Comparison of Pilot Symbol Embedded Channel Estimation Algorithms
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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.
A Fast Iterative Bayesian Inference Algorithm for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand; Manchón, Carles Navarro; Fleury, Bernard Henri
2013-01-01
In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of multipath components' gains with a hierarchical...
Non Minimum Phase Channel Estimation by Blind and Adaptive algorithms
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Zidane Mohammed
2015-09-01
Full Text Available In this paper the problem of the Non MinimumPhase (NMP channel identification using blind and adaptivealgorithms is theoretically and numerically evaluated in noiseenvironment case for different signal to noise ratio (SNR. Forthis problem, three blind algorithms based on Higher OrderCumulants (HOC versus adaptive algorithms, i.e. RecursiveLeast Squares (RLS and Least Mean Squares (LMS arepresented. In order to assess the performance of these approachesto identify the parameters of non minimum phase channels,we have selected the Macchi channel model. The simulationresults in noisy environment and for different data input channel,provided to illustrate the performance of the blind approachesand compare them with adaptive algorithms.
Pilot-based parametric channel estimation algorithm for DCO-OFDM-based visual light communications
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.
A Pilot-Pattern Based Algorithm for MIMO-OFDM Channel Estimation
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Guomin Li
2016-12-01
Full Text Available An improved pilot pattern algorithm for facilitating the channel estimation in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM systems is proposed in this paper. The presented algorithm reconfigures the parameter in the least square (LS algorithm, which belongs to the space-time block-coded (STBC category for channel estimation in pilot-based MIMO-OFDM system. Simulation results show that the algorithm has better performance in contrast to the classical single symbol scheme. In contrast to the double symbols scheme, the proposed algorithm can achieve nearly the same performance with only half of the complexity of the double symbols scheme.
A Novel OFDM Channel Estimation Algorithm with ICI Mitigation over Fast Fading Channels
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C. Tao
2010-06-01
Full Text Available Orthogonal frequency-division multiplexing (OFDM is well-known as a high-bit-rate transmission technique, but the Doppler frequency offset due to the high speed movement destroys the orthogonality of the subcarriers resulting in the intercarrier interference (ICI, and degrades the performance of the system at the same time. In this paper a novel OFDM channel estimation algorithm with ICI mitigation based on the ICI self-cancellation scheme is proposed. With this method, a more accurate channel estimation is obtained by comb-type double pilots and then ICI coefficients can be obtained to mitigate the ICI on each subcarrier under the assumption that the channel impulse response (CIR varies in a linear fashion. The theoretical analysis and simulation results show that the bit error rate (BER and spectral efficiency performances are improved significantly under high-speed mobility conditions (350 km/h – 500 km/h in comparison to ZHAO’s ICI self-cancellation scheme.
Channel estimation algorithm for interference suppression in IMDD-OQAM-OFDM transmission systems
Zhang, Lu; Xiao, Shilin; Bi, Meihua; Liu, Ling; Zhou, Zhao
2016-04-01
In this paper, we investigate the intrinsic interference caused by intra-symbol data and channel noise in the intensity-modulation direct-detection OQAM-OFDM (IMDD-OQAM-OFDM) system by theoretical derivation. Based on the analysis, we proposed and experimentally demonstrated a channel estimation algorithm with the combination of IAM-C and frequency-averaging method to combat the effect of these noises. Experimental results show that compared to the common channel estimation algorithms, our algorithm can greatly reduce the error vector magnitude (EVM) and achieve ~1.5 dB sensitivity improvement.
Liu, Beiyi; Gui, Guan; Xu, Li
2015-01-01
Least mean square (LMS) type adaptive algorithms have attracted much attention due to their low computational complexity. In the scenarios of sparse channel estimation, zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS) and reweighted -norm LMS (RL1-LMS) have been proposed to exploit channel sparsity. However, these proposed algorithms may hard to make tradeoff between convergence speed and estimation performance with only one step-size. To solve this problem, we propose three sparse i...
Directory of Open Access Journals (Sweden)
Yanyan Wang
2017-01-01
Full Text Available A norm combination penalized set-membership NLMS algorithm with l0 and l1 independently constrained, which is denoted as l0 and l1 independently constrained set-membership (SM NLMS (L0L1SM-NLMS algorithm, is presented for sparse adaptive multipath channel estimations. The L0L1SM-NLMS algorithm with fast convergence and small estimation error is implemented by independently exerting penalties on the channel coefficients via controlling the large group and small group channel coefficients which are implemented by l0 and l1 norm constraints, respectively. Additionally, a further improved L0L1SM-NLMS algorithm denoted as reweighted L0L1SM-NLMS (RL0L1SM-NLMS algorithm is presented via integrating a reweighting factor into our L0L1SM-NLMS algorithm to properly adjust the zero-attracting capabilities. Our developed RL0L1SM-NLMS algorithm provides a better estimation behavior than the presented L0L1SM-NLMS algorithm for implementing an estimation on sparse channels. The estimation performance of the L0L1SM-NLMS and RL0L1SM-NLMS algorithms is obtained for estimating sparse channels. The achieved simulation results show that our L0L1SM- and RL0L1SM-NLMS algorithms are superior to the traditional LMS, NLMS, SM-NLMS, ZA-LMS, RZA-LMS, and ZA-, RZA-, ZASM-, and RZASM-NLMS algorithms in terms of the convergence speed and steady-state performance.
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Xiaowen Wang
2002-08-01
Full Text Available Orthogonal frequency division multiplexing (OFDM is an effective technique for the future 3G communications because of its great immunity to impulse noise and intersymbol interference. The channel estimation is a crucial aspect in the design of OFDM systems. In this work, we propose a channel estimation algorithm based on a time-frequency polynomial model of the fading multipath channels. The algorithm exploits the correlation of the channel responses in both time and frequency domains and hence reduce more noise than the methods using only time or frequency polynomial model. The estimator is also more robust compared to the existing methods based on Fourier transform. The simulation shows that it has more than 5Ã¢Â€Â‰dB improvement in terms of mean-squared estimation error under some practical channel conditions. The algorithm needs little prior knowledge about the delay and fading properties of the channel. The algorithm can be implemented recursively and can adjust itself to follow the variation of the channel statistics.
Sparse Adaptive Channel Estimation Based on lp-Norm-Penalized Affine Projection Algorithm
Yingsong Li; Wenxing Li; Wenhua Yu; Jian Wan; Zhiwei Li
2014-01-01
We propose an lp-norm-penalized affine projection algorithm (LP-APA) for broadband multipath adaptive channel estimations. The proposed LP-APA is realized by incorporating an lp-norm into the cost function of the conventional affine projection algorithm (APA) to exploit the sparsity property of the broadband wireless multipath channel, by which the convergence speed and steady-state performance of the APA are significantly improved. The implementation of the LP-APA is equivalent to adding a z...
An improved channel estimation algorithm for CO-OFDM system and its performance analysis
Zhang, Shuai; Bai, Shun-chang; Bai, Cheng-lin; Luo, Qing-long; Fang, Wen-jing
2014-03-01
We present an extra processing added to conventional least square (LS) channel estimation to further improve its performance in coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. The influence of noise, chromatic dispersion and polarization mode dispersion on the performance of the proposed algorithm is analyzed. The simulation results show that the improved algorithm has better performance and lower complexity.
Sparse Adaptive Channel Estimation Based on lp-Norm-Penalized Affine Projection Algorithm
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Yingsong Li
2014-01-01
Full Text Available We propose an lp-norm-penalized affine projection algorithm (LP-APA for broadband multipath adaptive channel estimations. The proposed LP-APA is realized by incorporating an lp-norm into the cost function of the conventional affine projection algorithm (APA to exploit the sparsity property of the broadband wireless multipath channel, by which the convergence speed and steady-state performance of the APA are significantly improved. The implementation of the LP-APA is equivalent to adding a zero attractor to its iterations. The simulation results, which are obtained from a sparse channel estimation, demonstrate that the proposed LP-APA can efficiently improve channel estimation performance in terms of both the convergence speed and steady-state performance when the channel is exactly sparse.
Directory of Open Access Journals (Sweden)
Yingsong Li
2016-10-01
Full Text Available A robust sparse least-mean mixture-norm (LMMN algorithm is proposed, and its performance is appraised in the context of estimating a broadband multi-path wireless channel. The proposed algorithm is implemented via integrating a correntropy-induced metric (CIM penalty into the conventional LMMN algorithm to modify the basic cost function, which is denoted as the CIM-based LMMN (CIM-LMMN algorithm. The proposed CIM-LMMN algorithm is derived in detail within the kernel framework. The updating equation of CIM-LMMN can provide a zero attractor to attract the non-dominant channel coefficients to zeros, and it also gives a tradeoff between the sparsity and the estimation misalignment. Moreover, the channel estimation behavior is investigated over a broadband sparse multi-path wireless channel, and the simulation results are compared with the least mean square/fourth (LMS/F, least mean square (LMS, least mean fourth (LMF and the recently-developed sparse channel estimation algorithms. The channel estimation performance obtained from the designated sparse channel estimation demonstrates that the CIM-LMMN algorithm outperforms the recently-developed sparse LMMN algorithms and the relevant sparse channel estimation algorithms. From the results, we can see that our CIM-LMMN algorithm is robust and is superior to these mentioned algorithms in terms of both the convergence speed rate and the channel estimation misalignment for estimating a sparse channel.
Application of a Channel Estimation Algorithm to Spectrum Sensing in a Cognitive Radio Context
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Vincent Savaux
2014-01-01
Full Text Available This paper deals with spectrum sensing in an orthogonal frequency division multiplexing (OFDM context, allowing an opportunistic user to detect a vacant spectrum resource in a licensed band. The proposed method is based on an iterative algorithm used for the joint estimation of noise variance and frequency selective channel. It can be seen as a second-order detector, since it is performed by means of the minimum mean square error criterion. The main advantage of the proposed algorithm is its capability to perform spectrum sensing, noise variance estimation, and channel estimation in the presence of a signal. Furthermore, the sensing duration is limited to only one OFDM symbol. We theoretically show the convergence of the algorithm, and we derive its analytical detection and false alarm probabilities. Furthermore, we show that the detector is very efficient, even for low SNR values, and is robust against a channel uncertainty.
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
Fan, Tong-liang; Wen, Yu-cang; Kadri, Chaibou
Orthogonal frequency-division multiplexing (OFDM) is robust against frequency selective fading because of the increase of the symbol duration. However, the time-varying nature of the channel causes inter-carrier interference (ICI) which destroys the orthogonal of sub-carriers and degrades the system performance severely. To alleviate the detrimental effect of ICI, there is a need for ICI mitigation within one OFDM symbol. We propose an iterative Inter-Carrier Interference (ICI) estimation and cancellation technique for OFDM systems based on regularized constrained total least squares. In the proposed scheme, ICI aren't treated as additional additive white Gaussian noise (AWGN). The effect of Inter-Carrier Interference (ICI) and inter-symbol interference (ISI) on channel estimation is regarded as perturbation of channel. We propose a novel algorithm for channel estimation o based on regularized constrained total least squares. Computer simulations show that significant improvement can be obtained by the proposed scheme in fast fading channels.
Wolf, Michael
2012-01-01
A document describes an algorithm created to estimate the mass placed on a sample verification sensor (SVS) designed for lunar or planetary robotic sample return missions. A novel SVS measures the capacitance between a rigid bottom plate and an elastic top membrane in seven locations. As additional sample material (soil and/or small rocks) is placed on the top membrane, the deformation of the membrane increases the capacitance. The mass estimation algorithm addresses both the calibration of each SVS channel, and also addresses how to combine the capacitances read from each of the seven channels into a single mass estimate. The probabilistic approach combines the channels according to the variance observed during the training phase, and provides not only the mass estimate, but also a value for the certainty of the estimate. SVS capacitance data is collected for known masses under a wide variety of possible loading scenarios, though in all cases, the distribution of sample within the canister is expected to be approximately uniform. A capacitance-vs-mass curve is fitted to this data, and is subsequently used to determine the mass estimate for the single channel s capacitance reading during the measurement phase. This results in seven different mass estimates, one for each SVS channel. Moreover, the variance of the calibration data is used to place a Gaussian probability distribution function (pdf) around this mass estimate. To blend these seven estimates, the seven pdfs are combined into a single Gaussian distribution function, providing the final mean and variance of the estimate. This blending technique essentially takes the final estimate as an average of the estimates of the seven channels, weighted by the inverse of the channel s variance.
Savaux, Vincent
2014-01-01
This book presents an algorithm for the detection of an orthogonal frequency division multiplexing (OFDM) signal in a cognitive radio context by means of a joint and iterative channel and noise estimation technique. Based on the minimum mean square criterion, it performs an accurate detection of a user in a frequency band, by achieving a quasi-optimal channel and noise variance estimation if the signal is present, and by estimating the noise level in the band if the signal is absent. Organized into three chapters, the first chapter provides the background against which the system model is pr
A channel estimation algorithm for optical orthogonal frequency division multiplexing systems
Wang, Dong; Zou, Nian-yu; Wang, Wei-dong; Zhang, Ying-hai
2013-05-01
A simple channel estimation (CE) scheme, which is pilot-aided with just a little number of pilots inserted in the first half part of the subcarriers, is proposed and simulated for resisting the fiber dispersion in optical orthogonal frequency division multiplexing (OFDM) systems. The simulation results verify that the receiver sensitivity is improved by 2-3 dBm for bit error rate (BER) of 10-3 with the proposed CE algorithm than that with other kinds of CE algorithms based on linear square principle. The good constellation performance for a 40 Gbit/s transmission system can be also obtained by the proposed scheme.
An Off-Grid Turbo Channel Estimation Algorithm for Millimeter Wave Communications
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Lingyi Han
2016-09-01
Full Text Available The bandwidth shortage has motivated the exploration of the millimeter wave (mmWave frequency spectrum for future communication networks. To compensate for the severe propagation attenuation in the mmWave band, massive antenna arrays can be adopted at both the transmitter and receiver to provide large array gains via directional beamforming. To achieve such array gains, channel estimation (CE with high resolution and low latency is of great importance for mmWave communications. However, classic super-resolution subspace CE methods such as multiple signal classification (MUSIC and estimation of signal parameters via rotation invariant technique (ESPRIT cannot be applied here due to RF chain constraints. In this paper, an enhanced CE algorithm is developed for the off-grid problem when quantizing the angles of mmWave channel in the spatial domain where off-grid problem refers to the scenario that angles do not lie on the quantization grids with high probability, and it results in power leakage and severe reduction of the CE performance. A new model is first proposed to formulate the off-grid problem. The new model divides the continuously-distributed angle into a quantized discrete grid part, referred to as the integral grid angle, and an offset part, termed fractional off-grid angle. Accordingly, an iterative off-grid turbo CE (IOTCE algorithm is proposed to renew and upgrade the CE between the integral grid part and the fractional off-grid part under the Turbo principle. By fully exploiting the sparse structure of mmWave channels, the integral grid part is estimated by a soft-decoding based compressed sensing (CS method called improved turbo compressed channel sensing (ITCCS. It iteratively updates the soft information between the linear minimum mean square error (LMMSE estimator and the sparsity combiner. Monte Carlo simulations are presented to evaluate the performance of the proposed method, and the results show that it enhances the angle
An Off-Grid Turbo Channel Estimation Algorithm for Millimeter Wave Communications.
Han, Lingyi; Peng, Yuexing; Wang, Peng; Li, Yonghui
2016-09-22
The bandwidth shortage has motivated the exploration of the millimeter wave (mmWave) frequency spectrum for future communication networks. To compensate for the severe propagation attenuation in the mmWave band, massive antenna arrays can be adopted at both the transmitter and receiver to provide large array gains via directional beamforming. To achieve such array gains, channel estimation (CE) with high resolution and low latency is of great importance for mmWave communications. However, classic super-resolution subspace CE methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariant technique (ESPRIT) cannot be applied here due to RF chain constraints. In this paper, an enhanced CE algorithm is developed for the off-grid problem when quantizing the angles of mmWave channel in the spatial domain where off-grid problem refers to the scenario that angles do not lie on the quantization grids with high probability, and it results in power leakage and severe reduction of the CE performance. A new model is first proposed to formulate the off-grid problem. The new model divides the continuously-distributed angle into a quantized discrete grid part, referred to as the integral grid angle, and an offset part, termed fractional off-grid angle. Accordingly, an iterative off-grid turbo CE (IOTCE) algorithm is proposed to renew and upgrade the CE between the integral grid part and the fractional off-grid part under the Turbo principle. By fully exploiting the sparse structure of mmWave channels, the integral grid part is estimated by a soft-decoding based compressed sensing (CS) method called improved turbo compressed channel sensing (ITCCS). It iteratively updates the soft information between the linear minimum mean square error (LMMSE) estimator and the sparsity combiner. Monte Carlo simulations are presented to evaluate the performance of the proposed method, and the results show that it enhances the angle detection
Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki
2014-01-01
Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.
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Guan Gui
2014-01-01
Full Text Available Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO communication systems using orthogonal frequency division multiplexing (OFDM scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS algorithms were applied to adaptive sparse channel estimation (ACSE. It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE and bit error rate (BER metrics.
Peterson, Harold; Koshak, William J.
2009-01-01
An algorithm has been developed to estimate the altitude distribution of one-meter lightning channel segments. The algorithm is required as part of a broader objective that involves improving the lightning NOx emission inventories of both regional air quality and global chemistry/climate models. The algorithm was tested and applied to VHF signals detected by the North Alabama Lightning Mapping Array (NALMA). The accuracy of the algorithm was characterized by comparing algorithm output to the plots of individual discharges whose lengths were computed by hand; VHF source amplitude thresholding and smoothing were applied to optimize results. Several thousands of lightning flashes within 120 km of the NALMA network centroid were gathered from all four seasons, and were analyzed by the algorithm. The mean, standard deviation, and median statistics were obtained for all the flashes, the ground flashes, and the cloud flashes. One-meter channel segment altitude distributions were also obtained for the different seasons.
Message-Passing Algorithms for Channel Estimation and Decoding Using Approximate Inference
DEFF Research Database (Denmark)
Badiu, Mihai Alin; Kirkelund, Gunvor Elisabeth; Manchón, Carles Navarro
2012-01-01
We design iterative receiver schemes for a generic communication system by treating channel estimation and information decoding as an inference problem in graphical models. We introduce a recently proposed inference framework that combines belief propagation (BP) and the mean field (MF) approxima...
DEFF Research Database (Denmark)
Shutin, Dmitriy; Fleury, Bernard Henri
2011-01-01
the variational free energy, distributions of the multipath component parameters can be obtained instead of parameter point estimates and ii) the estimation of the number of relevant multipath components and the estimation of the component parameters are implemented jointly. The sparsity is achieved by defining......In this paper, we develop a sparse variational Bayesian (VB) extension of the space-alternating generalized expectation-maximization (SAGE) algorithm for the high resolution estimation of the parameters of relevant multipath components in the response of frequency and spatially selective wireless...
DEFF Research Database (Denmark)
Christensen, Lars P.B.; Larsen, Jan
2006-01-01
A general Variational Bayesian framework for iterative data and parameter estimation for coherent detection is introduced as a generalization of the EM-algorithm. Explicit solutions are given for MIMO channel estimation with Gaussian prior and noise covariance estimation with inverse-Wishart prior....... Simulation of a GSM-like system provides empirical proof that the VBEM-algorithm is able to provide better performance than the EM-algorithm. However, if the posterior distribution is highly peaked, the VBEM-algorithm approaches the EM-algorithm and the gain disappears. The potential gain is therefore...
Channel Estimation on the (EW RLS Algorithm Model of MIMO OFDM in Wireless Communication
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Sarnin Suzi Seroja
2016-01-01
(correspond to different mobility speeds and Monte Carlo simulations are performed and the MSE and BER performance versus SNR are obtained by averaging over 10000 channel realization. For comparisons, the BER performance is also presented for perfectly known channel at the receiver. In all the simulations, perfect synchronization between the transmitter and the receiver is assumed.
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....
Imperfect Channel State Estimation
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Tao Qin
2010-01-01
in a multiuser OFDM CR system. A simple back-off scheme is proposed, and simulation results are provided which show that the proposed scheme is very effective in mitigating the negative impact of channel estimation errors.
Bayesian Inference Methods for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand
2013-01-01
This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... complex prior representation achieve improved sparsity representations in low signalto- noise ratio as opposed to state-of-the-art sparse estimators. This result is of particular importance for the applicability of the algorithms in the field of channel estimation. We then derive various iterative...
Multisensor estimation: New distributed algorithms
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Plataniotis K. N.
1997-01-01
Full Text Available The multisensor estimation problem is considered in this paper. New distributed algorithms, which are able to locally process the information and which deliver identical results to those generated by their centralized counterparts are presented. The algorithms can be used to provide robust and computationally efficient solutions to the multisensor estimation problem. The proposed distributed algorithms are theoretically interesting and computationally attractive.
Efficient channel estimation in massive MIMO systems - a distributed approach
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.
The Extended-Window Channel Estimator for Iterative Channel-and-Symbol Estimation
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Barry John R
2005-01-01
Full Text Available The application of the expectation-maximization (EM algorithm to channel estimation results in a well-known iterative channel-and-symbol estimator (ICSE. The EM-ICSE iterates between a symbol estimator based on the forward-backward recursion (BCJR equalizer and a channel estimator, and may provide approximate maximum-likelihood blind or semiblind channel estimates. Nevertheless, the EM-ICSE has high complexity, and it is prone to misconvergence. In this paper, we propose the extended-window (EW estimator, a novel channel estimator for ICSE that can be used with any soft-output symbol estimator. Therefore, the symbol estimator may be chosen according to performance or complexity specifications. We show that the EW-ICSE, an ICSE that uses the EW estimator and the BCJR equalizer, is less complex and less susceptible to misconvergence than the EM-ICSE. Simulation results reveal that the EW-ICSE may converge faster than the EM-ICSE.
SDR Input Power Estimation Algorithms
Nappier, Jennifer M.; Briones, Janette C.
2013-01-01
The General Dynamics (GD) S-Band software defined radio (SDR) in the Space Communications and Navigation (SCAN) Testbed on the International Space Station (ISS) provides experimenters an opportunity to develop and demonstrate experimental waveforms in space. The SDR has an analog and a digital automatic gain control (AGC) and the response of the AGCs to changes in SDR input power and temperature was characterized prior to the launch and installation of the SCAN Testbed on the ISS. The AGCs were used to estimate the SDR input power and SNR of the received signal and the characterization results showed a nonlinear response to SDR input power and temperature. In order to estimate the SDR input from the AGCs, three algorithms were developed and implemented on the ground software of the SCAN Testbed. The algorithms include a linear straight line estimator, which used the digital AGC and the temperature to estimate the SDR input power over a narrower section of the SDR input power range. There is a linear adaptive filter algorithm that uses both AGCs and the temperature to estimate the SDR input power over a wide input power range. Finally, an algorithm that uses neural networks was designed to estimate the input power over a wide range. This paper describes the algorithms in detail and their associated performance in estimating the SDR input power.
SDR input power estimation algorithms
Briones, J. C.; Nappier, J. M.
The General Dynamics (GD) S-Band software defined radio (SDR) in the Space Communications and Navigation (SCAN) Testbed on the International Space Station (ISS) provides experimenters an opportunity to develop and demonstrate experimental waveforms in space. The SDR has an analog and a digital automatic gain control (AGC) and the response of the AGCs to changes in SDR input power and temperature was characterized prior to the launch and installation of the SCAN Testbed on the ISS. The AGCs were used to estimate the SDR input power and SNR of the received signal and the characterization results showed a nonlinear response to SDR input power and temperature. In order to estimate the SDR input from the AGCs, three algorithms were developed and implemented on the ground software of the SCAN Testbed. The algorithms include a linear straight line estimator, which used the digital AGC and the temperature to estimate the SDR input power over a narrower section of the SDR input power range. There is a linear adaptive filter algorithm that uses both AGCs and the temperature to estimate the SDR input power over a wide input power range. Finally, an algorithm that uses neural networks was designed to estimate the input power over a wide range. This paper describes the algorithms in detail and their associated performance in estimating the SDR input power.
Perceived Speech Quality Estimation Using DTW Algorithm
S. Arsenovski; Z. Gacovski; S. Chungurski; I. Kraljevski
2009-01-01
In this paper a method for speech quality estimation is evaluated by simulating the transfer of speech over packet switched and mobile networks. The proposed system uses Dynamic Time Warping algorithm for test and received speech comparison. Several tests have been made on a test speech sample of a single speaker with simulated packet (frame) loss effects on the perceived speech. The achieved results have been compared with measured PESQ values on the used transmission channel and their corre...
