.gov (United States)

Sabeti, Soroosh; Harley, Joel B.

2018-04-01

The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such as sparse wavenumber analysis (SWA), have been capable of recovering dispersion curves from limited data samples. A major limitation of SWA, however, is the assumption that the structure is isotropic. As a result, SWA fails when applied to composites and other anisotropic structures. There have been efforts to address this issue in the literature, but they either are not easily generalizable or do not sufficiently express the data. In this paper, we enhance the existing approaches by employing a two-dimensional wavenumber model to account for direction-dependent velocities in anisotropic media. We integrate this model with tools from compressive sensing to reconstruct a wavefield from incomplete data. Specifically, we create a modified two-dimensional orthogonal matching pursuit algorithm that takes an undersampled wavefield image, with specified unknown elements, and determines its sparse wavenumber characteristics. We then recover the entire wavefield from the sparse representations obtained with our small number of data samples.

Sparse matrix test collections

Energy Technology Data Exchange (ETDEWEB)

Duff, I.

1996-12-31

This workshop will discuss plans for coordinating and developing sets of test matrices for the comparison and testing of sparse linear algebra software. We will talk of plans for the next release (Release 2) of the Harwell-Boeing Collection and recent work on improving the accessibility of this Collection and others through the World Wide Web. There will only be three talks of about 15 to 20 minutes followed by a discussion from the floor.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

KAUST Repository

Labschutz, Matthias

2015-08-12

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

KAUST Repository

Labschutz, Matthias; Bruckner, Stefan; Groller, M. Eduard; Hadwiger, Markus; Rautek, Peter

2015-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure.

Science.gov (United States)

Labschütz, Matthias; Bruckner, Stefan; Gröller, M Eduard; Hadwiger, Markus; Rautek, Peter

2016-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

Directory of Open Access Journals (Sweden)

Qi Yuan(Alan

2010-01-01

Full Text Available Abstract The problem of uncovering transcriptional regulation by transcription factors (TFs based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ( status and Estrogen Receptor negative ( status, respectively.

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

Directory of Open Access Journals (Sweden)

Li Jun-Bao

2017-06-01

Full Text Available Magnetic Resonance Super-resolution Imaging Measurement (MRIM is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

Compact data structure and scalable algorithms for the sparse grid technique

KAUST Repository

Murarasu, Alin

2011-01-01

The sparse grid discretization technique enables a compressed representation of higher-dimensional functions. In its original form, it relies heavily on recursion and complex data structures, thus being far from well-suited for GPUs. In this paper, we describe optimizations that enable us to implement compression and decompression, the crucial sparse grid algorithms for our application, on Nvidia GPUs. The main idea consists of a bijective mapping between the set of points in a multi-dimensional sparse grid and a set of consecutive natural numbers. The resulting data structure consumes a minimum amount of memory. For a 10-dimensional sparse grid with approximately 127 million points, it consumes up to 30 times less memory than trees or hash tables which are typically used. Compared to a sequential CPU implementation, the speedups achieved on GPU are up to 17 for compression and up to 70 for decompression, respectively. We show that the optimizations are also applicable to multicore CPUs. Copyright © 2011 ACM.

Multisnapshot Sparse Bayesian Learning for DOA

DEFF Research Database (Denmark)

Gerstoft, Peter; Mecklenbrauker, Christoph F.; Xenaki, Angeliki

2016-01-01

The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source...

Continuous speech recognition with sparse coding

CSIR Research Space (South Africa)

Smit, WJ

2009-04-01

Full Text Available generative model. The spike train is classified by making use of a spike train model and dynamic programming. It is computationally expensive to find a sparse code. We use an iterative subset selection algorithm with quadratic programming for this process...

A density functional for sparse matter

DEFF Research Database (Denmark)

Langreth, D.C.; Lundqvist, Bengt; Chakarova-Kack, S.D.

2009-01-01

forces in molecules, to adsorbed molecules, like benzene, naphthalene, phenol and adenine on graphite, alumina and metals, to polymer and carbon nanotube (CNT) crystals, and hydrogen storage in graphite and metal-organic frameworks (MOFs), and to the structure of DNA and of DNA with intercalators......Sparse matter is abundant and has both strong local bonds and weak nonbonding forces, in particular nonlocal van der Waals (vdW) forces between atoms separated by empty space. It encompasses a broad spectrum of systems, like soft matter, adsorption systems and biostructures. Density-functional...... theory (DFT), long since proven successful for dense matter, seems now to have come to a point, where useful extensions to sparse matter are available. In particular, a functional form, vdW-DF (Dion et al 2004 Phys. Rev. Lett. 92 246401; Thonhauser et al 2007 Phys. Rev. B 76 125112), has been proposed...