TWA-based channel estimation for CO-OFDM systems
Li, Li; Wu, Di; Han, Li; Hu, Gui-jun
2014-03-01
An efficient channel estimation method called time-domain weighted average (TWA) algorithm is proposed for coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. On the premise of calculating the associated weight of channel transfer function, a more exact channel characteristic is obtained by calculating the weighted average of the pilot transfer function in this algorithm. Compared with time-domain average (TA) algorithm, the TWA algorithm can reach the same bit error rate (BER) with fewer pilots, and it improves the performance of CO-OFDM systems.
D-BLAST OFDM with Channel Estimation
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Du Jianxuan
2004-01-01
Full Text Available Multiple-input and multiple-output (MIMO systems formed by multiple transmit and receive antennas can improve performance and increase capacity of wireless communication systems. Diagonal Bell Laboratories Layered Space-Time (D-BLAST structure offers a low-complexity solution for realizing the attractive capacity of MIMO systems. However, for broadband wireless communications, channel is frequency-selective and orthogonal frequency division multiplexing (OFDM has to be used with MIMO techniques to reduce system complexity. In this paper, we investigate D-BLAST for MIMO-OFDM systems. We develop a layerwise channel estimation algorithm which is robust to channel variation by exploiting the characteristic of the D-BLAST structure. Further improvement is made by subspace tracking to considerably reduce the error floor. Simulation results show that the layerwise estimators require 1 dB less signal-to-noise ratio (SNR than the traditional blockwise estimator for a word error rate (WER of when Doppler frequency is 40 Hz. Among the layerwise estimators, the subspace-tracking estimator provides a 0.8 dB gain for WER with 200 Hz Doppler frequency compared with the DFT-based estimator.
Distributive estimation of frequency selective channels for massive MIMO systems
Zaib, Alam
2015-12-28
We consider frequency selective channel estimation in the uplink of massive MIMO-OFDM systems, where our major concern is complexity. A low complexity distributed LMMSE algorithm is proposed that attains near optimal channel impulse response (CIR) estimates from noisy observations at receive antenna array. In proposed method, every antenna estimates the CIRs of its neighborhood followed by recursive sharing of estimates with immediate neighbors. At each step, every antenna calculates the weighted average of shared estimates which converges to near optimal LMMSE solution. The simulation results validate the near optimal performance of proposed algorithm in terms of mean square error (MSE). © 2015 EURASIP.
Multiple Parameter Estimation With Quantized Channel Output
Mezghani, Amine; Nossek, Josef A
2010-01-01
We present a general problem formulation for optimal parameter estimation based on quantized observations, with application to antenna array communication and processing (channel estimation, time-of-arrival (TOA) and direction-of-arrival (DOA) estimation). The work is of interest in the case when low resolution A/D-converters (ADCs) have to be used to enable higher sampling rate and to simplify the hardware. An Expectation-Maximization (EM) based algorithm is proposed for solving this problem in a general setting. Besides, we derive the Cramer-Rao Bound (CRB) and discuss the effects of quantization and the optimal choice of the ADC characteristic. Numerical and analytical analysis reveals that reliable estimation may still be possible even when the quantization is very coarse.
The adaptive channel estimation for STBC-OFDM systems
Özbek, Berna; Yılmaz, Reyat
2005-01-01
In this paper, we propose adaptive channel estimation methods based on LMS and RLS for orthogonal STBC-OFDM systems with three transmit antennas. The performance of the proposed algorithms is obtained in the frequency selective channels using Hiperlan/2 characteristics.
Cooperative Algorithms for MIMO Interference Channels
Peters, Steven W
2010-01-01
Interference alignment is a transmission technique for exploiting all available degrees of freedom in the interference channel with an arbitrary number of users. Most prior work on interference alignment, however, neglects interference from other nodes in the network not participating in the alignment operation. This paper proposes three generalizations of interference alignment for the multiple-antenna interference channel with multiple users that account for colored noise, which models uncoordinated interference. First, a minimum interference-plus-noise leakage algorithm is presented, and shown to be equivalent to previous subspace methods when noise is spatially white or negligible. A joint minimum mean squared error design is then proposed that jointly optimizes the transmit precoders and receive spatial filters, whereas previous designs neglect the receive spatial filter. This algorithm is shown to be a generalization of previous joint MMSE designs for other system configurations such as the broadcast ch...
Perceived Speech Quality Estimation Using DTW Algorithm
Directory of Open Access Journals (Sweden)
S. Arsenovski
2009-06-01
Full Text Available In this paper a method for speech quality estimation is evaluated by simulating the transfer of speech over packet switched and mobile networks. The proposed system uses Dynamic Time Warping algorithm for test and received speech comparison. Several tests have been made on a test speech sample of a single speaker with simulated packet (frame loss effects on the perceived speech. The achieved results have been compared with measured PESQ values on the used transmission channel and their correlation has been observed.
Nonlinear Algorithms for Channel Equalization and Map Symbol Detection.
Giridhar, K.
The transfer of information through a communication medium invariably results in various kinds of distortion to the transmitted signal. In this dissertation, a feed -forward neural network-based equalizer, and a family of maximum a posteriori (MAP) symbol detectors are proposed for signal recovery in the presence of intersymbol interference (ISI) and additive white Gaussian noise. The proposed neural network-based equalizer employs a novel bit-mapping strategy to handle multilevel data signals in an equivalent bipolar representation. It uses a training procedure to learn the channel characteristics, and at the end of training, the multilevel symbols are recovered from the corresponding inverse bit-mapping. When the channel characteristics are unknown and no training sequences are available, blind estimation of the channel (or its inverse) and simultaneous data recovery is required. Convergence properties of several existing Bussgang-type blind equalization algorithms are studied through computer simulations, and a unique gain independent approach is used to obtain a fair comparison of their rates of convergence. Although simple to implement, the slow convergence of these Bussgang-type blind equalizers make them unsuitable for many high data-rate applications. Rapidly converging blind algorithms based on the principle of MAP symbol-by -symbol detection are proposed, which adaptively estimate the channel impulse response (CIR) and simultaneously decode the received data sequence. Assuming a linear and Gaussian measurement model, the near-optimal blind MAP symbol detector (MAPSD) consists of a parallel bank of conditional Kalman channel estimators, where the conditioning is done on each possible data subsequence that can convolve with the CIR. This algorithm is also extended to the recovery of convolutionally encoded waveforms in the presence of ISI. Since the complexity of the MAPSD algorithm increases exponentially with the length of the assumed CIR, a suboptimal
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...... the estimation accuracy for the sparse channel, while achieving the same performance as the conventional subspace method when the channel is dense. Moreover, the proposed method enables us to estimate the channel response with unknown channel order if the channel is sparse enough....
LMS Based Adaptive Channel Estimation for LTE Uplink
Directory of Open Access Journals (Sweden)
Md. Masud Rana
2010-12-01
Full Text Available In this paper, a variable step size based least mean squares (LMS channel estimation (CE algorithm is presented for a single carrier frequency division multiple access(SC-FDMA system under the umbrella of the long term evolution (LTE. This unbiased CE method can automatically adapts the weighting coefficients on the channel condition. Therefore, it does not require knowledge of channel,and noise statistics. Furthermore, it uses a phase weighting scheme to eliminate the signal fluctuations due to noise and decision errors. Such approaches can guarantee the convergence towards the true channel coefficient. The mean and mean square behaviors of the proposed CE algorithm are also analyzed. With the help of theoretical analysis and simulation results, we prove that the proposed algorithm outperforms the existing algorithms in terms of mean square error (MSE and bit error rate (BER by more than around 2.5dB.
Channel Estimation in DCT-Based OFDM
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
Channel estimation in DCT-based OFDM.
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.
A channel estimation scheme for MIMO-OFDM systems
He, Chunlong; Tian, Chu; Li, Xingquan; Zhang, Ce; Zhang, Shiqi; Liu, Chaowen
2017-08-01
In view of the contradiction of the time-domain least squares (LS) channel estimation performance and the practical realization complexity, a reduced complexity channel estimation method for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) based on pilot is obtained. This approach can transform the complexity of MIMO-OFDM channel estimation problem into a simple single input single output-orthogonal frequency division multiplexing (SISO-OFDM) channel estimation problem and therefore there is no need for large matrix pseudo-inverse, which greatly reduces the complexity of algorithms. Simulation results show that the bit error rate (BER) performance of the obtained method with time orthogonal training sequences and linear minimum mean square error (LMMSE) criteria is better than that of time-domain LS estimator and nearly optimal performance.
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.
Parameter estimation in channel network flow simulation
Directory of Open Access Journals (Sweden)
Han Longxi
2008-03-01
Full Text Available Simulations of water flow in channel networks require estimated values of roughness for all the individual channel segments that make up a network. When the number of individual channel segments is large, the parameter calibration workload is substantial and a high level of uncertainty in estimated roughness cannot be avoided. In this study, all the individual channel segments are graded according to the factors determining the value of roughness. It is assumed that channel segments with the same grade have the same value of roughness. Based on observed hydrological data, an optimal model for roughness estimation is built. The procedure of solving the optimal problem using the optimal model is described. In a test of its efficacy, this estimation method was applied successfully in the simulation of tidal water flow in a large complicated channel network in the lower reach of the Yangtze River in China.
Multi-Channel Maximum Likelihood Pitch Estimation
DEFF Research Database (Denmark)
Christensen, Mads Græsbøll
2012-01-01
In this paper, a method for multi-channel pitch estimation is proposed. The method is a maximum likelihood estimator and is based on a parametric model where the signals in the various channels share the same fundamental frequency but can have different amplitudes, phases, and noise characteristi...
Application of Bayesian Hierarchical Prior Modeling to Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand; Manchón, Carles Navarro; Shutin, Dmitriy
2012-01-01
terms have proven to have strong sparsity-inducing properties. In this work, we design pilot assisted channel estimators for OFDM wireless receivers within the framework of sparse Bayesian learning by defining hierarchical Bayesian prior models that lead to sparsity-inducing penalization terms......Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood function and a penalization term proportional to the l1-norm of the parameter of interest. However, other penalization....... The estimators result as an application of the variational message-passing algorithm on the factor graph representing the signal model extended with the hierarchical prior models. Numerical results demonstrate the superior performance of our channel estimators as compared to traditional and state...
Adaptive link selection algorithms for distributed estimation
Xu, Songcen; de Lamare, Rodrigo C.; Poor, H. Vincent
2015-12-01
This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.
Song, Lijun; Lei, Xia; Jin, Maozhu; Lv, Zhihan
2015-12-01
In the high-speed railway wireless communication, a joint channel estimation and signal detection algorithm is proposed for the orthogonal frequency division multiplexing (OFDM) system without cyclic prefix in the doubly-selective fading channels. Our proposed method first combines the basis expansion model (BEM) and the inter symbol interference (ISI) cancellation to overcome the situation that exists with the fast time-varying channel and the normalized maximum multipath channel exceeding the length of the cyclic prefix (CP). At first, the channel estimation and signal detection can be approximated without considering the ISI. Then, the channel parameters and signal detection are updated through ISI cancellation and circular convolution reconstruction from the frequency domain. The simulations show the algorithm can improve the performance of channel estimation and signal detection.
DEFF Research Database (Denmark)
De Carvalho, Elisabeth; Omar, Samir; Slock, Dirk
2013-01-01
We analyze two algorithms that have been introduced previously for Deterministic Maximum Likelihood (DML) blind estimation of multiple FIR channels. The first one is a modification of the Iterative Quadratic ML (IQML) algorithm. IQML gives biased estimates of the channel and performs poorly at lo...
Multi-channel PSD Estimators for Speech Dereverberation
DEFF Research Database (Denmark)
Kuklasinski, Adam; Doclo, Simon; Gerkmann, Timo
2015-01-01
In this paper we perform an extensive theoretical and experimental comparison of two recently proposed multi-channel speech dereverberation algorithms. Both of them are based on the multi-channel Wiener filter but they use different estimators of the speech and reverberation power spectral...... distributions of the interference both estimators yield the same MSE. The theoretically derived MSE values are in good agreement with numerical simulation results and with instrumental speech quality measures in a realistic speech dereverberation task for binaural hearing aids....
Affine Projection Algorithm Using Regressive Estimated Error
Zhang, Shu; Zhi, Yongfeng
2011-01-01
An affine projection algorithm using regressive estimated error (APA-REE) is presented in this paper. By redefining the iterated error of the affine projection algorithm (APA), a new algorithm is obtained, and it improves the adaptive filtering convergence rate. We analyze the iterated error signal and the stability for the APA-REE algorithm. The steady-state weights of the APA-REE algorithm are proved to be unbiased and consist. The simulation results show that the proposed algorithm has a f...
Estimation of channel impulse response and FPGA simulation
Directory of Open Access Journals (Sweden)
YU Longjie
2015-02-01
Full Text Available Wideband code division multiple access (WCDMA is a 3G wireless communication network.The common pilot channel in downlink of WCDMA provides an effective method to estimate the channel impulse response.In this paper,universal software radio peripheral (USRP is utilized to sample and process WCDMA signal which is emitted by China Unicom base station.Firstly,the received signal is pre-processed with filtering and down-sampling.Secondly,fast algorithm of WCDMA cell search is fulfilled.Thirdly,frequency shift caused by USRP′s crystal oscillator is checked and compensated.Eventually,channel impulse response is estimated.In this paper,MATLAB is used to describe the above algorithm and field programmable gate array (FPGA is used to simulate algorithm.In the process of simulation,pipeline and IP core multiplexing are introduced.In the case of 32 MHz clock frequency,FPGA simulation time is 80.861 ms.Simulation results show that FPGA is able to estimate the channel impulse response quickly and accurately with less hardware resources.
DEFF Research Database (Denmark)
Zhang, Fengchun; Fan, Wei; Pedersen, Gert F.
2017-01-01
Beamforming algorithms are expected to be extensively utilized in mm-wave systems to improve system performance. In this paper, we discuss three different beamforming algorithms based on uniform circular arrays (UCAs), i.e. classicial beamfomer, coventional frequency invariant beamformer. Numeric...... simulation results and channel sounding measurement results at mm-wave are provided to demonstrate and compare the performance of the different beamformers in channel parameter estimation applications.......Beamforming algorithms are expected to be extensively utilized in mm-wave systems to improve system performance. In this paper, we discuss three different beamforming algorithms based on uniform circular arrays (UCAs), i.e. classicial beamfomer, coventional frequency invariant beamformer. Numerical...
OFDM pilot allocation for sparse channel estimation
Pakrooh, Pooria; Amini, Arash; Marvasti, Farokh
2012-12-01
In communication systems, efficient use of the spectrum is an indispensable concern. Recently the use of compressed sensing for the purpose of estimating orthogonal frequency division multiplexing (OFDM) sparse multipath channels has been proposed to decrease the transmitted overhead in form of the pilot subcarriers which are essential for channel estimation. In this article, we investigate the problem of deterministic pilot allocation in OFDM systems. The method is based on minimizing the coherence of the submatrix of the unitary discrete fourier transform (DFT) matrix associated with the pilot subcarriers. Unlike the usual case of equidistant pilot subcarriers, we show that non-uniform patterns based on cyclic difference sets are optimal. In cases where there are no difference sets, we perform a greedy method for finding a suboptimal solution. We also investigate the performance of the recovery methods such as orthogonal matching pursuit (OMP) and iterative method with adaptive thresholding (IMAT) for estimation of the channel taps.
Heuristic estimates in shortest path algorithms
W.H.L.M. Pijls (Wim)
2007-01-01
textabstractShortest path problems occupy an important position in operations research as well as in artificial intelligence. In this paper we study shortest path algorithms that exploit heuristic estimates. The well-known algorithms are put into one framework. Besides, we present an interesting
Heuristic estimates in shortest path algorithms
W.H.L.M. Pijls (Wim)
2006-01-01
textabstractShortest path problems occupy an important position in Operations Research as well as in Arti¯cial Intelligence. In this paper we study shortest path algorithms that exploit heuristic estimates. The well-known algorithms are put into one framework. Besides we present an interesting
An algorithm to design a multiple-channel communications system
Nguyen, Tien M.; Gevargiz, John M.; Hinedi, Sami
1991-01-01
The authors present a novel algorithm to design a phase-modulated residual carrier communications system for optimum performance. The algorithm has been developed as an aid in the design of two telemetry data channels operating simultaneously with a turn-around ranging signal. The algorithm provides the selection of an optimum subcarrier frequency and modulation indices for minimizing the mutual interferences among the channels to achieve desired link performance margins. The set of modulation indices provided by this algorithm will also achieve an optimum power division between the carrier, the ranging and the high data rate and low data rate channels. Finally, a software program using this algorithm has been developed for use with personal computers. This program provides the computation of the optimum subcarrier frequencies and modulation indices for a standard case that is recommended by the international Consultative Committee for Space Data Systems.
Efficient optimal joint channel estimation and data detection for massive MIMO systems
Alshamary, Haider Ali Jasim
2016-08-15
In this paper, we propose an efficient optimal joint channel estimation and data detection algorithm for massive MIMO wireless systems. Our algorithm is optimal in terms of the generalized likelihood ratio test (GLRT). For massive MIMO systems, we show that the expected complexity of our algorithm grows polynomially in the channel coherence time. Simulation results demonstrate significant performance gains of our algorithm compared with suboptimal non-coherent detection algorithms. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.
SQNR Estimation of Fixed-Point DSP Algorithms
Caffarena, Gabriel; Carreras, Carlos; López, Juan A.; Fernández, Ángel
2010-12-01
A fast and accurate quantization noise estimator aiming at fixed-point implementations of Digital Signal Processing (DSP) algorithms is presented. The estimator enables significant reduction in the computation time required to perform complex word-length optimizations. The proposed estimator is based on the use of Affine Arithmetic (AA) and it is presented in two versions: (i) a general version suitable for differentiable nonlinear algorithms, and Linear Time-Invariant (LTI) algorithms with and without feedbacks; and (ii) an LTI optimized version. The process relies on the parameterization of the statistical properties of the noise at the output of fixed-point algorithms. Once the output noise is parameterized (i.e., related to the fixed-point formats of the algorithm signals), a fast estimation can be applied throughout the word-length optimization process using as a precision metric the Signal-to-Quantization Noise Ratio (SQNR). The estimator is tested using different LTI filters and transforms, as well as a subset of non-linear operations, such as vector operations, adaptive filters, and a channel equalizer. Fixed-point optimization times are boosted by three orders of magnitude while keeping the average estimation error down to 4%.
Adaptive Algorithm for Chirp-Rate Estimation
Directory of Open Access Journals (Sweden)
Igor Djurović
2009-01-01
Full Text Available Chirp-rate, as a second derivative of signal phase, is an important feature of nonstationary signals in numerous applications such as radar, sonar, and communications. In this paper, an adaptive algorithm for the chirp-rate estimation is proposed. It is based on the confidence intervals rule and the cubic-phase function. The window width is adaptively selected to achieve good tradeoff between bias and variance of the chirp-rate estimate. The proposed algorithm is verified by simulations and the results show that it outperforms the standard algorithm with fixed window width.
A Developed ESPRIT Algorithm for DOA Estimation
Fayad, Youssef; Wang, Caiyun; Cao, Qunsheng; Hafez, Alaa El-Din Sayed
2015-05-01
A novel algorithm for estimating direction of arrival (DOAE) for target, which aspires to contribute to increase the estimation process accuracy and decrease the calculation costs, has been carried out. It has introduced time and space multiresolution in Estimation of Signal Parameter via Rotation Invariance Techniques (ESPRIT) method (TS-ESPRIT) to realize subspace approach that decreases errors caused by the model's nonlinearity effect. The efficacy of the proposed algorithm is verified by using Monte Carlo simulation, the DOAE accuracy has evaluated by closed-form Cramér-Rao bound (CRB) which reveals that the proposed algorithm's estimated results are better than those of the normal ESPRIT methods leading to the estimator performance enhancement.
Adaptive low-rank channel estimation for multi-band OFDM ultra-wideband communications
Directory of Open Access Journals (Sweden)
Hu Chia-Chang
2011-01-01
Full Text Available Abstract In this paper, an adaptive channel estimation scheme based on the reduced-rank (RR Wiener filtering (WF technique is proposed for multi-band (MB orthogonal frequency division multiplexing (OFDM ultra-wideband (UWB communication systems in multipath fading channels. This RR-WF-based algorithm employs an adaptive fuzzy-inference-controlled (FIC filter rank. Additionally, a comparative investigation into various channel estimation schemes is presented as well for MB-OFDM UWB communication systems. As a consequence, the FIC RR-WF channel estimation algorithm is capable of producing the bit-error-rate (BER performance similar to that of the full-rank WF channel estimator and superior than those of other interpolation-based channel estimation schemes.
Research on Channel Estimation and OFDM Signals Detection in Rapidly Time-Variant Channels
Directory of Open Access Journals (Sweden)
M. Huang
2014-09-01
Full Text Available It is well known that iterative channel estimation and OFDM signals detection can significantly improve the performance of communication system. However, its performance is poor due to the modelling error of basis expansion model (BEM being large enough and can not being ignored in rapidly time-variant channels. In this paper, channel estimation and OFDM signals detection are integrated into a real non-linear least squares (NLS problem. Then the modified Broyden-Fletcher-Goldfarb-Shanno (MBFGS algorithm is adopted to search the optimal solution. In addition, Cramer-Rao Bound (CRB for our proposed approach is derived. Simulation results are presented to illustrate the superiority of the proposed approach.
Reweighted l1-norm Penalized LMS for Sparse Channel Estimation and Its Analysis
Taheri, Omid; Vorobyov, Sergiy A.
2014-01-01
A new reweighted l1-norm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper. Since standard LMS algorithm does not take into account the sparsity information about the channel impulse response (CIR), sparsity-aware modifications of the LMS algorithm aim at outperforming the standard LMS by introducing a penalty term to the standard LMS cost function which forces the solution to be sparse. Our reweighted l1-norm penalized LMS algorit...
Remote optimal state estimation over communication channels with random delays
Mahmoud, Magdi S.
2014-01-22
This paper considers the optimal estimation of linear systems over unreliable communication channels with random delays. In this work, it is assumed that the system to be estimated is far away from the filter. The observations of the system are capsulized without time stamp and then transmitted to the network node at which the filter is located. The probabilities of time delays are assumed to be known. The event-driven estimation scheme is applied in this paper and the estimate of the states is updated only at each time instant when any measurement arrives. To capture the feature of communication, the system considered is augmented, and the arrived measurements are regarded as the uncertain observations of the augmented system. The corresponding optimal estimation algorithm is proposed and additionally, a numerical simulation represents the performance of this work. © 2014 The authors. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.
Channel estimation for OFDM system in atmospheric optical communication based on compressive sensing
Zhao, Qingsong; Hao, Shiqi; Geng, Hongjian; Sun, Han
2015-10-01
Orthogonal frequency division multiplexing (OFDM) technique applied to the atmospheric optical communication can improve data transmission rate, restrain pulse interference, and reduce effect of multipath caused by atmospheric scattering. Channel estimation, as one of the important modules in OFDM, has been investigated thoroughly and widely with great progress. In atmospheric optical communication system, channel estimation methods based on pilot are common approaches, such as traditional least-squares (LS) algorithm and minimum mean square error (MMSE) algorithm. However, sensitivity of the noise effects and high complexity of computation are shortcomings of LS algorithm and MMSE algorithm, respectively. Here, a new method based on compressive sensing is proposed to estimate the channel state information of atmospheric optical communication OFDM system, especially when the condition is closely associated with turbulence. Firstly, time-varying channel model is established under the condition of turbulence. Then, in consideration of multipath effect, sparse channel model is available for compressive sensing. And, the pilot signal is reconstructed with orthogonal matching tracking (OMP) algorithm, which is used for reconstruction. By contrast, the work of channel estimation is completed by LS algorithm as well. After that, simulations are conducted respectively in two different indexes -signal error rate (SER) and mean square error (MSE). Finally, result shows that compared with LS algorithm, the application of compressive sensing can improve the performance of SER and MSE. Theoretical analysis and simulation results show that the proposed method is reasonable and efficient.
OFDM pilot allocation for sparse channel estimation
Pakrooh, Pooria; Marvasti, Farrokh
2011-01-01
In communication systems, efficient use of the spectrum is an indispensable concern. Recently the use of compressed sensing for the purpose of estimating Orthogonal Frequency Division Multiplexing (OFDM) sparse multipath channels has been proposed to decrease the transmitted overhead in form of the pilot subcarriers which are essential for channel estimation. In this paper, we investigate the problem of deterministic pilot allocation in OFDM systems. The method is based on minimizing the coherence of the submatrix of the unitary Discrete Fourier Transform (DFT) matrix associated with the pilot subcarriers. Unlike the usual case of equidistant pilot subcarriers, we show that non-uniform patterns based on cyclic difference sets are optimal. In cases where there are no difference sets, we perform a greedy search method for finding a suboptimal solution. We also investigate the performance of the recovery methods such as Orthogonal Matching Pursuit (OMP) and Iterative Method with Adaptive Thresholding (IMAT) for ...
Estimation and Direct Equalization of Doubly Selective Channels
Barhumi, I.; Leus, G.; Moonen, M.
2006-01-01
We propose channel estimation and direct equalization techniques for transmission over doubly selective channels. The doubly selective channel is approximated using the basis expansion model (BEM). Linear and decision feedback equalizers implemented by time-varying finite impulse response (FIR)
Validation of Core Temperature Estimation Algorithm
2016-01-20
and risk of heat injury. An algorithm for estimating core temperature based on heart rate has been developed by others in order to avoid standard... risk of heat injury. Accepted standards for measuring core temperature include probes in the pulmonary artery, rectum, or esophagus, and an ingestible...temperature estimation from heart rate for first responders wearing different levels of personal protective equipment," Ergonomics , 2015. 8. J.M
Optimal state estimation over communication channels with random delays
Mahmoud, Magdi S.
2013-04-01
This paper is concerned with the optimal estimation of linear systems over unreliable communication channels with random delays. The measurements are delivered without time stamp, and the probabilities of time delays are assumed to be known. Since the estimation is time-driven, the actual time delays are converted into virtual time delays among the formulation. The receiver of estimation node stores the sum of arrived measurements between two adjacent processing time instants and also counts the number of arrived measurements. The original linear system is modeled as an extended system with uncertain observation to capture the feature of communication, then the optimal estimation algorithm of systems with uncertain observations is proposed. Additionally, a numerical simulation is presented to show the performance of this work. © 2013 The Franklin Institute.
Multidimensional rare event probability estimation algorithm
Directory of Open Access Journals (Sweden)
Leonidas Sakalauskas
2013-09-01
Full Text Available This work contains Monte–Carlo Markov Chain algorithm for estimation of multi-dimensional rare events frequencies. Logits of rare event likelihood we are modeling with Poisson distribution, which parameters are distributed by multivariate normal law with unknown parameters – mean vector and covariance matrix. The estimations of unknown parameters are calculated by the maximum likelihood method. There are equations derived, those must be satisfied with model’s maximum likelihood parameters estimations. Positive definition of evaluated covariance matrixes are controlled by calculating ratio between matrix maximum and minimum eigenvalues.
Energy-efficient power allocation of two-hop cooperative systems with imperfect channel estimation
Amin, Osama
2015-06-08
Recently, much attention has been paid to the green design of wireless communication systems using energy efficiency (EE) metrics that should capture all energy consumption sources to deliver the required data. In this paper, we formulate an accurate EE metric for cooperative two-hop systems that use the amplify-and-forward relaying scheme. Different from the existing research that assumes the availability of perfect channel state information (CSI) at the communication cooperative nodes, we assume a practical scenario, where training pilots are used to estimate the channels. The estimated CSI can be used to adapt the available resources of the proposed system in order to maximize the EE. Two estimation strategies are assumed namely disintegrated channel estimation, which assumes the availability of channel estimator at the relay, and cascaded channel estimation, where the relay is not equipped with channel estimator and only forwards the received pilot(s) in order to let the destination estimate the cooperative link. The channel estimation cost is reflected on the EE metric by including the estimation error in the signal-to-noise term and considering the energy consumption during the estimation phase. Based on the formulated EE metric, we propose an energy-aware power allocation algorithm to maximize the EE of the cooperative system with channel estimation. Furthermore, we study the impact of the estimation parameters on the optimized EE performance via simulation examples.
A Robust Threshold for Iterative Channel Estimation in OFDM Systems
Directory of Open Access Journals (Sweden)
A. Kalaycioglu
2010-04-01
Full Text Available A novel threshold computation method for pilot symbol assisted iterative channel estimation in OFDM systems is considered. As the bits are transmitted in packets, the proposed technique is based on calculating a particular threshold for each data packet in order to select the reliable decoder output symbols to improve the channel estimation performance. Iteratively, additional pilot symbols are established according to the threshold and the channel is re-estimated with the new pilots inserted to the known channel estimation pilot set. The proposed threshold calculation method for selecting additional pilots performs better than non-iterative channel estimation, no threshold and fixed threshold techniques in poor HF channel simulations.
Performance of Advanced Hybrid Link Adaptation Algorithms in Mobile Radio Channel
Directory of Open Access Journals (Sweden)
V. Psenak
2008-09-01
Full Text Available The fast power adaptation is essential for WCDMA based mobile radio networks, as 3G UMTS. Although the first version of UMTS has been released in 1999 (Release 99 evolution was not finished yet. Quality of Service (QoS and user data rate (e.g. HSDPA and HSUPA are continuously increasing from release to release. Even though link adaptation frequency (1500 times per second seems to be enough to span accidental fadings of mobile radio channel, used link adaptation algorithm is based on non-actual information about mobile radio channel state, which causes transmitter reaction delay to the actual channel state. Usage of appropriate prediction method to estimate near future channel state seems to be a valuable step to improve hybrid link adaptation algorithm. In this article we have described and simulated the new SIR-slot based advanced link adaptation algorithms. Algorithms were designed to increase efficiency of data transmission among a user equipment and base stations (uplink for different simulation environments (pedestrian channel with mobile subscriber speed 5 km/h, 15 km/h and vehicular channel with speed 45 km/h.
Shibli, Hussain J.
2013-06-01
Opportunistic schedulers rely on the feedback of all users in order to schedule a set of users with favorable channel conditions. While the downlink channels can be easily estimated at all user terminals via a single broadcast, several key challenges are faced during uplink transmission. First of all, the statistics of the noisy and fading feedback channels are unknown at the base station (BS) and channel training is usually required from all users. Secondly, the amount of network resources (air-time) required for feedback transmission grows linearly with the number of users. In this paper, we tackle the above challenges and propose a Bayesian based scheduling algorithm that 1) reduces the air-time required to identify the strong users, and 2) is agnostic to the statistics of the feedback channels and utilizes the a priori statistics of the additive noise to identify the strong users. Numerical results show that the proposed algorithm reduces the feedback air-time while improving detection in the presence of fading and noisy channels when compared to recent compressed sensing based algorithms. Furthermore, the proposed algorithm achieves a sum-rate throughput close to that obtained by noiseless dedicated feedback systems. © 2013 IEEE.
Adaptive OFDM and CDMA Algorithms for SISO and MIMO Channels
Che, H.
2005-01-01
The objective of this thesis is to study adaptive modulation algorithms for Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) channels. The adaptive modulation technique has the advantages of flexibility, providing high transmission quality and throughput; and it can lead
Angular Domain Data-Assisted Channel Estimation for Pilot Decontamination in Massive MIMO
Directory of Open Access Journals (Sweden)
Yihenew Beyene
2017-01-01
Full Text Available Massive Multiple-Input-Multiple-Output (M-MIMO system is a promising technology that offers to mobile networks substantial increase in throughput. In Time-Division Duplexing (TDD, the uplink training allows a Base Station (BS to acquire Channel State Information (CSI for both uplink reception and downlink transmission. This is essential for M-MIMO systems where downlink training pilots would consume large portion of the bandwidth. In densely populated areas, pilot symbols are reused among neighboring cells. Pilot contamination is the fundamental bottleneck on the performance of M-MIMO systems. Pilot contamination effect in antenna arrays can be mitigated by treating the channel estimation problem in angular domain where channel sparsity can be exploited. In this paper, we introduce a codebook that projects the channel into orthogonal beams and apply Minimum Mean-Squared Error (MMSE criterion to estimate the channel. We also propose data-aided channel covariance matrix estimation algorithm for angular domain MMSE channel estimator by exploiting properties of linear antenna array. The algorithm is based on simple linear operations and no matrix inversion is involved. Numerical results show that the algorithm performs well in mitigating pilot contamination where the desired channel and other interfering channels span overlapping angle-of-arrivals.
Improvement of Source Number Estimation Method for Single Channel Signal.
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Zhi Dong
Full Text Available Source number estimation methods for single channel signal have been investigated and the improvements for each method are suggested in this work. Firstly, the single channel data is converted to multi-channel form by delay process. Then, algorithms used in the array signal processing, such as Gerschgorin's disk estimation (GDE and minimum description length (MDL, are introduced to estimate the source number of the received signal. The previous results have shown that the MDL based on information theoretic criteria (ITC obtains a superior performance than GDE at low SNR. However it has no ability to handle the signals containing colored noise. On the contrary, the GDE method can eliminate the influence of colored noise. Nevertheless, its performance at low SNR is not satisfactory. In order to solve these problems and contradictions, the work makes remarkable improvements on these two methods on account of the above consideration. A diagonal loading technique is employed to ameliorate the MDL method and a jackknife technique is referenced to optimize the data covariance matrix in order to improve the performance of the GDE method. The results of simulation have illustrated that the performance of original methods have been promoted largely.
An adaptive hybrid ARQ algorithm for radio channels
Shacham, N.; Livne, A.
An hybrid automatic repeat request in which the transmitter can encode data packets by one of several codes is considered. An algorithm is presented by which the transmitter selects, based on the past transmissions and acknowledgments, the code for each packet. According to the algorithm, the transmitter assesses the channel conditions by sending packets encoded by high-rate code and changes to the higher rate whenever a probing packet is received successfully. Negative acknowledgments for packets at a given code rate causes the transmitter to switch to lower rate code. A probabilistic model to ascertain throughput of the algorithm is presented, and its numerical results show that it performs well under a wide range of channel error rates.
Single-Channel Blind Estimation of Reverberation Parameters
DEFF Research Database (Denmark)
Doire, C.S.J.; Brookes, M. D.; Naylor, P. A.
2015-01-01
The reverberation of an acoustic channel can be characterised by two frequency-dependent parameters: the reverberation time and the direct-to-reverberant energy ratio. This paper presents an algorithm for blindly determining these parameters from a single-channel speech signal. The algorithm uses...
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
Directory of Open Access Journals (Sweden)
Guan Gui
2014-01-01
Full Text Available 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.
Gui, Guan; 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.
Maximum-Likelihood Semiblind Equalization of Doubly Selective Channels Using the EM Algorithm
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Gideon Kutz
2010-01-01
Full Text Available Maximum-likelihood semi-blind joint channel estimation and equalization for doubly selective channels and single-carrier systems is proposed. We model the doubly selective channel as an FIR filter where each filter tap is modeled as a linear combination of basis functions. This channel description is then integrated in an iterative scheme based on the expectation-maximization (EM principle that converges to the channel description vector estimation. We discuss the selection of the basis functions and compare various functions sets. To alleviate the problem of convergence to a local maximum, we propose an initialization scheme to the EM iterations based on a small number of pilot symbols. We further derive a pilot positioning scheme targeted to reduce the probability of convergence to a local maximum. Our pilot positioning analysis reveals that for high Doppler rates it is better to spread the pilots evenly throughout the data block (and not to group them even for frequency-selective channels. The resulting equalization algorithm is shown to be superior over previously proposed equalization schemes and to perform in many cases close to the maximum-likelihood equalizer with perfect channel knowledge. Our proposed method is also suitable for coded systems and as a building block for Turbo equalization algorithms.
Energy-balanced algorithm for RFID estimation
Zhao, Jumin; Wang, Fangyuan; Li, Dengao; Yan, Lijuan
2016-10-01
RFID has been widely used in various commercial applications, ranging from inventory control, supply chain management to object tracking. It is necessary for us to estimate the number of RFID tags deployed in a large area periodically and automatically. Most of the prior works use passive tags to estimate and focus on designing time-efficient algorithms that can estimate tens of thousands of tags in seconds. But for a RFID reader to access tags in a large area, active tags are likely to be used due to their longer operational ranges. But these tags use their own battery as energy supplier. Hence, conserving energy for active tags becomes critical. Some prior works have studied how to reduce energy expenditure of a RFID reader when it reads tags IDs. In this paper, we study how to reduce the amount of energy consumed by active tags during the process of estimating the number of tags in a system and make the energy every tag consumed balanced approximately. We design energy-balanced estimation algorithm that can achieve our goal we mentioned above.
the simple mono-canal algorithm for the temperature estimating of ...
African Journals Online (AJOL)
30 juin 2010 ... In this study we have developed an algorithm mono-canal to estimate land surface temperature (Ts) in spectral band as the infrared channel (IR) of METEOSAT-7. This algorithm permits us to join by a relationship of second order the surface temperature to the brightness temperature (Tb) at the sensor level.
A decentralized scheduling algorithm for time synchronized channel hopping
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Andrew Tinka
2011-09-01
Full Text Available Time Synchronized Channel Hopping (TSCH is an existing Medium Access Control scheme which enables robust communication through channel hopping and high data rates through synchronization. It is based on a time-slotted architecture, and its correct functioning depends on a schedule which is typically computed by a central node. This paper presents, to our knowledge, the first scheduling algorithm for TSCH networks which both is distributed and which copes with mobile nodes. Two variations on scheduling algorithms are presented. Aloha-based scheduling allocates one channel for broadcasting advertisements for new neighbors. Reservation- based scheduling augments Aloha-based scheduling with a dedicated timeslot for targeted advertisements based on gossip information. A mobile ad hoc motorized sensor network with frequent connectivity changes is studied, and the performance of the two proposed algorithms is assessed. This performance analysis uses both simulation results and the results of a field deployment of floating wireless sensors in an estuarial canal environment. Reservation-based scheduling performs significantly better than Aloha-based scheduling, suggesting that the improved network reactivity is worth the increased algorithmic complexity and resource consumption.
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Meher Krishna Patel
2017-01-01
Full Text Available This paper presents the antijamming performance of adaptive multiuser DS-CDMA system using maximal ratio combining technique in slow switching pulse noise jammer. Complex flat fading channel coefficients are assumed to be unknown at the receiver. Pilot symbols are used to estimate the fading coefficients using least mean square (LMS algorithm, and effect of imperfect channel estimation on bit error rate (BER is studied. Under the perfect synchronization conditions, probability of error in closed form is derived.
Conveyance estimation in channels with emergent bank vegetation ...
African Journals Online (AJOL)
Emergent vegetation along the banks of a river channel influences its conveyance considerably. The total channel discharge can be estimated as the sum of the discharges of the vegetated and clear channel zones calculated separately. The vegetated zone discharge is often negligible, but can be estimated using ...
Single-channel mixed signal blind source separation algorithm based on multiple ICA processing
Cheng, Xiefeng; Li, Ji
2017-01-01
Take separating the fetal heart sound signal from the mixed signal that get from the electronic stethoscope as the research background, the paper puts forward a single-channel mixed signal blind source separation algorithm based on multiple ICA processing. Firstly, according to the empirical mode decomposition (EMD), the single-channel mixed signal get multiple orthogonal signal components which are processed by ICA. The multiple independent signal components are called independent sub component of the mixed signal. Then by combining with the multiple independent sub component into single-channel mixed signal, the single-channel signal is expanded to multipath signals, which turns the under-determined blind source separation problem into a well-posed blind source separation problem. Further, the estimate signal of source signal is get by doing the ICA processing. Finally, if the separation effect is not very ideal, combined with the last time's separation effect to the single-channel mixed signal, and keep doing the ICA processing for more times until the desired estimated signal of source signal is get. The simulation results show that the algorithm has good separation effect for the single-channel mixed physiological signals.
Message-Passing Receiver Architecture With Reduced-Complexity Channel Estimation
DEFF Research Database (Denmark)
Badiu, Mihai Alin; Manchón, Carles Navarro; Fleury, Bernard Henri
2013-01-01
We propose an iterative receiver architecture which allows for adjusting the complexity of estimating the channel frequency response in OFDM systems. This is achieved by approximating the exact Gaussian channel model assumed in the system with a Markov model whose state-space dimension is a design...... parameter. We apply an inference framework combining belief propagation and the mean field approximation to a probabilistic model of the system which includes the approximate channel model. By doing so, we obtain a receiver algorithm with adjustable complexity which jointly performs channel and noise...... precision estimation, equalization and decoding. Simulation results show that low-complexity versions of the algorithm - obtained by selecting low state-space dimensions – can closely attain the performance of a receiver devised based on the exact channel model....
A Demosaicking Algorithm with Adaptive Inter-Channel Correlation
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Joan Duran
2015-12-01
Full Text Available Most common cameras use a CCD sensor device measuring a single color per pixel. Demosaicking is the interpolation process by which one can infer a full color image from such a matrix of values, thus interpolating the two missing components per pixel. Most demosaicking methods take advantage of inter-channel correlation locally selecting the best interpolation direction. The obtained results look convincing except when local geometry cannot be inferred from neighboring pixels or channel correlation is low. In these cases, these algorithms create interpolation artifacts such as zipper effect or color aliasing. This paper discusses the implementation details of the algorithm proposed in [J. Duran, A. Buades, ``Self-Similarity and Spectral Correlation Adaptive Algorithm for Color Demosaicking'', IEEE Transactions on Image Processing, 23(9, pp. 4031--4040, 2014]. The proposed method involves nonlocal image self-similarity in order to reduce interpolation artifacts when local geometry is ambiguous. It further introduces a clear and intuitive manner of balancing how much channel-correlation must be taken advantage of.
A Channelization-Based DOA Estimation Method for Wideband Signals.
Guo, Rui; Zhang, Yue; Lin, Qianqiang; Chen, Zengping
2016-07-04
In this paper, we propose a novel direction of arrival (DOA) estimation method for wideband signals with sensor arrays. The proposed method splits the wideband array output into multiple frequency sub-channels and estimates the signal parameters using a digital channelization receiver. Based on the output sub-channels, a channelization-based incoherent signal subspace method (Channelization-ISM) and a channelization-based test of orthogonality of projected subspaces method (Channelization-TOPS) are proposed. Channelization-ISM applies narrowband signal subspace methods on each sub-channel independently. Then the arithmetic mean or geometric mean of the estimated DOAs from each sub-channel gives the final result. Channelization-TOPS measures the orthogonality between the signal and the noise subspaces of the output sub-channels to estimate DOAs. The proposed channelization-based method isolates signals in different bandwidths reasonably and improves the output SNR. It outperforms the conventional ISM and TOPS methods on estimation accuracy and dynamic range, especially in real environments. Besides, the parallel processing architecture makes it easy to implement on hardware. A wideband digital array radar (DAR) using direct wideband radio frequency (RF) digitization is presented. Experiments carried out in a microwave anechoic chamber with the wideband DAR are presented to demonstrate the performance. The results verify the effectiveness of the proposed method.
A Channelization-Based DOA Estimation Method for Wideband Signals
Guo, Rui; Zhang, Yue; Lin, Qianqiang; Chen, Zengping
2016-01-01
In this paper, we propose a novel direction of arrival (DOA) estimation method for wideband signals with sensor arrays. The proposed method splits the wideband array output into multiple frequency sub-channels and estimates the signal parameters using a digital channelization receiver. Based on the output sub-channels, a channelization-based incoherent signal subspace method (Channelization-ISM) and a channelization-based test of orthogonality of projected subspaces method (Channelization-TOPS) are proposed. Channelization-ISM applies narrowband signal subspace methods on each sub-channel independently. Then the arithmetic mean or geometric mean of the estimated DOAs from each sub-channel gives the final result. Channelization-TOPS measures the orthogonality between the signal and the noise subspaces of the output sub-channels to estimate DOAs. The proposed channelization-based method isolates signals in different bandwidths reasonably and improves the output SNR. It outperforms the conventional ISM and TOPS methods on estimation accuracy and dynamic range, especially in real environments. Besides, the parallel processing architecture makes it easy to implement on hardware. A wideband digital array radar (DAR) using direct wideband radio frequency (RF) digitization is presented. Experiments carried out in a microwave anechoic chamber with the wideband DAR are presented to demonstrate the performance. The results verify the effectiveness of the proposed method. PMID:27384566
A Channelization-Based DOA Estimation Method for Wideband Signals
Directory of Open Access Journals (Sweden)
Rui Guo
2016-07-01
Full Text Available In this paper, we propose a novel direction of arrival (DOA estimation method for wideband signals with sensor arrays. The proposed method splits the wideband array output into multiple frequency sub-channels and estimates the signal parameters using a digital channelization receiver. Based on the output sub-channels, a channelization-based incoherent signal subspace method (Channelization-ISM and a channelization-based test of orthogonality of projected subspaces method (Channelization-TOPS are proposed. Channelization-ISM applies narrowband signal subspace methods on each sub-channel independently. Then the arithmetic mean or geometric mean of the estimated DOAs from each sub-channel gives the final result. Channelization-TOPS measures the orthogonality between the signal and the noise subspaces of the output sub-channels to estimate DOAs. The proposed channelization-based method isolates signals in different bandwidths reasonably and improves the output SNR. It outperforms the conventional ISM and TOPS methods on estimation accuracy and dynamic range, especially in real environments. Besides, the parallel processing architecture makes it easy to implement on hardware. A wideband digital array radar (DAR using direct wideband radio frequency (RF digitization is presented. Experiments carried out in a microwave anechoic chamber with the wideband DAR are presented to demonstrate the performance. The results verify the effectiveness of the proposed method.
Detection-Guided Fast Affine Projection Channel Estimator for Speech Applications
Directory of Open Access Journals (Sweden)
Homer John
2007-01-01
Full Text Available In various adaptive estimation applications, such as acoustic echo cancellation within teleconferencing systems, the input signal is a highly correlated speech. This, in general, leads to extremely slow convergence of the NLMS adaptive FIR estimator. As a result, for such applications, the affine projection algorithm (APA or the low-complexity version, the fast affine projection (FAP algorithm, is commonly employed instead of the NLMS algorithm. In such applications, the signal propagation channel may have a relatively low-dimensional impulse response structure, that is, the number m of active or significant taps within the (discrete-time modelled channel impulse response is much less than the overall tap length n of the channel impulse response. For such cases, we investigate the inclusion of an active-parameter detection-guided concept within the fast affine projection FIR channel estimator. Simulation results indicate that the proposed detection-guided fast affine projection channel estimator has improved convergence speed and has lead to better steady-state performance than the standard fast affine projection channel estimator, especially in the important case of highly correlated speech input signals.
Detection-Guided Fast Affine Projection Channel Estimator for Speech Applications
Directory of Open Access Journals (Sweden)
Yan Wu Jennifer
2007-04-01
Full Text Available In various adaptive estimation applications, such as acoustic echo cancellation within teleconferencing systems, the input signal is a highly correlated speech. This, in general, leads to extremely slow convergence of the NLMS adaptive FIR estimator. As a result, for such applications, the affine projection algorithm (APA or the low-complexity version, the fast affine projection (FAP algorithm, is commonly employed instead of the NLMS algorithm. In such applications, the signal propagation channel may have a relatively low-dimensional impulse response structure, that is, the number m of active or significant taps within the (discrete-time modelled channel impulse response is much less than the overall tap length n of the channel impulse response. For such cases, we investigate the inclusion of an active-parameter detection-guided concept within the fast affine projection FIR channel estimator. Simulation results indicate that the proposed detection-guided fast affine projection channel estimator has improved convergence speed and has lead to better steady-state performance than the standard fast affine projection channel estimator, especially in the important case of highly correlated speech input signals.
Efficient collaborative sparse channel estimation in massive MIMO
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.
A global algorithm for estimating Absolute Salinity
Directory of Open Access Journals (Sweden)
T. J. McDougall
2012-12-01
Full Text Available The International Thermodynamic Equation of Seawater – 2010 has defined the thermodynamic properties of seawater in terms of a new salinity variable, Absolute Salinity, which takes into account the spatial variation of the composition of seawater. Absolute Salinity more accurately reflects the effects of the dissolved material in seawater on the thermodynamic properties (particularly density than does Practical Salinity.
When a seawater sample has standard composition (i.e. the ratios of the constituents of sea salt are the same as those of surface water of the North Atlantic, Practical Salinity can be used to accurately evaluate the thermodynamic properties of seawater. When seawater is not of standard composition, Practical Salinity alone is not sufficient and the Absolute Salinity Anomaly needs to be estimated; this anomaly is as large as 0.025 g kg^{−1} in the northernmost North Pacific. Here we provide an algorithm for estimating Absolute Salinity Anomaly for any location (x, y, p in the world ocean.
To develop this algorithm, we used the Absolute Salinity Anomaly that is found by comparing the density calculated from Practical Salinity to the density measured in the laboratory. These estimates of Absolute Salinity Anomaly however are limited to the number of available observations (namely 811. In order to provide a practical method that can be used at any location in the world ocean, we take advantage of approximate relationships between Absolute Salinity Anomaly and silicate concentrations (which are available globally.
A Low-Complexity KL Expansion-Based Channel Estimator for OFDM Systems
Directory of Open Access Journals (Sweden)
Şenol Habib
2005-01-01
Full Text Available This paper first proposes a computationally efficient, pilot-aided linear minimum mean square error (MMSE batch channel estimation algorithm for OFDM systems in unknown wireless fading channels. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion, no matrix inversion is required in the proposed MMSE estimator. Moreover, optimal rank reduction is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We then consider the stochastic Cramér-Rao bound and derive the closed-form expression for the random KL coefficients and consequently exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also analyze the effect of a modelling mismatch on the estimator performance. To further reduce the complexity, we extend the batch linear MMSE to the sequential linear MMSE estimator. With the fast convergence property and the simple structure, the sequential linear MMSE estimator provides an attractive alternative to the implementation of channel estimator.