Sparse learning of stochastic dynamical equations

Science.gov (United States)

Boninsegna, Lorenzo; Nüske, Feliks; Clementi, Cecilia

2018-06-01

With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process.

A novel method to design sparse linear arrays for ultrasonic phased array.

Science.gov (United States)

Yang, Ping; Chen, Bin; Shi, Ke-Ren

2006-12-22

In ultrasonic phased array testing, a sparse array can increase the resolution by enlarging the aperture without adding system complexity. Designing a sparse array involves choosing the best or a better configuration from a large number of candidate arrays. We firstly designed sparse arrays by using a genetic algorithm, but found that the arrays have poor performance and poor consistency. So, a method based on the Minimum Redundancy Linear Array was then adopted. Some elements are determined by the minimum-redundancy array firstly in order to ensure spatial resolution and then a genetic algorithm is used to optimize the remaining elements. Sparse arrays designed by this method have much better performance and consistency compared to the arrays designed only by a genetic algorithm. Both simulation and experiment confirm the effectiveness.

Multiple instance learning tracking method with local sparse representation

KAUST Repository

Xie, Chengjun

2013-10-01

When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

Nonlinear Algorithms for Channel Equalization and Map Symbol Detection.

Science.gov (United States)

Giridhar, K.

decision-feedback mechanism is introduced to truncate the channel memory "seen" by the MAPSD section. Also, simpler gradient-based updates for the channel estimates, and a metric pruning technique are used to further reduce the MAPSD complexity. Spatial diversity MAP combiners are developed to enhance the error rate performance and combat channel fading. As a first application of the MAPSD algorithm, dual-mode recovery techniques for TDMA (time-division multiple access) mobile radio signals are presented. Combined estimation of the symbol timing and the multipath parameters is proposed, using an auxiliary extended Kalman filter during the training cycle, and then tracking of the fading parameters is performed during the data cycle using the blind MAPSD algorithm. For the second application, a single-input receiver is employed to jointly recover cochannel narrowband signals. Assuming known channels, this two-stage joint MAPSD (JMAPSD) algorithm is compared to the optimal joint maximum likelihood sequence estimator, and to the joint decision-feedback detector. A blind MAPSD algorithm for the joint recovery of cochannel signals is also presented. Computer simulation results are provided to quantify the performance of the various algorithms proposed in this dissertation.

Sparse linear models: Variational approximate inference and Bayesian experimental design

International Nuclear Information System (INIS)

Seeger, Matthias W

2009-01-01

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

Sparse linear models: Variational approximate inference and Bayesian experimental design

Energy Technology Data Exchange (ETDEWEB)

Seeger, Matthias W [Saarland University and Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbruecken (Germany)

2009-12-01

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

Discriminative object tracking via sparse representation and online dictionary learning.

Science.gov (United States)

Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

2014-04-01

We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

Building Input Adaptive Parallel Applications: A Case Study of Sparse Grid Interpolation

KAUST Repository

Murarasu, Alin; Weidendorfer, Josef

2012-01-01

bring a substantial contribution to the speedup. By identifying common patterns in the input data, we propose new algorithms for sparse grid interpolation that accelerate the state-of-the-art non-specialized version. Sparse grid interpolation

Performance Analysis of Ultra-Wideband Channel for Short-Range Monopulse Radar at Ka-Band

Directory of Open Access Journals (Sweden)

Naohiko Iwakiri

2012-01-01

Full Text Available High-range resolution is inherently provided with Ka-band ultra-wideband (UWB vehicular radars. The authors have developed a prototype UWB monopulse radar equipped with a two-element receiving antenna array and reported its measurement results. In this paper, a more detailed verification using these measurements is presented. The measurements were analyzed employing matched filtering and eigendecomposition, and then multipath components were extracted to examine the behavior of received UWB monopulse signals. Next, conventional direction finding algorithms based on narrowband assumption were evaluated using the extracted multipath components, resulting in acceptable angle-of-arrival (AOA from the UWB monopulse signal regardless of wideband signals. Performance degradation due to a number of averaging the received monopulses was also examined to design suitable radar's waveforms.