Directory of Open Access Journals (Sweden)
Hongjun Xu
2011-07-01
Full Text Available A channel and delay estimation algorithm for both positive and negative delay, based on the distributed Alamouti scheme, has been recently discussed for base-station–based asynchronous cooperative systems in frequency-flat fading channels. This paper extends the algorithm, the maximum likelihood estimator, to work in frequency-selective fading channels. The minimum mean square error (MMSE performance of channel estimation for both packet schemes and normal schemes is discussed in this paper. The symbol error rate (SER performance of equalisation and detection for both time-reversal space-time block code (STBC and single-carrier STBC is also discussed in this paper. The MMSE simulation results demonstrated the superior performance of the packet scheme over the normal scheme with an improvement in performance of up to 6 dB when feedback was used in the frequency-selective channel at a MSE of 3 x 10^{–2}. The SER simulation results showed that, although both the normal and packet schemes achieved similar diversity orders, the packet scheme demonstrated a 1 dB coding gain over the normal scheme at a SER of 10^{–5}. Finally, the SER simulations showed that the frequency-selective fading system outperformed the frequency-flat fading system.
Estimation of time-varying channels - A block approach
Leus, G.; Tang, Z.; Banelli, P.
2011-01-01
Channel state information (CSI) is indispensable for coherent detection in a wireless communication system. The pilot-aided method is one of the most intensively studied approaches for channel estimation. This method is especially attractive for time-varying channels because of their short coherence
Indoor positioning system using WLAN channel estimates as fingerprints for mobile devices
Schmidt, Erick; Akopian, David
2015-03-01
With the growing integration of location based services (LBS) such as GPS in mobile devices, indoor position systems (IPS) have become an important role for research. There are several IPS methods such as AOA, TOA, TDOA, which use trilateration for indoor location estimation but are generally based on line-of-sight. Other methods rely on classification such as fingerprinting which uses WLAN indoor signals. This paper re-examines the classical WLAN fingerprinting accuracy which uses received signal strength (RSS) measurements by introducing channel estimates for improvements in the classification of indoor locations. The purpose of this paper is to improve existing classification algorithms used in fingerprinting by introducing channel estimates when there are a low number of APs available. The channel impulse response, or in this case the channel estimation from the receiver, should characterize a complex indoor area which usually has multipath, thus providing a unique signature for each location which proves useful for better pattern recognition. In this experiment, channel estimates are extracted from a Software-Defined Radio (SDR) environment, thus exploiting the benefits of SDR from a NI-USRP model and LabVIEW software. Measurements are taken from a known building, and several scenarios with one and two access points (APs) are used in this experiment. Also, three granularities in distance between locations are analyzed. A Support Vector Machine (SVM) is used as the algorithm for pattern recognition of different locations based on the samples taken from RSS and channel estimation coefficients.
Propagation channel characterization, parameter estimation, and modeling for wireless communications
Yin, Xuefeng
2016-01-01
Thoroughly covering channel characteristics and parameters, this book provides the knowledge needed to design various wireless systems, such as cellular communication systems, RFID and ad hoc wireless communication systems. It gives a detailed introduction to aspects of channels before presenting the novel estimation and modelling techniques which can be used to achieve accurate models. To systematically guide readers through the topic, the book is organised in three distinct parts. The first part covers the fundamentals of the characterization of propagation channels, including the conventional single-input single-output (SISO) propagation channel characterization as well as its extension to multiple-input multiple-output (MIMO) cases. Part two focuses on channel measurements and channel data post-processing. Wideband channel measurements are introduced, including the equipment, technology and advantages and disadvantages of different data acquisition schemes. The channel parameter estimation methods are ...
Degenerated-Inverse-Matrix-Based Channel Estimation for OFDM Systems
Directory of Open Access Journals (Sweden)
Yoshida Makoto
2009-01-01
Full Text Available Abstract This paper addresses time-domain channel estimation for pilot-symbol-aided orthogonal frequency division multiplexing (OFDM systems. By using a cyclic sinc-function matrix uniquely determined by transmitted subcarriers, the performance of our proposed scheme approaches perfect channel state information (CSI, within a maximum of 0.4 dB degradation, regardless of the delay spread of the channel, Doppler frequency, and subcarrier modulation. Furthermore, reducing the matrix size by splitting the dispersive channel impulse response into clusters means that the degenerated inverse matrix estimator (DIME is feasible for broadband, high-quality OFDM transmission systems. In addition to theoretical analysis on normalized mean squared error (NMSE performance of DIME, computer simulations over realistic nonsample spaced channels also showed that the DIME is robust for intersymbol interference (ISI channels and fast time-invariant channels where a minimum mean squared error (MMSE estimator does not work well.
Degenerated-Inverse-Matrix-Based Channel Estimation for OFDM Systems
Directory of Open Access Journals (Sweden)
Makoto Yoshida
2009-01-01
Full Text Available This paper addresses time-domain channel estimation for pilot-symbol-aided orthogonal frequency division multiplexing (OFDM systems. By using a cyclic sinc-function matrix uniquely determined by Nc transmitted subcarriers, the performance of our proposed scheme approaches perfect channel state information (CSI, within a maximum of 0.4 dB degradation, regardless of the delay spread of the channel, Doppler frequency, and subcarrier modulation. Furthermore, reducing the matrix size by splitting the dispersive channel impulse response into clusters means that the degenerated inverse matrix estimator (DIME is feasible for broadband, high-quality OFDM transmission systems. In addition to theoretical analysis on normalized mean squared error (NMSE performance of DIME, computer simulations over realistic nonsample spaced channels also showed that the DIME is robust for intersymbol interference (ISI channels and fast time-invariant channels where a minimum mean squared error (MMSE estimator does not work well.
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.
Unification of Frequency direction Pilot-symbol Aided Channel Estimation (PACE) for OFDM
DEFF Research Database (Denmark)
Rom, Christian; Manchón, Carles Navarro; Deneire, Luc
2007-01-01
Frequency direction Pilot-symbol Aided Channel Estimation (PACE) for Orthogonal Frequency Division Multiplexing (OFDM) is crucial in high-rate wireless systems. The choice of an estimator for upcoming standards, such as the Long Term Evolution (LTE) of UTRA, has to take into account...... their specificities, namely the presence of virtual subcarriers and non-sample-spaced channels. To ease this choice, we propose a unified presentation of estimators encompassing most of the algorithms that can be found in literature, which only differ by the assumptions made on the channel. This unification leads...... to common Mean Squared Error (MSE) expression, both for sample-spaced and non sample-spaced channels, and enables easy, yet comprehensive comparisons between the estimators....
Fast Estimation of Outage Probabilities in MIMO Channels
Srinivasan, R.; Tiba, G.
2004-01-01
Fast estimation methods for small outage probabilities of signaling in fading multiple-input multiple-output (MIMO) channels are developed. Communication over such channels is of much current interest, and quick and accurate methods for estimating outage capacities are needed. The methods described
Low-Complexity Bayesian Estimation of Cluster-Sparse Channels
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.
Guo, Lianping; Tian, Shulin; Jiang, Jun
2015-03-01
This paper proposes an algorithm to estimate the channel mismatches in time-interleaved analog-to-digital converter (TIADC) based on fractional delay (FD) and sine curve fitting. Choose one channel as the reference channel and apply FD to the output samples of reference channel to obtain the ideal samples of non-reference channels with no mismatches. Based on least square method, the sine curves are adopted to fit the ideal and the actual samples of non-reference channels, and then the mismatch parameters can be estimated by comparing the ideal sine curves and the actual ones. The principle of this algorithm is simple and easily understood. Moreover, its implementation needs no extra circuits, lowering the hardware cost. Simulation results show that the estimation accuracy of this algorithm can be controlled within 2%. Finally, the practicability of this algorithm is verified by the measurement results of channel mismatch errors of a two-channel TIADC prototype.
An information-guided channel-hopping scheme for block-fading channels with estimation errors
Yang, Yuli
2010-12-01
Information-guided channel-hopping technique employing multiple transmit antennas was previously proposed for supporting high data rate transmission over fading channels. This scheme achieves higher data rates than some mature schemes, such as the well-known cyclic transmit antenna selection and space-time block coding, by exploiting the independence character of multiple channels, which effectively results in having an additional information transmitting channel. Moreover, maximum likelihood decoding may be performed by simply decoupling the signals conveyed by the different mapping methods. In this paper, we investigate the achievable spectral efficiency of this scheme in the case of having channel estimation errors, with optimum pilot overhead for minimum meansquare error channel estimation, when transmitting over blockfading channels. Our numerical results further substantiate the robustness of the presented scheme, even with imperfect channel state information. ©2010 IEEE.
Low Complexity Iterative Joint Channel Estimation and Multiuser Detection for DS-CDMA
DEFF Research Database (Denmark)
Christensen, Søren Skovgård; Kocian, Alexander; Fleury, Bernard Henri
2004-01-01
Previously the SAGE algorithm was applied to sub-optimal yet efficient Joint data Detection and channel Estimation (JDE) for DS-CDMA of moderate time complexity. Modifying the expectation and maximization steps of the SAGE-JDE scheme, it is possible to obtain an efficient receiver architecture...
Bidirectional Fano Algorithm for Lattice Coded MIMO Channels
Al-Quwaiee, Hessa
2013-05-08
Recently, lattices - a mathematical representation of infinite discrete points in the Euclidean space, have become an effective way to describe and analyze communication systems especially system those that can be modeled as linear Gaussian vector channel model. Channel codes based on lattices are preferred due to three facts: lattice codes have simple structure, the code can achieve the limits of the channel, and they can be decoded efficiently using lattice decoders which can be considered as the Closest Lattice Point Search (CLPS). Since the time lattice codes were introduced to Multiple Input Multiple Output (MIMO) channel, Sphere Decoder (SD) has been an efficient way to implement lattice decoders. Sphere decoder offers the optimal performance at the expense of high decoding complexity especially for low signal-to-noise ratios (SNR) and for high- dimensional systems. On the other hand, linear and non-linear receivers, Minimum Mean Square Error (MMSE), and MMSE Decision-Feedback Equalization (DFE), provide the lowest decoding complexity but unfortunately with poor performance. Several studies works have been conducted in the last years to address the problem of designing low complexity decoders for the MIMO channel that can achieve near optimal performance. It was found that sequential decoders using backward tree search can bridge the gap between SD and MMSE. The sequential decoder provides an interesting performance-complexity trade-off using a bias term. Yet, the sequential decoder still suffers from high complexity for mid-to-high SNR values. In this work, we propose a new algorithm for Bidirectional Fano sequential Decoder (BFD) in order to reduce the mid-to-high SNR complexity. Our algorithm consists of first constructing a unidirectional Sequential Decoder based on forward search using the QL decomposition. After that, BFD incorporates two searches, forward and backward, to work simultaneously till they merge and find the closest lattice point to the
Multicarrier chaotic communications in multipath fading channels without channel estimation
Directory of Open Access Journals (Sweden)
Shilian Wang
2015-01-01
Full Text Available A multi-carrier chaotic shift keying(MC-CSK communication scheme with low probability of interception(LPI is proposed in this article. We apply chaotic spreading sequences in the frequency domain, mapping a different chip of a chaotic sequence to an individual orthogonal frequency division multiplexing(OFDM subcarrier. In each block size of $M$ OFDM symbols, we use one pilot OFDM symbol inserted time-spaced in all-frequency to transmit the reference chaotic signal and use the other M-1 OFDM symbols to transmit the information-bearing signals each spreaded by the reference chaotic signal. At the receiver, we construct a differential detector after DFT and recover the information bits from the correlations between the pilot OFDM symbol and the other M-1 OFDM symbols in each block size of M. Performance analysis and computer simulations show that the MC-CSK outperforms differential chaos shift keying(DCSK in AWGN channels with high bandwidth efficiency for the block size of M=2 and that the MC-CSK exploits effectively the frequent diversity of the multipath channel.
Multicarrier chaotic communications in multipath fading channels without channel estimation
Energy Technology Data Exchange (ETDEWEB)
Wang, Shilian, E-mail: wangsl@nudt.edu.cn; Zhang, Zhili [College of Electrical Science and Engineering, National University of Defense Technology, Changsha, 410073, P R China (China)
2015-01-15
A multi-carrier chaotic shift keying(MC-CSK) communication scheme with low probability of interception(LPI) is proposed in this article. We apply chaotic spreading sequences in the frequency domain, mapping a different chip of a chaotic sequence to an individual orthogonal frequency division multiplexing(OFDM) subcarrier. In each block size of $M$ OFDM symbols, we use one pilot OFDM symbol inserted time-spaced in all-frequency to transmit the reference chaotic signal and use the other M-1 OFDM symbols to transmit the information-bearing signals each spreaded by the reference chaotic signal. At the receiver, we construct a differential detector after DFT and recover the information bits from the correlations between the pilot OFDM symbol and the other M-1 OFDM symbols in each block size of M. Performance analysis and computer simulations show that the MC-CSK outperforms differential chaos shift keying(DCSK) in AWGN channels with high bandwidth efficiency for the block size of M=2 and that the MC-CSK exploits effectively the frequent diversity of the multipath channel.
Multicarrier chaotic communications in multipath fading channels without channel estimation
Wang, Shilian; Zhang, Zhili
2015-01-01
A multi-carrier chaotic shift keying(MC-CSK) communication scheme with low probability of interception(LPI) is proposed in this article. We apply chaotic spreading sequences in the frequency domain, mapping a different chip of a chaotic sequence to an individual orthogonal frequency division multiplexing(OFDM) subcarrier. In each block size of M OFDM symbols, we use one pilot OFDM symbol inserted time-spaced in all-frequency to transmit the reference chaotic signal and use the other M-1 OFDM symbols to transmit the information-bearing signals each spreaded by the reference chaotic signal. At the receiver, we construct a differential detector after DFT and recover the information bits from the correlations between the pilot OFDM symbol and the other M-1 OFDM symbols in each block size of M. Performance analysis and computer simulations show that the MC-CSK outperforms differential chaos shift keying(DCSK) in AWGN channels with high bandwidth efficiency for the block size of M=2 and that the MC-CSK exploits effectively the frequent diversity of the multipath channel.
Hadei, Sayed A
2011-01-01
In this paper, we propose novel low-complexity adaptive channel estimation techniques for mob ile wireless chan- n els in presence of Rayleigh fading, carrier frequency offsets (CFO) and random channel variations. We show that the selective p artial update of the estimated channel tap-weight vector offers a better trade-off between the performance and computational complexity, compared to the full update of the estimated channel tap-weight vector. We evaluate the mean-square weight error of th e proposed methods and demonstrate the usefulness of its via simulation studies.
Directory of Open Access Journals (Sweden)
Duplicy Jonathan
2009-01-01
Full Text Available We present a blind coarse timing offset estimation technique for CP-OFDM and ZP-OFDM transmission over frequency selective channels. The technique exploits the cyclic prefix or zero-padding structure to estimate the starting position of the OFDM symbols without requiring any additional pilots. Simulation results are performed on various channel models with timing and frequency offsets. The presented technique is compared with the autocorrelation-based technique and various techniques in frequency selective channels. Our algorithm shows better performance results than those of the autocorrelation-based technique in NLOS channels, where the most predominant channel path is usually not the first arrival path.
Doppler-shift estimation of flat underwater channel using data-aided least-square approach
Directory of Open Access Journals (Sweden)
Weiqiang Pan
2015-03-01
Full Text Available In this paper we proposed a dada-aided Doppler estimation method for underwater acoustic communication. The training sequence is non-dedicate, hence it can be designed for Doppler estimation as well as channel equalization. We assume the channel has been equalized and consider only flat-fading channel. First, based on the training symbols the theoretical received sequence is composed. Next the least square principle is applied to build the objective function, which minimizes the error between the composed and the actual received signal. Then an iterative approach is applied to solve the least square problem. The proposed approach involves an outer loop and inner loop, which resolve the channel gain and Doppler coefficient, respectively. The theoretical performance bound, i.e. the Cramer-Rao Lower Bound (CRLB of estimation is also derived. Computer simulations results show that the proposed algorithm achieves the CRLB in medium to high SNR cases.
Sparse Channel Estimation for MIMO-OFDM Two-Way Relay Network with Compressed Sensing
Directory of Open Access Journals (Sweden)
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.
Sequential and Adaptive Learning Algorithms for M-Estimation
Directory of Open Access Journals (Sweden)
Guang Deng
2008-05-01
Full Text Available The M-estimate of a linear observation model has many important engineering applications such as identifying a linear system under non-Gaussian noise. Batch algorithms based on the EM algorithm or the iterative reweighted least squares algorithm have been widely adopted. In recent years, several sequential algorithms have been proposed. In this paper, we propose a family of sequential algorithms based on the Bayesian formulation of the problem. The basic idea is that in each step we use a Gaussian approximation for the posterior and a quadratic approximation for the log-likelihood function. The maximum a posteriori (MAP estimation leads naturally to algorithms similar to the recursive least squares (RLSs algorithm. We discuss the quality of the estimate, issues related to the initialization and estimation of parameters, and robustness of the proposed algorithm. We then develop LMS-type algorithms by replacing the covariance matrix with a scaled identity matrix under the constraint that the determinant of the covariance matrix is preserved. We have proposed two LMS-type algorithms which are effective and low-cost replacement of RLS-type of algorithms working under Gaussian and impulsive noise, respectively. Numerical examples show that the performance of the proposed algorithms are very competitive to that of other recently published algorithms.
The Simple Mono-Canal Algorithm for the Temperature Estimating of ...
African Journals Online (AJOL)
The knowledge of the surface temperature is strongly required in several applications, for instance in agrometeorology, climatology and environmental studies. In this study we have developed an algorithm mono-canal to estimate land surface temperature (Ts) in spectral band as the infrared channel (IR) of METEOSAT-7.
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.
Performance of Channel Estimation in MIMO-OFDM Systems
Directory of Open Access Journals (Sweden)
Subuh Pramono
2013-07-01
Full Text Available This paper presented the performance of faded channel estimation method on orthogonal frequency division multiplexing-multiple input multiple output (OFDM-MIMO i.e. least squares (LS and minimum mean squared error (MMSE. Channel impuls response (CIR was required to overcome the intersymbol interference (ISI. Channel impuls response information was obtained from channel estimation processing. Iterance simulation used monte-carlo technique to determined the performance of bit error rate (BER and mean squared error(MSE. Simulation results show that the mean squared error performance on MIMO system was better than the SISO system.On MMSE channel estimation, the MIMO 2Tx-2Rx system provided 2 dB improvement that compared to SISO system at value of MSE 10-2. Furthermore, MIMO 3Tx-2Rx produce improvement about 1.5 dB, MIMO 4Tx-2Rx improve about 3.5 dB at BER 10-4, respectively. The MIMO 2Tx-2Rx system, MMSE channel estimation produced better performance 1 dB than LS channel estimation with sufficient SNR value for MSE 10-2 . Pilot arrangement, the simulation results show that the block type-pilot arrangement produced better performance than the comb type-pilot arrangement at fast fading channel. Block type-pilot arrangement system produced better 10 dB than the comb type-pilot arrangement with MMSE method at value of BER 2 10-2
Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems
Zaib, Alam
2016-07-22
By virtue of large antenna arrays, massive MIMO systems have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. This paper addresses uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. We propose an efficient distributed minimum mean square error (MMSE) algorithm that can achieve near optimal channel estimates at low complexity by exploiting the strong spatial correlation among antenna array elements. The proposed method involves solving a reduced dimensional MMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring array elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. Furthermore, we use stochastic geometry to quantify the pilot contamination, and in turn use this information to analyze the effect of pilot contamination on channel MSE. The simulation results validate our analysis and show near optimal performance of the proposed estimation algorithms.
Rehan, Waqas; Fischer, Stefan; Rehan, Maaz
2016-09-12
Wireless sensor networks (WSNs) have become more and more diversified and are today able to also support high data rate applications, such as multimedia. In this case, per-packet channel handshaking/switching may result in inducing additional overheads, such as energy consumption, delays and, therefore, data loss. One of the solutions is to perform stream-based channel allocation where channel handshaking is performed once before transmitting the whole data stream. Deciding stream-based channel allocation is more critical in case of multichannel WSNs where channels of different quality/stability are available and the wish for high performance requires sensor nodes to switch to the best among the available channels. In this work, we will focus on devising mechanisms that perform channel quality/stability estimation in order to improve the accommodation of stream-based communication in multichannel wireless sensor networks. For performing channel quality assessment, we have formulated a composite metric, which we call channel rank measurement (CRM), that can demarcate channels into good, intermediate and bad quality on the basis of the standard deviation of the received signal strength indicator (RSSI) and the average of the link quality indicator (LQI) of the received packets. CRM is then used to generate a data set for training a supervised machine learning-based algorithm (which we call Normal Equation based Channel quality prediction (NEC) algorithm) in such a way that it may perform instantaneous channel rank estimation of any channel. Subsequently, two robust extensions of the NEC algorithm are proposed (which we call Normal Equation based Weighted Moving Average Channel quality prediction (NEWMAC) algorithm and Normal Equation based Aggregate Maturity Criteria with Beta Tracking based Channel weight prediction (NEAMCBTC) algorithm), that can perform channel quality estimation on the basis of both current and past values of channel rank estimation. In the end
Directory of Open Access Journals (Sweden)
Waqas Rehan
2016-09-01
Full Text Available Wireless sensor networks (WSNs have become more and more diversified and are today able to also support high data rate applications, such as multimedia. In this case, per-packet channel handshaking/switching may result in inducing additional overheads, such as energy consumption, delays and, therefore, data loss. One of the solutions is to perform stream-based channel allocation where channel handshaking is performed once before transmitting the whole data stream. Deciding stream-based channel allocation is more critical in case of multichannel WSNs where channels of different quality/stability are available and the wish for high performance requires sensor nodes to switch to the best among the available channels. In this work, we will focus on devising mechanisms that perform channel quality/stability estimation in order to improve the accommodation of stream-based communication in multichannel wireless sensor networks. For performing channel quality assessment, we have formulated a composite metric, which we call channel rank measurement (CRM, that can demarcate channels into good, intermediate and bad quality on the basis of the standard deviation of the received signal strength indicator (RSSI and the average of the link quality indicator (LQI of the received packets. CRM is then used to generate a data set for training a supervised machine learning-based algorithm (which we call Normal Equation based Channel quality prediction (NEC algorithm in such a way that it may perform instantaneous channel rank estimation of any channel. Subsequently, two robust extensions of the NEC algorithm are proposed (which we call Normal Equation based Weighted Moving Average Channel quality prediction (NEWMAC algorithm and Normal Equation based Aggregate Maturity Criteria with Beta Tracking based Channel weight prediction (NEAMCBTC algorithm, that can perform channel quality estimation on the basis of both current and past values of channel rank estimation
Rehan, Waqas; Fischer, Stefan; Rehan, Maaz
2016-01-01
Wireless sensor networks (WSNs) have become more and more diversified and are today able to also support high data rate applications, such as multimedia. In this case, per-packet channel handshaking/switching may result in inducing additional overheads, such as energy consumption, delays and, therefore, data loss. One of the solutions is to perform stream-based channel allocation where channel handshaking is performed once before transmitting the whole data stream. Deciding stream-based channel allocation is more critical in case of multichannel WSNs where channels of different quality/stability are available and the wish for high performance requires sensor nodes to switch to the best among the available channels. In this work, we will focus on devising mechanisms that perform channel quality/stability estimation in order to improve the accommodation of stream-based communication in multichannel wireless sensor networks. For performing channel quality assessment, we have formulated a composite metric, which we call channel rank measurement (CRM), that can demarcate channels into good, intermediate and bad quality on the basis of the standard deviation of the received signal strength indicator (RSSI) and the average of the link quality indicator (LQI) of the received packets. CRM is then used to generate a data set for training a supervised machine learning-based algorithm (which we call Normal Equation based Channel quality prediction (NEC) algorithm) in such a way that it may perform instantaneous channel rank estimation of any channel. Subsequently, two robust extensions of the NEC algorithm are proposed (which we call Normal Equation based Weighted Moving Average Channel quality prediction (NEWMAC) algorithm and Normal Equation based Aggregate Maturity Criteria with Beta Tracking based Channel weight prediction (NEAMCBTC) algorithm), that can perform channel quality estimation on the basis of both current and past values of channel rank estimation. In the end
Directory of Open Access Journals (Sweden)
Rugini Luca
2010-01-01
Full Text Available Modern wireless communication systems require high transmission rates, giving rise to frequency selectivity due to multipath propagation. In addition, high-mobility terminals and scatterers induce Doppler shifts that introduce time selectivity. Therefore, advanced techniques are needed to accurately model the time- and frequency-selective (i.e., doubly selective channels and to counteract the related performance degradation. In this paper, we develop new receivers for orthogonal frequency-division multiplexing (OFDM systems and single-carrier (SC systems in doubly selective channels by embedding the channel estimation task within low-complexity block turbo equalizers. Linear minimum mean-squared error (MMSE pilot-assisted channel estimators are presented, and the soft data estimates from the turbo equalizers are used to improve the quality of the channel estimates.
Weighted-noise threshold based channel estimation for OFDM ...
Indian Academy of Sciences (India)
In Kashyap & Mehta. (2014), in the context of driving power control in underlay cognitive radio, impact of various channel estimation errors has been studied. ...... Minn H and Bhargava V 2000 An investigation into time-domain approach for OFDM channel estima- tion. IEEE Trans. Broadcasting 46(4): 240–248, doi: 10.1109/ ...
CFO and channel estimation for MISO-OFDM systems
Ladaycia, Abdelhamid
2017-11-02
This study deals with the joint channel and carrier frequency offset (CFO) estimation in a Multiple Input Single Output (MISO) communications system. This problem arises in OFDM (Orthogonal Frequency Division Multiplexing) based multi-relay transmission protocols such that the geo-routing one proposed by A. Bader et al in 2012. Indeed, the outstanding performance of this multi-hop relaying scheme relies heavily on the channel and CFO estimation quality at the PHY layer. In this work, two approaches are considered: The first is based on estimating the overall channel (including the CFO) as a time-varying one using an adaptive scheme under the assumption of small or moderate CFOs while the second one performs separately, the channel and CFO parameters estimation based on the considered data model. The two solutions are analyzed and compared in terms of performance, cost and convergence rate.
Training Based Channel Estimation for Multitaper GFDM System
Directory of Open Access Journals (Sweden)
Shravan Kumar Bandari
2017-01-01
Full Text Available Recent activities in the cellular network world clearly show the need to design new physical layer waveforms in order to meet future wireless requirements. Generalized Frequency Division Multiplexing (GFDM is one of the leading candidates for 5G and one of its key features is the usage of circular pulse shaping of subcarriers to remove prototype filter transients. Due to the nonorthogonal nature of the conventional GFDM system, inherent interference will affect adversely channel estimation. With Discrete Prolate Spheroidal Sequences (DPSSs or multitapers as prototype filters an improved orthogonal GFDM system can be developed. In this work, we investigate channel estimation methods for multitaper GFDM (MGFDM systems with and without Discrete Fourier Transform (DFT. The simulation results are presented using Least Squares (LS and Minimum Mean Square Error (MMSE channel estimation (CE methods. DFT based CE methods provide better estimates of the channel but with an additional computational cost.
A Fast LMMSE Channel Estimation Method for OFDM Systems
Directory of Open Access Journals (Sweden)
Zhou Wen
2009-01-01
Full Text Available A fast linear minimum mean square error (LMMSE channel estimation method has been proposed for Orthogonal Frequency Division Multiplexing (OFDM systems. In comparison with the conventional LMMSE channel estimation, the proposed channel estimation method does not require the statistic knowledge of the channel in advance and avoids the inverse operation of a large dimension matrix by using the fast Fourier transform (FFT operation. Therefore, the computational complexity can be reduced significantly. The normalized mean square errors (NMSEs of the proposed method and the conventional LMMSE estimation have been derived. Numerical results show that the NMSE of the proposed method is very close to that of the conventional LMMSE method, which is also verified by computer simulation. In addition, computer simulation shows that the performance of the proposed method is almost the same with that of the conventional LMMSE method in terms of bit error rate (BER.
Evaluation of an automated single-channel sleep staging algorithm
Directory of Open Access Journals (Sweden)
Wang Y
2015-09-01
Full Text Available Ying Wang,1 Kenneth A Loparo,1,2 Monica R Kelly,3 Richard F Kaplan1 1General Sleep Corporation, Euclid, OH, 2Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, 3Department of Psychology, University of Arizona, Tucson, AZ, USA Background: We previously published the performance evaluation of an automated electroencephalography (EEG-based single-channel sleep–wake detection algorithm called Z-ALG used by the Zmachine® sleep monitoring system. The objective of this paper is to evaluate the performance of a new algorithm called Z-PLUS, which further differentiates sleep as detected by Z-ALG into Light Sleep, Deep Sleep, and Rapid Eye Movement (REM Sleep, against laboratory polysomnography (PSG using a consensus of expert visual scorers. Methods: Single night, in-lab PSG recordings from 99 subjects (52F/47M, 18–60 years, median age 32.7 years, including both normal sleepers and those reporting a variety of sleep complaints consistent with chronic insomnia, sleep apnea, and restless leg syndrome, as well as those taking selective serotonin reuptake inhibitor/serotonin–norepinephrine reuptake inhibitor antidepressant medications, previously evaluated using Z-ALG were re-examined using Z-PLUS. EEG data collected from electrodes placed at the differential-mastoids (A1–A2 were processed by Z-ALG to determine wake and sleep, then those epochs detected as sleep were further processed by Z-PLUS to differentiate into Light Sleep, Deep Sleep, and REM. EEG data were visually scored by multiple certified polysomnographic technologists according to the Rechtschaffen and Kales criterion, and then combined using a majority-voting rule to create a PSG Consensus score file for each of the 99 subjects. Z-PLUS output was compared to the PSG Consensus score files for both epoch-by-epoch (eg, sensitivity, specificity, and kappa and sleep stage-related statistics (eg, Latency to Deep Sleep, Latency to REM
UWB channel estimation using new generating TR transceivers
Nekoogar, Faranak [San Ramon, CA; Dowla, Farid U [Castro Valley, CA; Spiridon, Alex [Palo Alto, CA; Haugen, Peter C [Livermore, CA; Benzel, Dave M [Livermore, CA
2011-06-28
The present invention presents a simple and novel channel estimation scheme for UWB communication systems. As disclosed herein, the present invention maximizes the extraction of information by incorporating a new generation of transmitted-reference (Tr) transceivers that utilize a single reference pulse(s) or a preamble of reference pulses to provide improved channel estimation while offering higher Bit Error Rate (BER) performance and data rates without diluting the transmitter power.
Modeling, Real-Time Estimation, and Identification of UWB Indoor Wireless Channels
Directory of Open Access Journals (Sweden)
Mohammed M. Olama
2013-01-01
Full Text Available Stochastic differential equations (SDEs are used to model ultrawideband (UWB indoor wireless channels. We show that the impulse responses for time-varying indoor wireless channels can be approximated in a mean-square sense as close as desired by impulse responses that can be realized by SDEs. The state variables represent the inphase and quadrature components of the UWB channel. The expected maximization and extended Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Both resolvable and nonresolvable multipath received signals are considered and represented as small-scaled Nakagami fading. The proposed models together with the estimation algorithm are tested using UWB indoor measurement data demonstrating the method’s viability and the results are presented.
Pilot symbol assisted channel estimation for OFDM-based cognitive radio systems
Manasseh, Emmanuel; Ohno, Shuichi; Nakamoto, Masayoshi
2013-12-01
In this article, challenges regarding the provision of channel state information (CSI) in non-contiguous orthogonal frequency division multiplexing (NC-OFDM) cognitive radio (CR) systems are addressed. We propose a novel scheme that utilizes cross entropy (CE) optimization together with an analytical pilot power distribution technique to design pilot symbols that minimizes the channel estimate mean squared error (MSE) of frequency-selective channels. The optimal selection of pilot subcarriers is a combinatorial problem that requires heavy computations. To reduce the computational complexity, the CE optimization is utilized to determine the position of pilot subcarriers. Then, for a given pilot placement obtained by the CE algorithm, a closed form expression to obtain optimal pilot power distribution is employed. Simulation results indicate that, the proposed pilot symbol design provides better channel estimate MSE as well as the bit error rate (BER) performance when compared with the conventional equal powered pilot design.
Semiblind frequency-domain timing synchronization and channel estimation for OFDM systems
Kung, Te-Lung; Parhi, Keshab K.
2013-12-01
In this article, we propose unit vectors in the high dimensional Cartesian coordinate system as the preamble, and then propose a semiblind timing synchronization and channel estimation scheme for orthogonal frequency division multiplexing (OFDM) systems. Due to the lack of useful information in the time-domain, a frequency-domain timing synchronization algorithm is proposed. The proposed semiblind approach consists of three stages. In the first stage, a coarse timing offset related to the delayed timing of the path with the maximum gain in multipath fading channels is obtained. Then, a fine time adjustment algorithm is performed to find the actual delayed timing in channels. Finally, the channel response in the frequency-domain is obtained based on the final timing estimate. Although the required number of additions in the proposed algorithm is higher than those in conventional methods, the simulation results show that the proposed approach has excellent performance of timing synchronization in several channel models at signal-to-noise ratio (SNR) smaller than 6 dB. In addition, for a low-density parity-check coded single-input single-output OFDM system, our proposed approach has better bit-error-rate performance than conventional approaches for SNR varying from 3 to 8 dB.
Algorithms For Wireless Channel Equalization With Joint Coding And Soft Decision Feedback
Directory of Open Access Journals (Sweden)
Radu DOBRESCU
2001-12-01
Full Text Available The paper proposes a new approach based on Joint Entropy Maximisation (JEM using a soft decision feedback equalizer (S-DE to suppress error propagation. In its first section, the paper presents the principle of the solution and the theoretical framework based on entropy maximisation, which allows introducing the soft decision device without assuming that the channel distortion is Gaussian. Because JE is a non-linear function, a gradient descent algorithm is used for maximising. Then an equivalence of JEM and ISIC (Inter-Symbol Interference Cancellation is proved in order to establish that an equalised single carrier system using coded modulation (8-phase shift keying associated with a convolution code offers similar performances when compared with multicarrier modulation. In the second section the paper develop an adequate receiver model for joint convolution coding and S-DFE. The error correction decoder uses a standard Viterbi algorithm. The DFE consists of a feedforward finite impulse response (FIR filter (FFF and a feedback filter (FBF implemented as a transversal FIR filter. FFF eliminates the precursor ISI, while FBF minimise the effect of residual ISI using soft decisions by the joint coding and equalisation process. The third main section of the paper describes the proposed method for estimating optimum soft feedback using a maximum a posteriori probability (MAP algorithm. Then, performances of the soft decision device in a simulated environment are analysed on a structure with 8 taps for FFF and 5 taps for FBF. Since the purpose of the evaluation was to compare the proposed S-DFE with a former H-EFE, the coded packet error rate was estimated in a two-path and in a six- path channel. We have shown that in some case the proposed algorithm offers better convergence rate and robustness when compared with the corresponding existing algorithm. Some conclusions on the extension of the S-DFE techniques in vary applications are finally presented.
Advanced Channel Estimation and Multiuser Detection in GSM
DEFF Research Database (Denmark)
Arildsen, Thomas; Blauendahl, Jesper
A single-antenna interference cancellation-capable data detector employing the SAGE-algorithm for GSM downlink transmission with co-channel interference has been designed and tested. Two scenarios were considered: First, a frequency-flat Rayleigh fading scenario with synchronously received users....
Contributions in Radio Channel Sounding, Modeling, and Estimation
DEFF Research Database (Denmark)
Pedersen, Troels
2009-01-01
of reverberant channels. We develop a theory for optimization of spatio-temporal apertures used in multiple-input multiple-output (MIMO) channel sounding. Initially, we focus on joint estimation of bi-direction and Doppler frequency from time-division multiplexing (TDM) MIMO measurements. We introduce...... probability density functions (pdfs); the pdfs are defined by their first- and second-order moments. We derive estimators of these parameters and illustrate their applicability on measurement data. The obtained spread estimates are significantly smaller, and the estimated psds are much more concentrated...
Regularized Tyler's Scatter Estimator: Existence, Uniqueness, and Algorithms
Sun, Ying; Babu, Prabhu; Palomar, Daniel P.
2014-10-01
This paper considers the regularized Tyler's scatter estimator for elliptical distributions, which has received considerable attention recently. Various types of shrinkage Tyler's estimators have been proposed in the literature and proved work effectively in the "small n large p" scenario. Nevertheless, the existence and uniqueness properties of the estimators are not thoroughly studied, and in certain cases the algorithms may fail to converge. In this work, we provide a general result that analyzes the sufficient condition for the existence of a family of shrinkage Tyler's estimators, which quantitatively shows that regularization indeed reduces the number of required samples for estimation and the convergence of the algorithms for the estimators. For two specific shrinkage Tyler's estimators, we also proved that the condition is necessary and the estimator is unique. Finally, we show that the two estimators are actually equivalent. Numerical algorithms are also derived based on the majorization-minimization framework, under which the convergence is analyzed systematically.
A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.
Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan
2015-01-01
In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.
Adaptive underwater channel estimation for hybrid lidar/radar
Lee, Robert W.; Illig, David W.; Mullen, Linda J.
2017-05-01
Adaptive filtering and channel estimation techniques are applied to laser based ranging systems that utilize wide-band intensity modulation to measure the range and reflectivity of underwater objects. The proposed method aims to iteratively learn the frequency dependent characteristics of the underwater environment using a frequency domain adaptive filter, which results in an estimate for the channels optical impulse response. This work presents the application of the frequency domain adaptive filter to simulated and experimental data, and shows it is possible to iteratively learn the underwater optical channel impulse response while using Hybrid Lidar/Radar techniques.
Directory of Open Access Journals (Sweden)
Carlos Ribeiro
2009-01-01
Full Text Available We present a CFO estimation algorithm and an associated channel estimation method for broadband OFDM systems with transmitter diversity. The CFO estimation algorithm explores the TD structure of the transmitted symbols carrying pilots and data, relying solely on the data component present on the symbols to estimate the CFO, thus avoiding additional overhead like training symbols or null subcarriers. An intermediate output of the CFO algorithm provides an easy-to-get initial CIR estimate that will be improved with the utilization of a TD LMMSE filter. The feasibility of the investigated methods is substantiated by system simulation using indoor and outdoor broadband wireless channel models. Simulation results show that the joint algorithms provide a near optimal system's performance.
On the secrecy capacity of the MISO wiretap channel under imperfect channel estimation
Rezki, Zouheir
2014-12-01
We consider a wiretap channel consisting of a source with multiple antennas, a legitimate receiver and an eavesdropper with a single antenna each. The channels between the source and the receivers undergo fast fading. We assume that the transmitter, in addition to the statistics of both channels, is only aware of a noisy version of the CSI to the legitimate receiver referred to as main channel. The legitimate receiver is aware of both its instantaneous channel gain and the transmitter\\'s estimate of the main channel. On the other hand, the eavesdropper\\'s receiver, in addition to its instantaneous channel realization, is aware of the actual main CSI and the transmitter\\'s estimate as well. While the capacity of this channel is still open even with perfect CSI at the transmitter, we provide in this paper upper and lower bounds on the secrecy capacity. The upper bound is tighter than the one corresponding to perfect main CSI and the gap between the two upper bounds is characterized in function of the channel estimation error variance, at high-SNR. Furthermore, we show that our upper and lower bounds coincide in the case of no main CSI providing a trivial secrecy capacity.
Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times
Senthilmurugan, S.; Ansari, Junaid; Mähönen, Petri; Venkatesh, T. G.; Petrova, Marina
2016-01-01
We consider a multichannel Cognitive Radio Network (CRN), where secondary users sequentially sense channels for opportunistic spectrum access. In this scenario, the Channel Selection Algorithm (CSA) allows secondary users to find a vacant channel with the minimal number of channel switches. Most of the existing CSA literature assumes exponential ON-OFF time distribution for primary users (PU) channel occupancy pattern. This exponential assumption might be helpful to get performance bounds; bu...
Novel Method for 5G Systems NLOS Channels Parameter Estimation
Directory of Open Access Journals (Sweden)
Vladeta Milenkovic
2017-01-01
Full Text Available For the development of new 5G systems to operate in mm bands, there is a need for accurate radio propagation modelling at these bands. In this paper novel approach for NLOS channels parameter estimation will be presented. Estimation will be performed based on LCR performance measure, which will enable us to estimate propagation parameters in real time and to avoid weaknesses of ML and moment method estimation approaches.
Energy-Efficient Channel Estimation in MIMO Systems
Directory of Open Access Journals (Sweden)
2006-01-01
Full Text Available The emergence of MIMO communications systems as practical high-data-rate wireless communications systems has created several technical challenges to be met. On the one hand, there is potential for enhancing system performance in terms of capacity and diversity. On the other hand, the presence of multiple transceivers at both ends has created additional cost in terms of hardware and energy consumption. For coherent detection as well as to do optimization such as water filling and beamforming, it is essential that the MIMO channel is known. However, due to the presence of multiple transceivers at both the transmitter and receiver, the channel estimation problem is more complicated and costly compared to a SISO system. Several solutions have been proposed to minimize the computational cost, and hence the energy spent in channel estimation of MIMO systems. We present a novel method of minimizing the overall energy consumption. Unlike existing methods, we consider the energy spent during the channel estimation phase which includes transmission of training symbols, storage of those symbols at the receiver, and also channel estimation at the receiver. We develop a model that is independent of the hardware or software used for channel estimation, and use a divide-and-conquer strategy to minimize the overall energy consumption.
Dermoune, Azzouz; Simon, Eric Pierre
2017-12-01
This paper is a theoretical analysis of the maximum likelihood (ML) channel estimator for orthogonal frequency-division multiplexing (OFDM) systems in the presence of unknown interference. The following theoretical results are presented. Firstly, the uniqueness of the ML solution for practical applications, i.e., when thermal noise is present, is analytically demonstrated when the number of transmitted OFDM symbols is strictly greater than one. The ML solution is then derived from the iterative conditional ML (CML) algorithm. Secondly, it is shown that the channel estimate can be described as an algebraic function whose inputs are the initial value and the means and variances of the received samples. Thirdly, it is theoretically demonstrated that the channel estimator is not biased. The second and the third results are obtained by employing oblique projection theory. Furthermore, these results are confirmed by numerical results.
MVDR Algorithm Based on Estimated Diagonal Loading for Beamforming
Directory of Open Access Journals (Sweden)
Yuteng Xiao
2017-01-01
Full Text Available Beamforming algorithm is widely used in many signal processing fields. At present, the typical beamforming algorithm is MVDR (Minimum Variance Distortionless Response. However, the performance of MVDR algorithm relies on the accurate covariance matrix. The MVDR algorithm declines dramatically with the inaccurate covariance matrix. To solve the problem, studying the beamforming array signal model and beamforming MVDR algorithm, we improve MVDR algorithm based on estimated diagonal loading for beamforming. MVDR optimization model based on diagonal loading compensation is established and the interval of the diagonal loading compensation value is deduced on the basis of the matrix theory. The optimal diagonal loading value in the interval is also determined through the experimental method. The experimental results show that the algorithm compared with existing algorithms is practical and effective.
Estimation of UAV Position with Use of Smoothing Algorithms
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Kaniewski Piotr
2017-03-01
Full Text Available The paper presents methods of on-line and off-line estimation of UAV position on the basis of measurements from its integrated navigation system. The navigation system installed on board UAV contains an INS and a GNSS receiver. The UAV position, as well as its velocity and orientation are estimated with the use of smoothing algorithms. For off-line estimation, a fixed-interval smoothing algorithm has been applied. On-line estimation has been accomplished with the use of a fixed-lag smoothing algorithm. The paper includes chosen results of simulations demonstrating improvements of accuracy of UAV position estimation with the use of smoothing algorithms in comparison with the use of a Kalman filter.
Parameter estimation for chaotic systems using improved bird swarm algorithm
Xu, Chuangbiao; Yang, Renhuan
2017-12-01
Parameter estimation of chaotic systems is an important problem in nonlinear science and has aroused increasing interest of many research fields, which can be basically reduced to a multidimensional optimization problem. In this paper, an improved boundary bird swarm algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the good global convergence and robustness of the bird swarm algorithm and the exploitation capability of improved boundary learning strategy. Experiments are conducted on the Lorenz system and the coupling motor system. Numerical simulation results reveal the effectiveness and with desirable performance of IBBSA for parameter estimation of chaotic systems.
EDMC: An enhanced distributed multi-channel anti-collision algorithm for RFID reader system
Zhang, YuJing; Cui, Yinghua
2017-05-01
In this paper, we proposes an enhanced distributed multi-channel reader anti-collision algorithm for RFID environments which is based on the distributed multi-channel reader anti-collision algorithm for RFID environments (called DiMCA). We proposes a monitor method to decide whether reader receive the latest control news after it selected the data channel. The simulation result shows that it improves interrogation delay.
Directory of Open Access Journals (Sweden)
Gelle Guillaume
2007-01-01
Full Text Available A new algorithm estimating channel impulse response (CIR length and noise variance for orthogonal frequency-division multiplexing (OFDM systems with adaptive guard interval (GI length is proposed. To estimate the CIR length and the noise variance, the different statistical characteristics of the additive noise and the mobile radio channels are exploited. This difference is due to the fact that the variance of the channel coefficients depends on the position within the CIR, whereas the noise variance of each estimated channel tap is equal. Moreover, the channel can vary rapidly, but its length changes more slowly than its coefficients. An auxiliary function is established to distinguish these characteristics. The CIR length and the noise variance are estimated by varying the parameters of this function. The proposed method provides reliable information of the estimated CIR length and the noise variance even at signal-to-noise ratio (SNR of 0 dB. This information can be applied to an OFDM system with adaptive GI length, where the length of the GI is adapted to the current length of the CIR. The length of the GI can therefore be optimized. Consequently, the spectral efficiency of the system is increased.
Directory of Open Access Journals (Sweden)
Van Duc Nguyen
2007-02-01
Full Text Available A new algorithm estimating channel impulse response (CIR length and noise variance for orthogonal frequency-division multiplexing (OFDM systems with adaptive guard interval (GI length is proposed. To estimate the CIR length and the noise variance, the different statistical characteristics of the additive noise and the mobile radio channels are exploited. This difference is due to the fact that the variance of the channel coefficients depends on the position within the CIR, whereas the noise variance of each estimated channel tap is equal. Moreover, the channel can vary rapidly, but its length changes more slowly than its coefficients. An auxiliary function is established to distinguish these characteristics. The CIR length and the noise variance are estimated by varying the parameters of this function. The proposed method provides reliable information of the estimated CIR length and the noise variance even at signal-to-noise ratio (SNR of 0 dB. This information can be applied to an OFDM system with adaptive GI length, where the length of the GI is adapted to the current length of the CIR. The length of the GI can therefore be optimized. Consequently, the spectral efficiency of the system is increased.
A Motion Estimation Algorithm Using DTCWT and ARPS
Directory of Open Access Journals (Sweden)
Unan Y. Oktiawati
2013-09-01
Full Text Available In this paper, a hybrid motion estimation algorithm utilizing the Dual Tree Complex Wavelet Transform (DTCWT and the Adaptive Rood Pattern Search (ARPS block is presented. The proposed algorithm first transforms each video sequence with DTCWT. The frame n of the video sequence is used as a reference input and the frame n+2 is used to find the motion vector. Next, the ARPS block search algorithm is carried out and followed by an inverse DTCWT. The motion compensation is then carried out on each inversed frame n and motion vector. The results show that PSNR can be improved for mobile device without depriving its quality. The proposed algorithm also takes less memory usage compared to the DCT-based algorithm. The main contribution of this work is a hybrid wavelet-based motion estimation algorithm for mobile devices. Other contribution is the visual quality scoring system as used in section 6.
Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
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Siyu Liu
2017-10-01
Full Text Available This paper focuses on the iterative parameter estimation algorithms for dual-frequency signal models that are disturbed by stochastic noise. The key of the work is to overcome the difficulty that the signal model is a highly nonlinear function with respect to frequencies. A gradient-based iterative (GI algorithm is presented based on the gradient search. In order to improve the estimation accuracy of the GI algorithm, a Newton iterative algorithm and a moving data window gradient-based iterative algorithm are proposed based on the moving data window technique. Comparative simulation results are provided to illustrate the effectiveness of the proposed approaches for estimating the parameters of signal models.
Validation of Core Temperature Estimation Algorithm
2016-01-29
a constant heart rate until the CT estimate converges, with convergence defined as the time for CT to fall within 0.5% of the final temperature...range of error within which 95% of the estimated errors should fall assuming a normal distribution, which is consistent with the error distribution...and B.C. Ruby , "Core-Temperature Sensor Ingestion Timing and Measurement Variability," Jounral of Athletic Training, vol. 45, no. 6, pp. 594–600
Algorithm of the managing systems state estimation
Directory of Open Access Journals (Sweden)
Skubilin M. D.
2010-02-01
Full Text Available The possibility of an electronic estimation of automatic and automated managing systems state is analyzed. An estimation of a current state (functional readiness of technical equipment and person-operator as integrated system allows to take operatively adequate measures on an exception and-or minimisation of consequences of system’s transition in a supernumerary state. The offered method is universal enough and can be recommended for normalisation of situations on transport, mainly in aircraft.