Jointly-check iterative decoding algorithm for quantum sparse graph codes

International Nuclear Information System (INIS)

Jun-Hu, Shao; Bao-Ming, Bai; Wei, Lin; Lin, Zhou

2010-01-01

For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with a standard belief-propagation (BP) algorithm. In this paper, we present a jointly-check iterative algorithm suitable for decoding quantum sparse graph codes efficiently. Numerical simulations show that this modified method outperforms the standard BP algorithm with an obvious performance improvement. (general)

Rotational image deblurring with sparse matrices

DEFF Research Database (Denmark)

Hansen, Per Christian; Nagy, James G.; Tigkos, Konstantinos

2014-01-01

We describe iterative deblurring algorithms that can handle blur caused by a rotation along an arbitrary axis (including the common case of pure rotation). Our algorithms use a sparse-matrix representation of the blurring operation, which allows us to easily handle several different boundary...

Normalization for sparse encoding of odors by a wide-field interneuron.

Science.gov (United States)

Papadopoulou, Maria; Cassenaer, Stijn; Nowotny, Thomas; Laurent, Gilles

2011-05-06

Sparse coding presents practical advantages for sensory representations and memory storage. In the insect olfactory system, the representation of general odors is dense in the antennal lobes but sparse in the mushroom bodies, only one synapse downstream. In locusts, this transformation relies on the oscillatory structure of antennal lobe output, feed-forward inhibitory circuits, intrinsic properties of mushroom body neurons, and connectivity between antennal lobe and mushroom bodies. Here we show the existence of a normalizing negative-feedback loop within the mushroom body to maintain sparse output over a wide range of input conditions. This loop consists of an identifiable "giant" nonspiking inhibitory interneuron with ubiquitous connectivity and graded release properties.

Optimal concurrent access strategies in mobile communication networks

NARCIS (Netherlands)

Bhulai, S.; Hoekstra, G.; van der Mei, R.D.

2010-01-01

Current wireless channel capacities are closely approaching the theoretical limit. Hence, further capacity improvements from complex signal processing schemes may only gain modest improvements. Multi-path communication approaches, however, combine the benefits of higher performance and reliability

Sparse Representation Denoising for Radar High Resolution Range Profiling

Directory of Open Access Journals (Sweden)

Min Li

2014-01-01

Full Text Available Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile.

The Real-Valued Sparse Direction of Arrival (DOA Estimation Based on the Khatri-Rao Product

Directory of Open Access Journals (Sweden)

Tao Chen

2016-05-01

Full Text Available There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR product called the L1-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV model, and a new virtual overcomplete dictionary is constructed according to the KR product’s property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV. The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.

Integrative analysis of multiple diverse omics datasets by sparse group multitask regression

Directory of Open Access Journals (Sweden)

Dongdong eLin

2014-10-01

Full Text Available A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: 1 treat the biomarker identification in each single study as a task and then combine them by multitask learning; 2 group variables from all studies for identifying significant genes; 3 enforce sparse constraint on groups of variables to overcome the ‘small sample, but large variables’ problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed

A modified sparse reconstruction method for three-dimensional synthetic aperture radar image

Science.gov (United States)

Zhang, Ziqiang; Ji, Kefeng; Song, Haibo; Zou, Huanxin

2018-03-01

There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l0 norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l0 norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SL0 algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.

Simulation and design of omni-directional high speed multibeam transmitter system

Science.gov (United States)

Tang, Jaw-Luen; Jui, Ping-Chang; Wang, Sun-Chen

2006-09-01

For future high speed indoor wireless communication, diffuse wireless optical communications offer more robust optical links against shadowing than line-of-sight links. However, their performance may be degraded by multipath dispersion resulting from surface reflections. We have developed a multipath diffusive propagation model capable of providing channel impulse responses data. It is aimed to design and simulate any multi-beam transmitter under a variety of indoor environments. In this paper, a multi-beam transmitter system with semi-sphere structure is proposed to combat the diverse effects of multipath distortion albeit, at the cost of increased laser power and cost. Simulation results of multiple impulse responses showed that this type of multi-beam transmitter can significantly improve the performance of BER suitable for high bit rate application. We present the performance and simulation results for both line-of-sight and diffuse link configurations.