Semiblind channel estimation for MIMO-OFDM systems
Chen, Yi-Sheng; Song, Jyu-Han
2012-12-01
This article proposes a semiblind channel estimation method for multiple-input multiple-output orthogonal frequency-division multiplexing systems based on circular precoding. Relying on the precoding scheme at the transmitters, the autocorrelation matrix of the received data induces a structure relating the outer product of the channel frequency response matrix and precoding coefficients. This structure makes it possible to extract information about channel product matrices, which can be used to form a Hermitian matrix whose positive eigenvalues and corresponding eigenvectors yield the channel impulse response matrix. This article also tests the resistance of the precoding design to finite-sample estimation errors, and explores the effects of the precoding scheme on channel equalization by performing pairwise error probability analysis. The proposed method is immune to channel zero locations, and is reasonably robust to channel order overestimation. The proposed method is applicable to the scenarios in which the number of transmitters exceeds that of the receivers. Simulation results demonstrate the performance of the proposed method and compare it with some existing methods.
Comparison of two global digital algorithms for Minkowski tensor estimation
DEFF Research Database (Denmark)
are confirmed by simulations on test sets, and recommendations for input arguments of the algorithms are given. For increasing resolutions, we obtain more accurate estimators for the Minkowski tensors. Digitisations of more complicated objects are shown to require higher resolutions....
Error estimation for the linearized auto-localization algorithm.
Guevara, Jorge; Jiménez, Antonio R; Prieto, Jose Carlos; Seco, Fernando
2012-01-01
The Linearized Auto-Localization (LAL) algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs), using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons' positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL), the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method.
Error Estimation for the Linearized Auto-Localization Algorithm
Directory of Open Access Journals (Sweden)
Fernando Seco
2012-02-01
Full Text Available The Linearized Auto-Localization (LAL algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs, using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons’ positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL, the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method.
Simulation of devices mobility to estimate wireless channel quality metrics in 5G networks
Orlov, Yu.; Fedorov, S.; Samuylov, A.; Gaidamaka, Yu.; Molchanov, D.
2017-07-01
The problem of channel quality estimation for devices in a wireless 5G network is formulated. As a performance metrics of interest we choose the signal-to-interference-plus-noise ratio, which depends essentially on the distance between the communicating devices. A model with a plurality of moving devices in a bounded three-dimensional space and a simulation algorithm to determine the distances between the devices for a given motion model are devised.
Dabiri, Mohammad Taghi; Sadough, Seyed Mohammad Sajad; Khalighi, Mohammad Ali
2017-11-01
In the free-space optical (FSO) links, atmospheric turbulence and pointing errors lead to scintillation in the received signal. Due to its ease of implementation, intensity modulation with direct detection (IM/DD) based on ON-OFF-keying(OOK) is a popular signaling scheme in these systems. For long-haul FSO links, avalanche photo diodes (APDs) are commonly used, which provide an internal gain in photo-detection, allowing larger transmission ranges, as compared with PIN photo-detector (PD) counterparts. Since optimal OOK detection at the receiver requires the knowledge of the instantaneous channel fading coefficient, channel estimation is an important task that can considerably impact the link performance. In this paper, we investigate the channel estimation issue when using an APD at the receiver. Here, optimal signal detection is quite more delicate than in the case of using a PIN PD. In fact, given that APD-based receivers are usually shot-noise limited, the receiver noise will have a different distribution depending on whether the transmitted bit is '0' or '1', and moreover, its statistics are further affected by the scintillation. To deal with this, we first consider minimum mean-square-error (MMSE), maximum a posteriori probability (MAP) and maximum likelihood (ML) channel estimation over an observation window encompassing several consecutive received OOK symbols. Due to the high computational complexity of these methods, in a second step, we propose an ML channel estimator based on the expectation-maximization (EM) algorithm which has a low implementation complexity, making it suitable for high data-rate FSO communications. Numerical results show that for a sufficiently large observation window, by using the proposed EM channel estimator, we can achieve bit error rate performance very close to that with perfect channel state information. We also derive the Cramer-Rao lower bound (CRLB) of MSE of estimation errors and show that for a large enough observation
Knee Angle Estimation Algorithm for Myoelectric Control of Active Transfemoral Prostheses
Delis, Alberto López; de Carvalho, João Luiz Azevedo; Da Rocha, Adson Ferreira; de Oliveira Nascimento, Francisco Assis; Borges, Geovany Araújo
This paper presents a bioinstrumentation system for the acquisition and pre-processing of surface electromyographic (SEMG) signals, and a knee angle estimation algorithm for control of active transfemoral leg prostheses, using methods for feature extraction and classification of myoelectric signal patterns. The presented microcontrolled bioinstrumentation system is capable of recording up to four SEMG channels, and one electrogoniometer channel. The proposed neural myoelectric controller algorithm is capable of predicting the intended knee joint angle from the measured SEMG signals. The algorithm is designed in three stages: feature extraction, using auto-regressive model and amplitude histogram; feature projection, using self organizing maps; and pattern classification, using a Levenberg-Marquardt neural network. The use of SEMG signals and additional mechanical information such as that provided by the electrogoniometer may improve precision in the control of leg prostheses. Preliminary results are presented.
Bayesian fusion algorithm for improved oscillometric blood pressure estimation.
Forouzanfar, Mohamad; Dajani, Hilmi R; Groza, Voicu Z; Bolic, Miodrag; Rajan, Sreeraman; Batkin, Izmail
2016-11-01
A variety of oscillometric algorithms have been recently proposed in the literature for estimation of blood pressure (BP). However, these algorithms possess specific strengths and weaknesses that should be taken into account before selecting the most appropriate one. In this paper, we propose a fusion method to exploit the advantages of the oscillometric algorithms and circumvent their limitations. The proposed fusion method is based on the computation of the weighted arithmetic mean of the oscillometric algorithms estimates, and the weights are obtained using a Bayesian approach by minimizing the mean square error. The proposed approach is used to fuse four different oscillometric blood pressure estimation algorithms. The performance of the proposed method is evaluated on a pilot dataset of 150 oscillometric recordings from 10 subjects. It is found that the mean error and standard deviation of error are reduced relative to the individual estimation algorithms by up to 7 mmHg and 3 mmHg in estimation of systolic pressure, respectively, and by up to 2 mmHg and 3 mmHg in estimation of diastolic pressure, respectively. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.
Algorithms for estimating blood velocities using ultrasound
DEFF Research Database (Denmark)
Jensen, Jørgen Arendt
2000-01-01
have been developed for performing the estimation, and the various approaches are described. The current systems only display the velocity along the ultrasound beam direction and a velocity transverse to the beam is not detected. This is a major problem in these systems, since most blood vessels...
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available This paper proposes a turbo joint channel estimation, synchronization, and decoding scheme for coded multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM systems. The effects of carrier frequency offset (CFO, sampling frequency offset (SFO, and channel impulse responses (CIRs on the received samples are analyzed and explored to develop the turbo decoding process and vector recursive least squares (RLSs algorithm for joint CIR, CFO, and SFO tracking. For burst transmission, with initial estimates derived from the preamble, the proposed scheme can operate without the need of pilot tones during the data segment. Simulation results show that the proposed turbo joint channel estimation, synchronization, and decoding scheme offers fast convergence and low mean squared error (MSE performance over quasistatic Rayleigh multipath fading channels. The proposed scheme can be used in a coded MIMO-OFDM transceiver in the presence of multipath fading, carrier frequency offset, and sampling frequency offset to provide a bit error rate (BER performance comparable to that in an ideal case of perfect synchronization and channel estimation over a wide range of SFO values.
Directory of Open Access Journals (Sweden)
Ko ChiChung
2009-01-01
Full Text Available This paper proposes a turbo joint channel estimation, synchronization, and decoding scheme for coded multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM systems. The effects of carrier frequency offset (CFO, sampling frequency offset (SFO, and channel impulse responses (CIRs on the received samples are analyzed and explored to develop the turbo decoding process and vector recursive least squares (RLSs algorithm for joint CIR, CFO, and SFO tracking. For burst transmission, with initial estimates derived from the preamble, the proposed scheme can operate without the need of pilot tones during the data segment. Simulation results show that the proposed turbo joint channel estimation, synchronization, and decoding scheme offers fast convergence and low mean squared error (MSE performance over quasistatic Rayleigh multipath fading channels. The proposed scheme can be used in a coded MIMO-OFDM transceiver in the presence of multipath fading, carrier frequency offset, and sampling frequency offset to provide a bit error rate (BER performance comparable to that in an ideal case of perfect synchronization and channel estimation over a wide range of SFO values.
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Zhang Han
2009-01-01
Full Text Available We address the problem of superimposed trainings- (STs- based linearly time-varying (LTV channel estimation and symbol detection for orthogonal frequency-division multiplexing access (OFDMA systems at the uplink receiver. The LTV channel coefficients are modeled by truncated discrete Fourier bases (DFBs. By judiciously designing the superimposed pilot symbols, we estimate the LTV channel transfer functions over the whole frequency band by using a weighted average procedure, thereby providing validity for adaptive resource allocation. We also present a performance analysis of the channel estimation approach to derive a closed-form expression for the channel estimation variances. In addition, an iterative symbol detector is presented to mitigate the superimposed training effects on information sequence recovery. By the iterative mitigation procedure, the demodulator achieves a considerable gain in signal-interference ratio and exhibits a nearly indistinguishable symbol error rate (SER performance from that of frequency-division multiplexed trainings. Compared to existing frequency-division multiplexed training schemes, the proposed algorithm does not entail any additional bandwidth while with the advantage for system adaptive resource allocation.
Channel estimation for physical layer network coding systems
Gao, Feifei; Wang, Gongpu
2014-01-01
This SpringerBrief presents channel estimation strategies for the physical later network coding (PLNC) systems. Along with a review of PLNC architectures, this brief examines new challenges brought by the special structure of bi-directional two-hop transmissions that are different from the traditional point-to-point systems and unidirectional relay systems. The authors discuss the channel estimation strategies over typical fading scenarios, including frequency flat fading, frequency selective fading and time selective fading, as well as future research directions. Chapters explore the performa
Directory of Open Access Journals (Sweden)
Biguesh Mehrzad
2004-01-01
Full Text Available In mobile communication systems with multisensor antennas at base stations, downlink channel estimation plays a key role because accurate channel estimates are needed for transmit beamforming. One efficient approach to this problem is channel probing with feedback. In this method, the base station array transmits probing (training signals. The channel is then estimated from feedback reports provided by the users. This paper studies the performance of the channel probing method with feedback using a multisensor base station antenna array and single-sensor users. The least squares (LS, linear minimum mean square error (LMMSE, and a new scaled LS (SLS approaches to the channel estimation are studied. Optimal choice of probing signals is investigated for each of these techniques and their channel estimation performances are analyzed. In the case of multiple LS channel estimates, the best linear unbiased estimation (BLUE scheme for their linear combining is developed and studied.
Directory of Open Access Journals (Sweden)
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
Bhuiyan, Mohammad Zahidul H.; Lohan, Elena Simona; Renfors, Markku
2007-12-01
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
Algorithms of optimum location of sensors for solidification parameters estimation
Directory of Open Access Journals (Sweden)
J. Mendakiewicz
2010-10-01
Full Text Available The algorithms of optimal sensor location for estimation of solidification parameters are discussed. These algorithms base on the Fisher Information Matrix and A-optimality or D-optimality criterion. Numerical examples of planning algorithms are presented and next foroptimal position of sensors the inverse problems connected with the identification of unknown parameters are solved. The examplespresented concern the simultaneous estimation of mould thermophysical parameters (volumetric specific heat and thermal conductivityand also the components of volumetric latent heat of cast iron.
Power system static state estimation using Kalman filter algorithm
Directory of Open Access Journals (Sweden)
Saikia Anupam
2016-01-01
Full Text Available State estimation of power system is an important tool for operation, analysis and forecasting of electric power system. In this paper, a Kalman filter algorithm is presented for static estimation of power system state variables. IEEE 14 bus system is employed to check the accuracy of this method. Newton Raphson load flow study is first carried out on our test system and a set of data from the output of load flow program is taken as measurement input. Measurement inputs are simulated by adding Gaussian noise of zero mean. The results of Kalman estimation are compared with traditional Weight Least Square (WLS method and it is observed that Kalman filter algorithm is numerically more efficient than traditional WLS method. Estimation accuracy is also tested for presence of parametric error in the system. In addition, numerical stability of Kalman filter algorithm is tested by considering inclusion of zero mean errors in the initial estimates.
On the secrecy capacity of the wiretap channel with imperfect main channel estimation
Rezki, Zouheir
2014-10-01
We study the secrecy capacity of fast fading channels under imperfect main channel (between the transmitter and the legitimate receiver) estimation at the transmitter. Lower and upper bounds on the ergodic secrecy capacity are derived for a class of independent identically distributed (i.i.d.) fading channels. The achievable rate follows from a standard wiretap code in which a simple on-off power control is employed along with a Gaussian input. The upper bound is obtained using an appropriate correlation scheme of the main and eavesdropper channels and is the best known upper bound so far. The upper and lower bounds coincide with recently derived ones in case of perfect main CSI. Furthermore, the upper bound is tight in case of no main CSI, where the secrecy capacity is equal to zero. Asymptotic analysis at high and low signal-to-noise ratio (SNR) is also given. At high SNR, we show that the capacity is bounded by providing upper and lower bounds that depend on the channel estimation error. At low SNR, however, we prove that the secrecy capacity is asymptotically equal to the capacity of the main channel as if there were no secrecy constraint. Numerical results are provided for i.i.d. Rayleigh fading channels.
Parameter Estimation of Damped Compound Pendulum Using Bat Algorithm
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Saad Mohd Sazli
2016-01-01
Full Text Available In this study, the parameter identification of the damped compound pendulum system is proposed using one of the most promising nature inspired algorithms which is Bat Algorithm (BA. The procedure used to achieve the parameter identification of the experimental system consists of input-output data collection, ARX model order selection and parameter estimation using bat algorithm (BA method. PRBS signal is used as an input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the autoregressive with exogenous input (ARX model. The performance of the model is validated using mean squares error (MSE between the actual and predicted output responses of the models. Finally, comparative study is conducted between BA and the conventional estimation method (i.e. Least Square. Based on the results obtained, MSE produce from Bat Algorithm (BA is outperformed the Least Square (LS method.
Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems
Energy Technology Data Exchange (ETDEWEB)
Wang, Dexin; Yang, Liuqing; Florita, Anthony; Alam, S.M. Shafiul; Elgindy, Tarek; Hodge, Bri-Mathias
2017-04-24
The deregulation of the power system and the incorporation of generation from renewable energy sources recessitates faster state estimation in the smart grid. Distributed state estimation (DSE) has become a promising and scalable solution to this urgent demand. In this paper, we investigate the regionalization algorithms for the power system, a necessary step before distributed state estimation can be performed. To the best of the authors' knowledge, this is the first investigation on automatic regionalization (AR). We propose three spectral clustering based AR algorithms. Simulations show that our proposed algorithms outperform the two investigated manual regionalization cases. With the help of AR algorithms, we also show how the number of regions impacts the accuracy and convergence speed of the DSE and conclude that the number of regions needs to be chosen carefully to improve the convergence speed of DSEs.
An O(NlogN Algorithm for Region Definition Using Channels/Switchboxes and Ordering Assignment
Directory of Open Access Journals (Sweden)
Jin-Tai Yan
1996-01-01
Full Text Available For a building block placement, the routing space can be further partitioned into channels and switchboxes. In general, the definition of switchboxes releases the cyclic channel precedence constraints and further yields a safe routing ordering process. However, switchbox routing is more difficult than channel routing. In this paper, an O(NlogN region definition and ordering assignment (RDAOA algorithm is proposed to minimize the number of switchboxes for the routing phase, where N is the number of vertices in a channel precedence graph. Several examples have been tested on the proposed algorithm, and the experimental results are listed and compared.
On Channel Estimation for Analog Network Coding in a Frequency-Selective Fading Channel
Directory of Open Access Journals (Sweden)
Sjödin Tomas
2011-01-01
Full Text Available Recently, broadband analog network coding (ANC was introduced for high-speed transmission over the wireless (frequency-selective fading channel. However, ANC requires the knowledge of channel state information (CSI for self-information removal and coherent signal detection. In ANC, the users' pilot signals interfere during the first slot, which renders the relay unable to estimate CSIs of different users, and, consequently, four time-slot pilot-assisted channel estimation (CE is required to avoid interference. Naturally, this will reduce the capacity of ANC scheme. In this paper, we theoretically analyze the bit error rate (BER performance of bi-directional broadband ANC communication based on orthogonal frequency division multiplexing (OFDM radio access. We also theoretically analyze the performance of the channel estimator's mean square error (MSE. The analysis is based on the assumption of perfect timing and frequency synchronization. The achievable BER performance and the estimator's MSE for broadband ANC is evaluated by numerical and computer simulation. We discuss how, and by how much, the imperfect knowledge of CSI affects the BER performance of broadband ANC. It is shown that the CE scheme achieves a slightly higher BER in comparison with ideal CE case for a low and moderate mobile terminal speed in a frequency-selective fading channel.
Optimum Quantization and Parallel Algorithms for Nonlinear State Estimation,
estimation problem can be reformulated so as to use parallel computers effectively to approximate the optimal state estimate. The problem of...simple hardware. The parallel algorithms described in the paper are suitable for a large class of parallel computers . (Author)
Using transformation algorithms to estimate (co)variance ...
African Journals Online (AJOL)
... to multiple traits by the use of canonical transformations. A computing strategy is developed for use on large data sets employing two different REML algorithms for the estimation of (co)variance components. Results from a simulation study indicate that (co)variance components can be estimated efficiently at a low cost on ...
Weighted-noise threshold based channel estimation for OFDM ...
Indian Academy of Sciences (India)
Orthogonal frequency division multiplexing (OFDM) technology is the key to evolving telecommunication standards including 3GPP-LTE Advanced and WiMAX. Reliability of any OFDM system increases with improvedmean square error performance (MSE) of its channel estimator (CE). Particularly, a least squares (LS) ...
DOA estimation and mutual coupling calibration with the SAGE algorithm
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Xiong Kunlai
2014-12-01
Full Text Available In this paper, a novel algorithm is presented for direction of arrival (DOA estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization (SAGE algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time, our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration.
Efficient AM Algorithms for Stochastic ML Estimation of DOA
Directory of Open Access Journals (Sweden)
Haihua Chen
2016-01-01
Full Text Available The estimation of direction-of-arrival (DOA of signals is a basic and important problem in sensor array signal processing. To solve this problem, many algorithms have been proposed, among which the Stochastic Maximum Likelihood (SML is one of the most concerned algorithms because of its high accuracy of DOA. However, the estimation of SML generally involves the multidimensional nonlinear optimization problem. As a result, its computational complexity is rather high. This paper addresses the issue of reducing computational complexity of SML estimation of DOA based on the Alternating Minimization (AM algorithm. We have the following two contributions. First using transformation of matrix and properties of spatial projection, we propose an efficient AM (EAM algorithm by dividing the SML criterion into two components. One depends on a single variable parameter while the other does not. Second when the array is a uniform linear array, we get the irreducible form of the EAM criterion (IAM using polynomial forms. Simulation results show that both EAM and IAM can reduce the computational complexity of SML estimation greatly, while IAM is the best. Another advantage of IAM is that this algorithm can avoid the numerical instability problem which may happen in AM and EAM algorithms when more than one parameter converges to an identical value.
Directory of Open Access Journals (Sweden)
Simoens Frederik
2006-01-01
Full Text Available This paper concerns channel tracking in a multiantenna context for correlated flat-fading channels obeying a Gauss-Markov model. It is known that data-aided tracking of fast-fading channels requires a lot of pilot symbols in order to achieve sufficient accuracy, and hence decreases the spectral efficiency. To overcome this problem, we design a code-aided estimation scheme which exploits information from both the pilot symbols and the unknown coded data symbols. The algorithm is derived based on a factor graph representation of the system and application of the sum-product algorithm. The sum-product algorithm reveals how soft information from the decoder should be exploited for the purpose of estimation and how the information bits can be detected. Simulation results illustrate the effectiveness of our approach.
Radaydeh, Redha Mahmoud Mesleh
2010-01-01
The performance of transmit antenna selection for threshold-based maximal ratio combining (MRC) diversity receivers in the presence of multiple co-channel interfering signals is studied. The impact of imperfect channel estimation of desired user signals is considered, and the effect of phase and time misalignments between desired and undesired signals is incorporated in the analysis. Precise formulation for Nakagami-m faded interfering signals is presented. The analysis is applicable for arbitrary transmit antenna selection, which is based on the receiver combined signal-to-noise ratios (SNRs) or combined signal-to-interference-plus-noise ratios (SINRs) for different transmit channels. New expressions for the distribution of combined SINR and outage probability performance are derived considering SNR-based as well as SINR-based selection algorithms. The results can be used to study the performance of different system architectures under various channel conditions when the implementation complexity is of interest. ©2010 IEEE.
On Channel Estimation for OFDM/TDM Using MMSE-FDE in a Fast Fading Channel
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Gacanin Haris
2009-01-01
Full Text Available Abstract MMSE-FDE can improve the transmission performance of OFDM combined with time division multiplexing (OFDM/TDM, but knowledge of the channel state information and the noise variance is required to compute the MMSE weight. In this paper, a performance evaluation of OFDM/TDM using MMSE-FDE with pilot-assisted channel estimation over a fast fading channel is presented. To improve the tracking ability against fast fading a robust pilot-assisted channel estimation is presented that uses time-domain filtering on a slot-by-slot basis and frequency-domain interpolation. We derive the mean square error (MSE of the channel estimator and then discuss a tradeoff between improving the tracking ability against fading and the noise reduction. The achievable bit error rate (BER performance is evaluated by computer simulation and compared with conventional OFDM. It is shown that the OFDM/TDM using MMSE-FDE achieves a lower BER and a better tracking ability against fast fading in comparison with conventional OFDM.
Diamond recognition algorithm using two-channel x-ray radiographic separator
Nikolaev, Dmitry P.; Gladkov, Andrey; Chernov, Timofey; Bulatov, Konstantin
2015-02-01
In this paper real time classification method for two-channel X-ray radiographic diamond separation is discussed. Proposed method does not require direct hardware calibration but uses sample images as a train dataset. It includes online dynamic time warping algorithm for inter-channel synchronization. Additionally, algorithms of online source signal control are discussed, including X-ray intensity control, optical noise detection and sensor occlusion detection.
Channel estimation in DFT-based offset-QAM OFDM systems.
Zhao, Jian
2014-10-20
Offset quadrature amplitude modulation (offset-QAM) orthogonal frequency division multiplexing (OFDM) exhibits enhanced net data rates compared to conventional OFDM, and reduced complexity compared to Nyquist FDM (N-FDM). However, channel estimation in discrete-Fourier-transform (DFT) based offset-QAM OFDM is different from that in conventional OFDM and requires particular study. In this paper, we derive a closed-form expression for the demultiplexed signal in DFT-based offset-QAM systems and show that although the residual crosstalk is orthogonal to the decoded signal, its existence degrades the channel estimation performance when the conventional least-square method is applied. We propose and investigate four channel estimation algorithms for offset-QAM OFDM that vary in terms of performance, complexity, and tolerance to system parameters. It is theoretically and experimentally shown that simple channel estimation can be realized in offset-QAM OFDM with the achieved performance close to the theoretical limit. This, together with the existing advantages over conventional OFDM and N-FDM, makes this technology very promising for optical communication systems.
Spacecraft Angular Velocity Estimation Algorithm Based on Orientation Quaternion Measurements
Directory of Open Access Journals (Sweden)
M. V. Li
2016-01-01
Full Text Available The spacecraft (SC mission involves providing the appropriate orientation and stabilization of the associated axes in space. One of the main sources of information for the attitude control system is the angular rate sensor blocks. One way to improve a reliability of the system is to provide a back up of the control algorithms in case of failure of these blocks. To solve the problem of estimation of SP angular velocity vector in the inertial system of coordinates with a lack of information from the angular rate sensors is supposed the use of orientation data from the star sensors; in this case at each clock of the onboard digital computer. The equations in quaternions are used to describe the kinematics of rotary motion. Their approximate solution is used to estimate the angular velocity vector. Methods of modal control and multi-dimensional decomposition of a control object are used to solve the problem of observation and identification of the angular rates. These methods enabled us to synthesize the SP angular velocity vector estimation algorithm and obtain the equations, which relate the error quaternion with the calculated estimate of the angular velocity. Mathematical modeling was carried out to test the algorithm. Cases of different initial conditions were simulated. Time between orientation quaternion measurements and angular velocity of the model was varied. The algorithm was compared with a more accurate algorithm, built on more complete equations. Graphs of difference in angular velocity estimation depending on the number of iterations are presented. The difference in angular velocity estimation is calculated from results of the synthesized algorithm and the algorithm for more accurate equations. Graphs of error distribution for angular velocity estimation with initial conditions being changed are also presented, and standard deviations of estimation errors are calculated. The synthesized algorithm is inferior in accuracy assessment to
White matter lesion segmentation using robust parameter estimation algorithms
Yang, Faguo; Zhu, Litao; Jiang, Tianzi
2003-05-01
White matter lesions are common brain abnormalities. In this paper, we introduce an automatic algorithm for segmentation of white matter lesions from brain MRI images. The intensities of each tissue is assumed to be Gaussian distributed, whose parameters (mean vector and covariance matrix) are estimated using a tissue distribution model. And then a measure is defined to indicate in how much content a voxel belongs to the lesions. Experimental results demonstrate that our algorithm works well.
A Virtual Channels Scheduling Algorithm with Broad Applicability Based on Movable Boundary
Directory of Open Access Journals (Sweden)
Ye Tian
2013-01-01
Full Text Available A virtual channels scheduling algorithm with broad applicability based on movable boundary is proposed. According to the types of date sources, transmission time slots are divided into synchronous ones and asynchronous ones with a movable boundary between them. During the synchronous time slots, the virtual channels are scheduled with a polling algorithm; during the asynchronous time slots, the virtual channels are scheduled with an algorithm based on virtual channel urgency and frame urgency. If there are no valid frames in the corresponding VC at a certain synchronous time slot, a frame of the other synchronous VCs or asynchronous VCs will be transmitted through the physical channel. Only when there are no valid frames in all VCs would an idle frame be generated and transmitted. Experiments show that the proposed algorithm yields much lower scheduling delay and higher channel utilization ratio than those based on unmovable boundary or virtual channel urgency in many kinds of sources. Therefore, broad applicability can be achieved by the proposed algorithm.
Algorithms and estimators for summarization of unaggregated data streams
DEFF Research Database (Denmark)
Cohen, Edith; Duffield, Nick; Kaplan, Haim
2014-01-01
. A summarization algorithm, such as Cisco's sampled NetFlow, is applied to IP packet streams that consist of multiple interleaving IP flows. We develop sampling algorithms and unbiased estimators which address sources of inefficiency in current methods. First, we design tunable algorithms whereas currently...... a single parameter (the sampling rate) controls utilization of both memory and processing/access speed (which means that it has to be set according to the bottleneck resource). Second, we make a better use of the memory hierarchy, which involves exporting partial summaries to slower storage during...
Data-driven algorithm to estimate friction in automobile engine
DEFF Research Database (Denmark)
Stotsky, Alexander A.
2010-01-01
Algorithms based on the oscillations of the engine angular rotational speed under fuel cutoff and no-load were proposed for estimation of the engine friction torque. The recursive algorithm to restore the periodic signal is used to calculate the amplitude of the engine speed signal at fuel cutoff....... The values of the friction torque in the corresponding table entries are updated at acquiring new measurements of the friction moment. A new, data-driven algorithm for table adaptation on the basis of stepwise regression was developed and verified using the six-cylinder Volvo engine....
Pose estimation for augmented reality applications using genetic algorithm.
Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen
2005-12-01
This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.
Maximum Likelihood Blind Channel Estimation for Space-Time Coding Systems
Directory of Open Access Journals (Sweden)
Hakan A. Çırpan
2002-05-01
Full Text Available Sophisticated signal processing techniques have to be developed for capacity enhancement of future wireless communication systems. In recent years, space-time coding is proposed to provide significant capacity gains over the traditional communication systems in fading wireless channels. Space-time codes are obtained by combining channel coding, modulation, transmit diversity, and optional receive diversity in order to provide diversity at the receiver and coding gain without sacrificing the bandwidth. In this paper, we consider the problem of blind estimation of space-time coded signals along with the channel parameters. Both conditional and unconditional maximum likelihood approaches are developed and iterative solutions are proposed. The conditional maximum likelihood algorithm is based on iterative least squares with projection whereas the unconditional maximum likelihood approach is developed by means of finite state Markov process modelling. The performance analysis issues of the proposed methods are studied. Finally, some simulation results are presented.
Global stereo matching algorithm based on disparity range estimation
Li, Jing; Zhao, Hong; Gu, Feifei
2017-09-01
The global stereo matching algorithms are of high accuracy for the estimation of disparity map, but the time-consuming in the optimization process still faces a curse, especially for the image pairs with high resolution and large baseline setting. To improve the computational efficiency of the global algorithms, a disparity range estimation scheme for the global stereo matching is proposed to estimate the disparity map of rectified stereo images in this paper. The projective geometry in a parallel binocular stereo vision is investigated to reveal a relationship between two disparities at each pixel in the rectified stereo images with different baselines, which can be used to quickly obtain a predicted disparity map in a long baseline setting estimated by that in the small one. Then, the drastically reduced disparity ranges at each pixel under a long baseline setting can be determined by the predicted disparity map. Furthermore, the disparity range estimation scheme is introduced into the graph cuts with expansion moves to estimate the precise disparity map, which can greatly save the cost of computing without loss of accuracy in the stereo matching, especially for the dense global stereo matching, compared to the traditional algorithm. Experimental results with the Middlebury stereo datasets are presented to demonstrate the validity and efficiency of the proposed algorithm.
A novel time of arrival estimation algorithm using an energy detector receiver in MMW systems
Liang, Xiaolin; Zhang, Hao; Lyu, Tingting; Xiao, Han; Gulliver, T. Aaron
2017-12-01
This paper presents a new time of arrival (TOA) estimation technique using an improved energy detection (ED) receiver based on the empirical mode decomposition (EMD) in an impulse radio (IR) 60 GHz millimeter wave (MMW) system. A threshold is employed via analyzing the characteristics of the received energy values with an extreme learning machine (ELM). The effect of the channel and integration period on the TOA estimation is evaluated. Several well-known ED-based TOA algorithms are used to compare with the proposed technique. It is shown that this ELM-based technique has lower TOA estimation error compared to other approaches and provides robust performance with the IEEE 802.15.3c channel models.
Khamukhin, A. A.
2017-02-01
Simple navigation algorithms are needed for small autonomous unmanned aerial vehicles (UAVs). These algorithms can be implemented in a small microprocessor with low power consumption. This will help to reduce the weight of the UAVs computing equipment and to increase the flight range. The proposed algorithm uses only the number of opaque channels (ommatidia in bees) through which a target can be seen by moving an observer from location 1 to 2 toward the target. The distance estimation is given relative to the distance between locations 1 and 2. The simple scheme of an appositional compound eye to develop calculation formula is proposed. The distance estimation error analysis shows that it decreases with an increase of the total number of opaque channels to a certain limit. An acceptable error of about 2 % is achieved with the angle of view from 3 to 10° when the total number of opaque channels is 21600.
Algorithm Design for Assignment Frequency Channels for Services in CATV Network
Directory of Open Access Journals (Sweden)
Jan Hlubik
2008-01-01
Full Text Available This article describes algorithm design for assignment frequency channels for services in CATV network. QAM modulated channels in designed system are dynamically switched and frequency adapted. On introduction, there is shortly described principle of providing analog video, digital video and data services in CATV network. Object of the article is focused on method of allocation individual TV and data channels on the frequency band. The conclusion compares advantages and disadvantages present and design system.
Directory of Open Access Journals (Sweden)
Çırpan HakanAli
2006-01-01
Full Text Available Focusing on transmit diversity orthogonal frequency-division multiplexing (OFDM transmission through frequency-selective channels, this paper pursues a channel estimation approach in time domain for both space-frequency OFDM (SF-OFDM and space-time OFDM (ST-OFDM systems based on AR channel modelling. The paper proposes a computationally efficient, pilot-aided linear minimum mean-square-error (MMSE time-domain channel estimation algorithm for OFDM systems with transmitter diversity in unknown wireless fading channels. The proposed approach employs a convenient representation of the channel impulse responses based on the Karhunen-Loeve (KL orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion, no matrix inversion is required in the proposed MMSE estimator. Subsequently, optimal rank reduction is applied to obtain significant taps resulting in a smaller computational load on the proposed estimation algorithm. The performance of the proposed approach is studied through the analytical results and computer simulations. In order to explore the performance, the closed-form expression for the average symbol error rate (SER probability is derived for the maximum ratio receive combiner (MRRC. We then consider the stochastic Cramer-Rao lower bound(CRLB and derive the closed-form expression for the random KL coefficients, and consequently exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also analyze the effect of a modelling mismatch on the estimator performance. Simulation results confirm our theoretical analysis and illustrate that the proposed algorithms are capable of tracking fast fading and improving overall performance.
Testing of Gyroless Estimation Algorithms for the Fuse Spacecraft
Harman, R.; Thienel, J.; Oshman, Yaakov
2004-01-01
This paper documents the testing and development of magnetometer-based gyroless attitude and rate estimation algorithms for the Far Ultraviolet Spectroscopic Explorer (FUSE). The results of two approaches are presented, one relies on a kinematic model for propagation, a method used in aircraft tracking, and the other is a pseudolinear Kalman filter that utilizes Euler's equations in the propagation of the estimated rate. Both algorithms are tested using flight data collected over a few months after the failure of two of the reaction wheels. The question of closed-loop stability is addressed. The ability of the controller to meet the science slew requirements, without the gyros, is analyzed.
Transmit Delay Structure Design for Blind Channel Estimation over Multipath Channels
Directory of Open Access Journals (Sweden)
Ding Zhi
2007-01-01
Full Text Available Wireless communications often exploit guard intervals between data blocks to reduce interblock interference in frequency-selective fading channels. Here we propose a dual-branch transmission scheme that utilizes guard intervals for blind channel estimation and equalization. Unlike existing transmit diversity schemes, in which different antennas transmit delayed, zero-padded, or time-reversed versions of the same signal, in the proposed transmission scheme, each antenna transmits an independent data stream. It is shown that for systems with two transmit antennas and one receive antenna, as in the case of one transmit antenna and two receive antennas, blind channel estimation and equalization can be carried out based only on the second-order statistics of symbol-rate sampled channel output. The proposed approach involves no preequalization and has no limitations on channel-zero locations. Moreover, extension of the proposed scheme to systems with multiple receive antennas and/or more than two transmit antennas is discussed. It is also shown that in combination with the threaded layered space-time (TST architecture and turbo coding, significant improvement can be achieved in the overall system performance.
Estimating aquifer channel recharge using optical data interpretation.
Walter, Gary R; Necsoiu, Marius; McGinnis, Ronald
2012-01-01
Recharge through intermittent and ephemeral stream channels is believed to be a primary aquifer recharge process in arid and semiarid environments. The intermittent nature of precipitation and flow events in these channels, and their often remote locations, makes direct flow and loss measurements difficult and expensive. Airborne and satellite optical images were interpreted to evaluate aquifer recharge due to stream losses on the Frio River in south-central Texas. Losses in the Frio River are believed to be a major contributor of recharge to the Edwards Aquifer. The results of this work indicate that interpretation of readily available remote sensing optical images can offer important insights into the spatial distribution of aquifer recharge from losing streams. In cases where upstream gauging data are available, simple visual analysis of the length of the flowing reach downstream from the gauging station can be used to estimate channel losses. In the case of the Frio River, the rate of channel loss estimated from the length of the flowing reach at low flows was about half of the loss rate calculated from in-stream gain-loss measurements. Analysis based on water-surface width and channel slope indicated that losses were mainly in a reach downstream of the mapped recharge zone. The analysis based on water-surface width, however, did not indicate that this method could yield accurate estimates of actual flow in pool and riffle streams, such as the Frio River and similar rivers draining the Edwards Plateau. © 2011, Southwest Research Institute. Ground Water © 2011, National Ground Water Association.
On the ergodic secrecy capacity of the wiretap channel under imperfect main channel estimation
Rezki, Zouheir
2011-11-01
The ergodic secrecy capacity of the wiretap channel is known when the main channel (between the transmitter and the legitimate receiver) state information (CSI) is perfect at the transmitter and the coherence period is sufficiently large to enable random coding arguments in each block. In a fast fading scenario, when the codeword length spans many coherence periods, the secrecy capacity is still not known. In this paper, we present a framework that characterizes this secrecy capacity under imperfect main channel estimation at the transmitter. Inner and outer bounds on the ergodic secrecy capacity are derived for a class of independent identically distributed (i.i.d.) fading channels. The achievable rate is a simple on-off scheme using a Gaussian input. The upper bound is obtained using an appropriate correlation scheme of the main and the eavesdropper channels. The upper and the lower bounds coincide with recently derived ones in the perfect main CSI extreme. Furthermore, the lower bound matches the upper bound in no main CSI extreme, where the secrecy capacity is equal to zero. Numerical results are provided for independent identically distributed (i.i.d.) Rayleigh fading channels. © 2011 IEEE.
Zhou, Xinyu
2016-03-15
A Gaussian multiple-input single-output (MISO) fading channel is considered. We assume that the transmitter, in addition to the statistics of all channel gains, is aware instantaneously of a noisy version of the channel to the legitimate receiver. On the other hand, the legitimate receiver is aware instantaneously of its channel to the transmitter, whereas the eavesdropper instantaneously knows all channel gains. We evaluate an achievable rate using a Gaussian input without indexing an auxiliary random variable. A sufficient condition for beamforming to be optimal is provided. When the number of transmit antennas is large, beamforming also turns out to be optimal. In this case, the maximum achievable rate can be expressed in a simple closed form and scales with the logarithm of the number of transmit antennas. Furthermore, in the case when a noisy estimate of the eavesdropper’s channel is also available at the transmitter, we introduce the SNR difference and the SNR ratio criterions and derive the related optimal transmission strategies and the corresponding achievable rates.
Estimation of flow direction in meandering compound channels
Liu, Xingnian; Zhou, Qin; Huang, Sheng; Guo, Yakun; Liu, Chao
2018-01-01
The flow in the main channel of a meandering compound channel does not occur in the ridge direction because of the effect of the upstream floodplain flows. This study proposes a model for estimating the flow direction in the depth-averaged two-dimensional domain (depth-averaged flow angles) between the entrance and the apex sections. Detailed velocity measurements were performed in the region between the meander entrance section and apex section in a large-scale meandering compound channel. The vertical size of the secondary current cell is highly related to the depth-averaged flow angle; thus, the means of the local flow angles above the secondary current cell and within the cell are separately discussed. The experimental measurements indicate that the mean local flow angle above the cell is equal to the section angle, whereas the mean local flow angle within the cell is equal to zero. The proposed model is validated using published data from five sources. Good agreement is obtained between the predictions and measurements, indicating that the proposed model can accurately estimate the depth-averaged flow direction in the meandering compound channels. Finally, the limitations and application ranges of the model are discussed.
Algorithms for non-linear M-estimation
DEFF Research Database (Denmark)
Madsen, Kaj; Edlund, O; Ekblom, H
1997-01-01
a sequence of estimation problems for linearized models is solved. In the testing we apply four estimators to ten non-linear data fitting problems. The test problems are also solved by the Generalized Levenberg-Marquardt method and standard optimization BFGS method. It turns out that the new method......In non-linear regression, the least squares method is most often used. Since this estimator is highly sensitive to outliers in the data, alternatives have became increasingly popular during the last decades. We present algorithms for non-linear M-estimation. A trust region approach is used, where...
Savaux, Vincent; Louët, Yves; Djoko-Kouam, Moïse; Skrzypczak, Alexandre
2013-12-01
This article presents an iterative minimum mean square error- (MMSE-) based method for the joint estimation of signal-to-noise ratio (SNR) and frequency-selective channel in an orthogonal frequency division multiplexing (OFDM) context. We estimate the SNR thanks to the MMSE criterion and the channel frequency response by means of the linear MMSE (LMMSE). As each estimation requires the other one to be performed, the proposed algorithm is iterative. In this article, a realistic case is considered; i.e., the channel covariance matrix used in LMMSE is supposed to be totally unknown at the receiver and must be estimated. We will theoretically prove that the algorithm converges for a relevantly chosen initialization value. Furthermore simulations show that the algorithm quickly converges to a solution that is close to the one in which the covariance matrix is perfectly known. Compared to existing SNR estimation methods, the algorithm improves the trade-off between the number of required pilots and the SNR estimation quality.
Liao, Anwen
2017-11-01
Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) with hybrid precoding is a promising technique for the future 5G wireless communications. Due to a large number of antennas but a much smaller number of radio frequency (RF) chains, estimating the high-dimensional mmWave massive MIMO channel will bring the large pilot overhead. To overcome this challenge, this paper proposes a super-resolution channel estimation scheme based on two-dimensional (2D) unitary ESPRIT algorithm. By exploiting the angular sparsity of mmWave channels, the continuously distributed angle of arrivals/departures (AoAs/AoDs) can be jointly estimated with high accuracy. Specifically, by designing the uplink training signals at both base station (BS) and mobile station (MS), we first use low pilot overhead to estimate a low-dimensional effective channel, which has the same shift-invariance of array response as the high-dimensional mmWave MIMO channel to be estimated. From the low-dimensional effective channel, the superresolution estimates of AoAs and AoDs can be jointly obtained by exploiting the 2D unitary ESPRIT channel estimation algorithm. Furthermore, the associated path gains can be acquired based on the least squares (LS) criterion. Finally, we can reconstruct the high-dimensional mmWave MIMO channel according to the obtained AoAs, AoDs, and path gains. Simulation results have confirmed that the proposed scheme is superior to conventional schemes with a much lower pilot overhead.
Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network.
Choi, Sangil; Park, Jong Hyuk
2016-12-02
Wireless mesh networks (WMNs) have been considered as one of the key technologies for the configuration of wireless machines since they emerged. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts in the network and also allow them to access the Internet via gateway devices. Wireless routers are typically equipped with multiple radios operating on different channels to increase network throughput. Multicast is a form of communication that delivers data from a source to a set of destinations simultaneously. It is used in a number of applications, such as distributed games, distance education, and video conferencing. In this study, we address a channel assignment problem for multicast in multi-radio multi-channel WMNs. In a multi-radio multi-channel WMN, two nearby nodes will interfere with each other and cause a throughput decrease when they transmit on the same channel. Thus, an important goal for multicast channel assignment is to reduce the interference among networked devices. We have developed a minimum interference channel assignment (MICA) algorithm for multicast that accurately models the interference relationship between pairs of multicast tree nodes using the concept of the interference factor and assigns channels to tree nodes to minimize interference within the multicast tree. Simulation results show that MICA achieves higher throughput and lower end-to-end packet delay compared with an existing channel assignment algorithm named multi-channel multicast (MCM). In addition, MICA achieves much lower throughput variation among the destination nodes than MCM.
Enhanced signal-to-noise ratio estimation for tropospheric lidar channels
Saeed, Umar; Barragan, Rubén; Rocadenbosch, Francesc
2016-04-01
This works combines the fields of tropospheric lidar remote sensing and signal processing to come up with a robust signal-to-noise ratio (SNR) estimator apt for elastic and Raman channels. The estimator uses a combined low-pass / high-pass filtering scheme along with high-order statistics (kurtosis) to estimate the range-dependent signal and noise components with minimum distortion. While low-pass filtering is used to estimate the range-dependent signal level, high-pass filtering is used to estimate the noise component with minimum distortion. From this noise component estimate (a random realization) the noise level (e.g., variance) is computed as a function of range along with error bars. The minimum-distortion specification determines the optimal cut-off de-noising filter frequency and, in turn, the spatial resolution of the SNR estimation algorithm. The proposed SNR estimator has a much wider dynamic range of operation than well-known classic SNR estimation techniques, in which the SNR is directly computed from the mean and standard deviation of the measured noise-corrupted lidar signal along successive adjacent range intervals and where the spatial resolution is just a subjective input from the user's side. Aligned with ACTRIS (http://www.actris.net) WP on "optimization of the processing chain and Single-Calculus Chain (SCC)" the proposed topic is of application to assess lidar reception channel performance and confidence on the detected atmospheric morphology (e.g., cloud base and top, and location of aerosol layers). The SNR algorithm is tested against the classic SNR estimation approach using test-bed synthetic lidar data modelling the UPC multi-spectral lidar. Towards this end, the Nd:YAG UPC elastic-Raman lidar provides aerosol channels in the near-infrared (1064 nm), visible (532 nm), and ultra-violet (355 nm) as well as aerosol Raman and water-vapour channels with fairly varying SNR levels. The SNR estimator is also used to compare SNR levels between
A Fast DOA Estimation Algorithm Based on Polarization MUSIC
Directory of Open Access Journals (Sweden)
R. Guo
2015-04-01
Full Text Available A fast DOA estimation algorithm developed from MUSIC, which also benefits from the processing of the signals' polarization information, is presented. Besides performance enhancement in precision and resolution, the proposed algorithm can be exerted on various forms of polarization sensitive arrays, without specific requirement on the array's pattern. Depending on the continuity property of the space spectrum, a huge amount of computation incurred in the calculation of 4-D space spectrum is averted. Performance and computation complexity analysis of the proposed algorithm is discussed and the simulation results are presented. Compared with conventional MUSIC, it is indicated that the proposed algorithm has considerable advantage in aspects of precision and resolution, with a low computation complexity proportional to a conventional 2-D MUSIC.
A Novel DOA Estimation Algorithm Using Array Rotation Technique
Directory of Open Access Journals (Sweden)
Xiaoyu Lan
2014-03-01
Full Text Available The performance of traditional direction of arrival (DOA estimation algorithm based on uniform circular array (UCA is constrained by the array aperture. Furthermore, the array requires more antenna elements than targets, which will increase the size and weight of the device and cause higher energy loss. In order to solve these issues, a novel low energy algorithm utilizing array base-line rotation for multiple targets estimation is proposed. By rotating two elements and setting a fixed time delay, even the number of elements is selected to form a virtual UCA. Then, the received data of signals will be sampled at multiple positions, which improves the array elements utilization greatly. 2D-DOA estimation of the rotation array is accomplished via multiple signal classification (MUSIC algorithms. Finally, the Cramer-Rao bound (CRB is derived and simulation results verified the effectiveness of the proposed algorithm with high resolution and estimation accuracy performance. Besides, because of the significant reduction of array elements number, the array antennas system is much simpler and less complex than traditional array.
A Genetic Algorithm Approach to Nonlinear Least Squares Estimation
Olinsky, Alan D.; Quinn, John T.; Mangiameli, Paul M.; Chen, Shaw K.
2004-01-01
A common type of problem encountered in mathematics is optimizing nonlinear functions. Many popular algorithms that are currently available for finding nonlinear least squares estimators, a special class of nonlinear problems, are sometimes inadequate. They might not converge to an optimal value, or if they do, it could be to a local rather than…
An Application of Data Mining Algorithms for Shipbuilding Cost Estimation
Kaluzny, B.L.; Barbici, S.; Berg, G.; Chiomento, R.; Derpanis,D.; Jonsson, U.; Shaw, R.H.A.D.; Smit, M.C.; Ramaroson, F.
2011-01-01
This article presents a novel application of known data mining algorithms to the problem of estimating the cost of ship development and construction. The work is a product of North Atlantic Treaty Organization Research and Technology Organization Systems Analysis and Studies 076 Task Group “NATO
Cloud cover estimation optical package: New facility, algorithms and techniques
Krinitskiy, Mikhail
2017-02-01
Short- and long-wave radiation is an important component of surface heat budget over sea and land. For estimating them accurate observations of the cloud cover are needed. While massively observed visually, for building accurate parameterizations cloud cover needs also to be quantified using precise instrumental measurements. Major disadvantages of the most of existing cloud-cameras are associated with their complicated design and inaccuracy of post-processing algorithms which typically result in the uncertainties of 20% to 30% in the camera-based estimates of cloud cover. The accuracy of these types of algorithm in terms of true scoring compared to human-observed values is typically less than 10%. We developed new generation package for cloud cover estimating, which provides much more accurate results and also allows for measuring additional characteristics. New algorithm, namely SAIL GrIx, based on routine approach, also developed for this package. It uses the synthetic controlling index ("grayness rate index") which allows to suppress the background sunburn effect. This makes it possible to increase the reliability of the detection of the optically thin clouds. The accuracy of this algorithm in terms of true scoring became 30%. One more approach, namely SAIL GrIx ML, we have used to increase the cloud cover estimating accuracy is the algorithm that uses machine learning technique along with some other signal processing techniques. Sun disk condition appears to be a strong feature in this kind of models. Artificial Neural Networks type of model demonstrates the best quality. This model accuracy in terms of true scoring increases up to 95,5%. Application of a new algorithm lets us to modify the design of the optical sensing package and to avoid the use of the solar trackers. This made the design of the cloud camera much more compact. New cloud-camera has already been tested in several missions across Atlantic and Indian oceans on board of IORAS research vessels.