Porting of the DBCSR library for Sparse Matrix-Matrix Multiplications to Intel Xeon Phi systems

OpenAIRE

Bethune, Iain; Gloess, Andeas; Hutter, Juerg; Lazzaro, Alfio; Pabst, Hans; Reid, Fiona

2017-01-01

Multiplication of two sparse matrices is a key operation in the simulation of the electronic structure of systems containing thousands of atoms and electrons. The highly optimized sparse linear algebra library DBCSR (Distributed Block Compressed Sparse Row) has been specifically designed to efficiently perform such sparse matrix-matrix multiplications. This library is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. It is para...

Fast Solution in Sparse LDA for Binary Classification

Science.gov (United States)

Moghaddam, Baback

2010-01-01

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic

Punctuated sediment record resulting from channel migration in a shallow sand-dominated micro-tidal lagoon, Northern Wadden Sea, Denmark

DEFF Research Database (Denmark)

Fruergaard, M.; Andersen, T.J.; Nielsen, L.H.

2011-01-01

depositional environment, but tidal channel sediments dominate in the five sediment cores, making up 56% of the 15 mof sediment core. Sedimentation in the lagoon alternated between slow vertical aggradation of sand flats (1.5–2 mm yr-1) and very fast lateral progradation of point bars in tidal channels, which...... caused the formation of a punctuated lagoonal fill. Frequent and comprehensive reworking of the sand flat sediments by tidal channel migration entails loss of sedimentary structures and bioturbation related to sand flat deposits, and old sand flat sediments are only very sparsely preserved. We further...... conclude that long-term (millennial timescale) sediment accumulation in the lagoon was controlled by rising sea-level, whereas short-term (centurial timescale) sediment accumulation was controlled by local erosion and depositional events caused by lateral migration of channels. Records of short-term sea...

Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Chunhui Zhao

2015-09-01

Full Text Available Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-MN schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust.

Robust visual tracking via multiscale deep sparse networks

Science.gov (United States)

Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

2017-04-01

In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

Efficient MATLAB computations with sparse and factored tensors.

Energy Technology Data Exchange (ETDEWEB)

Bader, Brett William; Kolda, Tamara Gibson (Sandia National Lab, Livermore, CA)

2006-12-01

In this paper, the term tensor refers simply to a multidimensional or N-way array, and we consider how specially structured tensors allow for efficient storage and computation. First, we study sparse tensors, which have the property that the vast majority of the elements are zero. We propose storing sparse tensors using coordinate format and describe the computational efficiency of this scheme for various mathematical operations, including those typical to tensor decomposition algorithms. Second, we study factored tensors, which have the property that they can be assembled from more basic components. We consider two specific types: a Tucker tensor can be expressed as the product of a core tensor (which itself may be dense, sparse, or factored) and a matrix along each mode, and a Kruskal tensor can be expressed as the sum of rank-1 tensors. We are interested in the case where the storage of the components is less than the storage of the full tensor, and we demonstrate that many elementary operations can be computed using only the components. All of the efficiencies described in this paper are implemented in the Tensor Toolbox for MATLAB.

A network of spiking neurons for computing sparse representations in an energy-efficient way.

Science.gov (United States)

Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B

2012-11-01

Computing sparse redundant representations is an important problem in both applied mathematics and neuroscience. In many applications, this problem must be solved in an energy-efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating by low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, the operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We show that the numerical performance of HDA is on par with existing algorithms. In the asymptotic regime, the representation error of HDA decays with time, t, as 1/t. HDA is stable against time-varying noise; specifically, the representation error decays as 1/√t for gaussian white noise.

Sparse grid techniques for particle-in-cell schemes

Science.gov (United States)

Ricketson, L. F.; Cerfon, A. J.

2017-02-01

We propose the use of sparse grids to accelerate particle-in-cell (PIC) schemes. By using the so-called ‘combination technique’ from the sparse grids literature, we are able to dramatically increase the size of the spatial cells in multi-dimensional PIC schemes while paying only a slight penalty in grid-based error. The resulting increase in cell size allows us to reduce the statistical noise in the simulation without increasing total particle number. We present initial proof-of-principle results from test cases in two and three dimensions that demonstrate the new scheme’s efficiency, both in terms of computation time and memory usage.

Feature selection and multi-kernel learning for sparse representation on a manifold

KAUST Repository

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2014-01-01