Directory of Open Access Journals (Sweden)
P. Beinschob
2010-11-01
Full Text Available In this paper we present a novel approach in Multiple-Input Multiple Output (MIMO Orthogonal Frequency Division Multiplexing (OFDM channel estimation technique based on a Decision Directed Recursive Least Squares (RLS algorithm in which no pilot symbols need to be integrated in the data after a short initial preamble. The novelty and key concept of the proposed technique is the block-wise causal and anti-causal RLS processing that yields two independent processings of RLS along with the associated decisions. Due to the usage of low density parity check (LDPC channel code, the receiver operates with soft information, which enables us to introduce a new modification of the Turbo principle as well as a simple information combining approach based on approximated aposteriori log-likelihood ratios (LLRs. Although the computational complexity is increased by both of our approaches, the latter is relatively less complex than the former. Simulation results show that these implementations outperform the simple RLS-DDCE algorithm and yield lower bit error rates (BER and more accurate channel estimates.
Rainfall Estimation over the Nile Basin using an Adapted Version of the SCaMPR Algorithm
Habib, E. H.; Kuligowski, R. J.; Elshamy, M. E.; Ali, M. A.; Haile, A.; Amin, D.; Eldin, A.
2011-12-01
Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite-derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). This study reports on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self-Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application over the Nile Basin. The algorithm uses a set of rainfall predictors from multi-spectral Infrared cloud top observations and self-calibrates them to a set of predictands from Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as SSM/I, SSMIS, AMSU, AMSR-E, and TMI. The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real-time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA
Head pose estimation algorithm based on deep learning
Cao, Yuanming; Liu, Yijun
2017-05-01
Head pose estimation has been widely used in the field of artificial intelligence, pattern recognition and intelligent human-computer interaction and so on. Good head pose estimation algorithm should deal with light, noise, identity, shelter and other factors robustly, but so far how to improve the accuracy and robustness of attitude estimation remains a major challenge in the field of computer vision. A method based on deep learning for pose estimation is presented. Deep learning with a strong learning ability, it can extract high-level image features of the input image by through a series of non-linear operation, then classifying the input image using the extracted feature. Such characteristics have greater differences in pose, while they are robust of light, identity, occlusion and other factors. The proposed head pose estimation is evaluated on the CAS-PEAL data set. Experimental results show that this method is effective to improve the accuracy of pose estimation.
Performance Evaluation of Proportional Fair Scheduling Algorithm with Measured Channels
DEFF Research Database (Denmark)
Sørensen, Troels Bundgaard; Pons, Manuel Rubio
2005-01-01
Motivated by the promising performance results of the proportional fair (PF) packet scheduling algorithm, often quoted in connection with WCDMA High Speed Downlink Packet Access (HSDPA), we have performed an evaluation of the PF gain in comparison to the simpler round robin (RR) scheduler when...
Directory of Open Access Journals (Sweden)
Vladimir Cipov
2012-01-01
Full Text Available This paper provides a brief survey of the Time of Arrival (ToA ranging method often used in the Range-based localization for node distance estimation. It is focused especially on the Non Line-of-Sight (NLoS channel identification – one of the dominant factors that negative affect the location accuracy. It deals with the improvement of the received signal arrival time determination in the case of NLoS communication with undetected direct path. The low complexity NLoS mitigation method is proposed in this paper that estimates the time of arrival of the missing direct path as a mean of the time delays of reflected paths. This method is implemented in the cooperative positioning algorithm based on the circular trilateration. The IEEE 802.15.4a statistical channel model is used to simulate the mobile node communication.
Tang, Bo-Hui; Wu, Hua-; Li, Zhao-Liang; Nerry, Françoise
2012-07-30
This work addressed the validation of the MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared (MIR) channel, proposed by Tang and Li [Int. J. Remote Sens. 29, 4907 (2008)], with ground-measured data, which were collected from a field campaign that took place in June 2004 at the ONERA (Office National d'Etudes et de Recherches Aérospatiales) center of Fauga-Mauzac, on the PIRRENE (Programme Interdisciplinaire de Recherche sur la Radiométrie en Environnement Extérieur) experiment site [Opt. Express 15, 12464 (2007)]. The leaving-surface spectral radiances measured by a BOMEM (MR250 Series) Fourier transform interferometer were used to calculate the ground brightness temperatures with the combination of the inversion of the Planck function and the spectral response functions of MODIS channels 22 and 23, and then to estimate the ground brightness temperature without the contribution of the solar direct beam and the bidirectional reflectivity by using Tang and Li's proposed algorithm. On the other hand, the simultaneously measured atmospheric profiles were used to obtain the atmospheric parameters and then to calculate the ground brightness temperature without the contribution of the solar direct beam, based on the atmospheric radiative transfer equation in the MIR region. Comparison of those two kinds of brightness temperature obtained by two different methods indicated that the Root Mean Square Error (RMSE) between the brightness temperatures estimated respectively using Tang and Li's algorithm and the atmospheric radiative transfer equation is 1.94 K. In addition, comparison of the hemispherical-directional reflectances derived by Tang and Li's algorithm with those obtained from the field measurements showed that the RMSE is 0.011, which indicates that Tang and Li's algorithm is feasible to retrieve the bidirectional reflectivity in MIR channel from MODIS data.
An algorithm for estimating surface normal from its boundary curves
Directory of Open Access Journals (Sweden)
Jisoon Park
2015-01-01
Full Text Available Recently, along with the improvements of geometry modeling methods using sketch-based interface, there have been a lot of developments in research about generating surface model from 3D curves. However, surfacing a 3D curve network remains an ambiguous problem due to the lack of geometric information. In this paper, we propose a new algorithm for estimating the normal vectors of the 3D curves which accord closely with user intent. Bending energy is defined by utilizing RMF(Rotation-Minimizing Frame of 3D curve, and we estimated this minimal energy frame as the one that accords design intent. The proposed algorithm is demonstrated with surface model creation of various curve networks. The algorithm of estimating geometric information in 3D curves which is proposed in this paper can be utilized to extract new information in the sketch-based modeling process. Also, a new framework of 3D modeling can be expected through the fusion between curve network and surface creating algorithm.
Thermodynamic optimization of conjugate convection from a finned channel using genetic algorithms
Rakshit, Dibakar; Balaji, C.
2005-04-01
For the first time, this study reports the results of numerical investigation of conjugate convection from a finned channel. The computational domain of investigation consists of a horizontal channel with vertical rectangular fins being mounted on outside of the channel. The equations governing two-dimensional, steady, incompressible, constant property laminar flow have been solved for the fluid flowing outside the channel. In doing this, Boussinesq assumption is assumed to be valid for the fluid flowing outside the channel along the fins. For the fluid flowing inside the channel, flow is assumed to be turbulent with forced convection as the mode of heat transfer. From a large volume of numerically generated data correlations have been proposed for (1) Nusselt number and (2) Entropy generated by the system. These correlations are finally used to obtain thermodynamic optimum where in we seek a solution with minimum total entropy generation rate for varying heat duties, by using the state-of-the art Genetic algorithms.
Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En
2015-08-13
A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.
A genetic algorithm to reduce stream channel cross section data
Berenbrock, C.
2006-01-01
A genetic algorithm (GA) was used to reduce cross section data for a hypothetical example consisting of 41 data points and for 10 cross sections on the Kootenai River. The number of data points for the Kootenai River cross sections ranged from about 500 to more than 2,500. The GA was applied to reduce the number of data points to a manageable dataset because most models and other software require fewer than 100 data points for management, manipulation, and analysis. Results indicated that the program successfully reduced the data. Fitness values from the genetic algorithm were lower (better) than those in a previous study that used standard procedures of reducing the cross section data. On average, fitnesses were 29 percent lower, and several were about 50 percent lower. Results also showed that cross sections produced by the genetic algorithm were representative of the original section and that near-optimal results could be obtained in a single run, even for large problems. Other data also can be reduced in a method similar to that for cross section data.
Novel coherent receivers for AF distributed STBC using disintegrated channel estimation
Khan, Fahd Ahmed
2011-05-01
For a single relay network, disintegrated channel estimation (DCE), where the source-relay channel is estimated at the relay and the relay-destination channel is estimated at the destination, gives better performance than the cascaded channel estimation. We derive novel receivers for the relay network with disintegrated channel estimation. The derived receivers do not require channel estimation at the destination, as they use the received pilot signals and the source-relay channel estimate for decoding directly. We also consider the effect of quantized source-relay channel estimate on the performance of the designed receivers. Simulation results show that a performance gain of up to 2.2 dB can be achieved by the new receivers, compared with the conventional mismatched coherent receiver with DCE. © 2011 IEEE.
A Review On Missing Value Estimation Using Imputation Algorithm
Armina, Roslan; Zain, Azlan Mohd; Azizah Ali, Nor; Sallehuddin, Roselina
2017-09-01
The presence of the missing value in the data set has always been a major problem for precise prediction. The method for imputing missing value needs to minimize the effect of incomplete data sets for the prediction model. Many algorithms have been proposed for countermeasure of missing value problem. In this review, we provide a comprehensive analysis of existing imputation algorithm, focusing on the technique used and the implementation of global or local information of data sets for missing value estimation. In addition validation method for imputation result and way to measure the performance of imputation algorithm also described. The objective of this review is to highlight possible improvement on existing method and it is hoped that this review gives reader better understanding of imputation method trend.
State-Estimation Algorithm Based on Computer Vision
Bayard, David; Brugarolas, Paul
2007-01-01
An algorithm and software to implement the algorithm are being developed as means to estimate the state (that is, the position and velocity) of an autonomous vehicle, relative to a visible nearby target object, to provide guidance for maneuvering the vehicle. In the original intended application, the autonomous vehicle would be a spacecraft and the nearby object would be a small astronomical body (typically, a comet or asteroid) to be explored by the spacecraft. The algorithm could also be used on Earth in analogous applications -- for example, for guiding underwater robots near such objects of interest as sunken ships, mineral deposits, or submerged mines. It is assumed that the robot would be equipped with a vision system that would include one or more electronic cameras, image-digitizing circuitry, and an imagedata- processing computer that would generate feature-recognition data products.
Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia
Park, Sang Seo; Kim, Jhoon; Lee, Jaehwa; Lee, Sukjo; Kim, Jeong Soo; Chang, Lim Seok; Ou, Steve
2014-01-01
A new dust detection algorithm is developed by combining the results of multiple dust detectionmethods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTDmethods have limitations in identifying the offset values of the BTDto discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10 × 10 pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia.
Multiuser detection and channel estimation: Exact and approximate methods
DEFF Research Database (Denmark)
Fabricius, Thomas
2003-01-01
. We also derive optimal detectors when nuisance parameters such as the channel and noise level are unknown, and show how well the proposed methods fit into this framework via the Generalised Expectation Maximisation algorithm. Our numerical evaluation show that naive mean field annealing and adaptive......This dissertation deals with optimal and close to optimal multiuser detection in Code Division Multiple Access. We derive optimal detection strategies in the sense of minimum expected probability of bit error, sequence error, and mean square error. These are implemented efficiently by the use...... analysis of the convexity and bifurcations of the naive mean field free energy and optima. This proves that we can avoid local minima by tracking a global convex solution into the non-convex region, effectively avoiding error propagation. This method is in statistical physics denoted mean field annealing...
Surface structure feature matching algorithm for cardiac motion estimation.
Zhang, Zhengrui; Yang, Xuan; Tan, Cong; Guo, Wei; Chen, Guoliang
2017-12-20
Cardiac diseases represent the leading cause of sudden death worldwide. During the development of cardiac diseases, the left ventricle (LV) changes obviously in structure and function. LV motion estimation plays an important role for diagnosis and treatment of cardiac diseases. To estimate LV motion accurately for cine magnetic resonance (MR) cardiac images, we develop an algorithm by combining point set matching with surface structure features of myocardium. The structure features of myocardial wall are described by estimating the normal directions of points locating on the myocardium contours using an approximation approach. The Gaussian mixture model (GMM) of structure features is used to represent LV structure feature distribution. A new cost function is defined to represent the differences between two Gaussian mixture models, which are the GMM of structure features and the GMM of positions of two point sets. To optimize the cost function, its gradient is derived to use the Quasi-Newton (QN). Furthermore, to resolve the dis-convergence issue of Quasi-Newton for high-dimensional parameter space, Stochastic Gradient Descent (SGD) is used and SGD gradient is derived. Finally, the new cost function is solved by optimization combining SGD with QN. With the closed form expression of gradient, this paper provided a computationally efficient registration algorithm. Three public datasets are employed to verify the performance of our algorithm, including cardiac MR image sequences acquired from 33 subjects, 14 inter-subject heart cases, and the data obtained in MICCAI 2009s 3D Segmentation Challenge for Clinical Applications. We compare our results with those of the other point set registration methods for LV motion estimation. The obtained results demonstrate that our algorithm shows inherent statistical robustness, due to the combination of SGD and Quasi-Newton optimization. Furthermore, our method is shown to outperform other point set matching methods in the
Parameter Estimation of Damped Compound Pendulum Differential Evolution Algorithm
Directory of Open Access Journals (Sweden)
Saad Mohd Sazli
2016-01-01
Full Text Available This paper present the parameter identification of damped compound pendulum using differential evolution algorithm. The procedure used to achieve the parameter identification of the experimental system consisted of input output data collection, ARX model order selection and parameter estimation using conventional method least square (LS and differential evolution (DE algorithm. PRBS signal is used to be input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the ARX model. The residual error between the actual and predicted output responses of the models is validated using mean squares error (MSE. Analysis showed that, MSE value for LS is 0.0026 and MSE value for DE is 3.6601×10-5. Based results obtained, it was found that DE have lower MSE than the LS method.
Windowed Least Square Algorithm Based PMSM Parameters Estimation
Directory of Open Access Journals (Sweden)
Song Wang
2013-01-01
Full Text Available Stator resistance and inductances in d-axis and q-axis of permanent magnet synchronous motors (PMSMs are important parameters. Acquiring these accurate parameters is usually the fundamental part in driving and controlling system design, to guarantee the performance of driver and controller. In this paper, we adopt a novel windowed least algorithm (WLS to estimate the parameters with fixed value or the parameter with time varying characteristic. The simulation results indicate that the WLS algorithm has a better performance in fixed parameters estimation and parameters with time varying characteristic identification than the recursive least square (RLS and extended Kalman filter (EKF. It is suitable for engineering realization in embedded system due to its rapidity, less system resource possession, less computation, and flexibility to adjust the window size according to the practical applications.
Parametric algorithms for frequency estimation in PWM converter systems
Energy Technology Data Exchange (ETDEWEB)
Lobos, T.; Hejke, I.; Rezmer, J.; Sikorski, T.; Kostyla, P. [Dept. of Electrical Engineering, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw (Poland)
2009-11-15
Proper estimation of voltage parameters has a significant meaning for power converters control systems. One of the measured parameter, directly associated with control process, is frequency of the basic voltage component at the converter output. Unfortunately, power converters generate a wide spectrum of harmonics as well as interharmonics, that makes difficulties in estimation this frequency. Measurement carried out in industrial applications exhibits complex nature of investigated signals often corrupted by resonances or overvoltages phenomena. It entails searching a new digital algorithms for frequency measurement. This work presents algorithm using modification of high-resolution Prony method connected with digital filtering. The investigations was based on simulations and real measured signals recorded in two different commercial PWM converters. Obtained results indicate accuracy of the method as well as its fast response. (author)
Masoumeh Soflaei; Paeiz Azmi
2014-01-01
One of the most important problems of reliable communications in shallow water channels is intersymbol interference (ISI) which is due to scattering from surface and reflecting from bottom. Using adaptive equalizers in receiver is one of the best suggested ways for overcoming this problem. In this paper, we apply the family of selective regressor affine projection algorithms (SR-APA) and the family of selective partial update APA (SPU-APA) which have low computational complexity that is one ...
Development of a Sidescan Imagery Automated Roughness Estimation Algorithm
2006-03-01
Development of a Sidescan Imagery Automated Roughness Estimation Algorithm Marlin L. Gendron, Maura C. Lohrenz and Geary Layne Naval Research...relative to nadir), which varies as a function of beam angle ( Fish and Carr, 1990). Likewise, the length of the surface of the object facing the SSS can...John Wiley & Sons. Fish , J. P. and Carr, H. A. Carr (1990). Sound Underwater Images: A Guide to the Generation and Interpretation of Sonar
Adaptive algorithm for mobile user positioning based on environment estimation
Directory of Open Access Journals (Sweden)
Grujović Darko
2014-01-01
Full Text Available This paper analyzes the challenges to realize an infrastructure independent and a low-cost positioning method in cellular networks based on RSS (Received Signal Strength parameter, auxiliary timing parameter and environment estimation. The proposed algorithm has been evaluated using field measurements collected from GSM (Global System for Mobile Communications network, but it is technology independent and can be applied in UMTS (Universal Mobile Telecommunication Systems and LTE (Long-Term Evolution networks, also.
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems.
Smith, J E
2012-01-01
Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes
Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network.
Lin, Kai; Wang, Di; Hu, Long
2016-07-01
With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC). The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S) evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods.
Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network
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Kai Lin
2016-07-01
Full Text Available With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC. The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods.
A Stereo Dual-Channel Dynamic Programming Algorithm for UAV Image Stitching.
Li, Ming; Chen, Ruizhi; Zhang, Weilong; Li, Deren; Liao, Xuan; Wang, Lei; Pan, Yuanjin; Zhang, Peng
2017-09-08
Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new algorithm for seamlessly stitching UAV images based on a dynamic programming approach. Our solution consists of two steps: Firstly, an image matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is developed based on the concept of a stereo dual-channel energy accumulation. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching, especially for the cases in dense urban areas. Our solution is also direction-independent, which has better adaptability and robustness for stitching images.
A Kalman-based Fundamental Frequency Estimation Algorithm
DEFF Research Database (Denmark)
Shi, Liming; Nielsen, Jesper Kjær; Jensen, Jesper Rindom
2017-01-01
Fundamental frequency estimation is an important task in speech and audio analysis. Harmonic model-based methods typically have superior estimation accuracy. However, such methods usually as- sume that the fundamental frequency and amplitudes are station- ary over a short time frame. In this paper......, we propose a Kalman filter-based fundamental frequency estimation algorithm using the harmonic model, where the fundamental frequency and amplitudes can be truly nonstationary by modeling their time variations as first- order Markov chains. The Kalman observation equation is derived from the harmonic...... model and formulated as a compact nonlinear matrix form, which is further used to derive an extended Kalman filter. Detailed and continuous fundamental frequency and ampli- tude estimates for speech, the sustained vowel /a/ and solo musical tones with vibrato are demonstrated....
A sea ice concentration estimation algorithm utilizing radiometer and SAR data
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J. Karvonen
2014-09-01
Full Text Available We have studied the possibility of combining the high-resolution synthetic aperture radar (SAR segmentation and ice concentration estimated by radiometer brightness temperatures. Here we present an algorithm for mapping a radiometer-based concentration value for each SAR segment. The concentrations are estimated by a multi-layer perceptron (MLP neural network which has the AMSR-2 (Advanced Microwave Scanning Radiometer 2 polarization ratios and gradient ratios of four radiometer channels as its inputs. The results have been compared numerically to the gridded Finnish Meteorological Institute (FMI ice chart concentrations and high-resolution AMSR-2 ASI (ARTIST Sea Ice algorithm concentrations provided by the University of Hamburg and also visually to the AMSR-2 bootstrap algorithm concentrations, which are given in much coarser resolution. The differences when compared to FMI daily ice charts were on average small. When compared to ASI ice concentrations, the differences were a bit larger, but still small on average. According to our comparisons, the largest differences typically occur near the ice edge and sea–land boundary. The main advantage of combining radiometer-based ice concentration estimation and SAR segmentation seems to be a more precise estimation of the boundaries of different ice concentration zones.
Fountain Code-Inspired Channel Estimation for Multi-user Millimeter Wave MIMO Systems
Kokshoorn, Matthew; Chen, He; Li, Yonghui; Vucetic, Branka
2016-01-01
This paper develops a novel channel estimation approach for multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent mmWave channel sparsity, we propose a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which the average number of channel measurements is adapted to various channel conditions. To this end, the base station (BS) and each user continue to measure the channel with a random subset of transmit/re...
EM algorithm for Bayesian estimation of genomic breeding values
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Iwata Hiroyoshi
2010-01-01
Full Text Available Abstract Background In genomic selection, a model for prediction of genome-wide breeding value (GBV is constructed by estimating a large number of SNP effects that are included in a model. Two Bayesian methods based on MCMC algorithm, Bayesian shrinkage regression (BSR method and stochastic search variable selection (SSVS method, (which are called BayesA and BayesB, respectively, in some literatures, have been so far proposed for the estimation of SNP effects. However, much computational burden is imposed on the MCMC-based Bayesian methods. A method with both high computing efficiency and prediction accuracy is desired to be developed for practical use of genomic selection. Results EM algorithm applicable for BSR is described. Subsequently, we propose a new EM-based Bayesian method, called wBSR (weighted BSR, which is a modification of BSR incorporating a weight for each SNP according to the strength of its association to a trait. Simulation experiments show that the computational time is much reduced with wBSR based on EM algorithm and the accuracy in predicting GBV is improved by wBSR in comparison with BSR based on MCMC algorithm. However, the accuracy of predicted GBV with wBSR is inferior to that with SSVS based on MCMC algorithm which is currently considered to be a method of choice for genomic selection. Conclusions EM-based wBSR method proposed in this study is much advantageous over MCMC-based Bayesian methods in computational time and can predict GBV more accurately than MCMC-based BSR. Therefore, wBSR is considered a practical method for genomic selection with a large number of SNP markers.
The PARAFAC-MUSIC Algorithm for DOA Estimation with Doppler Frequency in a MIMO Radar System
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Nan Wang
2014-01-01
Full Text Available The PARAFAC-MUSIC algorithm is proposed to estimate the direction-of-arrival (DOA of the targets with Doppler frequency in a monostatic MIMO radar system in this paper. To estimate the Doppler frequency, the PARAFAC (parallel factor algorithm is firstly utilized in the proposed algorithm, and after the compensation of Doppler frequency, MUSIC (multiple signal classification algorithm is applied to estimate the DOA. By these two steps, the DOA of moving targets can be estimated successfully. Simulation results show that the proposed PARAFAC-MUSIC algorithm has a higher accuracy than the PARAFAC algorithm and the MUSIC algorithm in DOA estimation.
AN EVOLUTIONARY ALGORITHM FOR CHANNEL ASSIGNMENT PROBLEM IN WIRELESS MOBILE NETWORKS
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Yee Shin Chia
2012-12-01
Full Text Available The channel assignment problem in wireless mobile network is the assignment of appropriate frequency spectrum to incoming calls while maintaining a satisfactory level of electromagnetic compatibility (EMC constraints. An effective channel assignment strategy is important due to the limited capacity of frequency spectrum in wireless mobile network. Most of the existing channel assignment strategies are based on deterministic methods. In this paper, an adaptive genetic algorithm (GA based channel assignment strategy is introduced for resource management and to reduce the effect of EMC interferences. The most significant advantage of the proposed optimization method is its capability to handle both the reassignment of channels for existing calls as well as the allocation of channel to a new incoming call in an adaptive process to maximize the utility of the limited resources. It is capable to adapt the population size to the number of eligible channels for a particular cell upon new call arrivals to achieve reasonable convergence speed. The MATLAB simulation on a 49-cells network model for both uniform and nonuniform call traffic distributions showed that the proposed channel optimization method can always achieve a lower average new incoming call blocking probability compared to the deterministic based channel assignment strategy.
Françoise Benz
2004-01-01
ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on natural annealing processes or Evolutionary Computation, based on biological evolution processes. Geneti...
Françoise Benz
2004-01-01
ENSEIGNEMENT ACADEMIQUE ACADEMIC TRAINING Françoise Benz 73127 academic.training@cern.ch ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on nat...
Space-Time Coded MC-CDMA: Blind Channel Estimation, Identifiability, and Receiver Design
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Li Hongbin
2002-01-01
Full Text Available Integrating the strengths of multicarrier (MC modulation and code division multiple access (CDMA, MC-CDMA systems are of great interest for future broadband transmissions. This paper considers the problem of channel identification and signal combining/detection schemes for MC-CDMA systems equipped with multiple transmit antennas and space-time (ST coding. In particular, a subspace based blind channel identification algorithm is presented. Identifiability conditions are examined and specified which guarantee unique and perfect (up to a scalar channel estimation when knowledge of the noise subspace is available. Several popular single-user based signal combining schemes, namely the maximum ratio combining (MRC and the equal gain combining (EGC, which are often utilized in conventional single-transmit-antenna based MC-CDMA systems, are extended to the current ST-coded MC-CDMA (STC-MC-CDMA system to perform joint combining and decoding. In addition, a linear multiuser minimum mean-squared error (MMSE detection scheme is also presented, which is shown to outperform the MRC and EGC at some increased computational complexity. Numerical examples are presented to evaluate and compare the proposed channel identification and signal detection/combining techniques.
Accelerating the HyperLogLog Cardinality Estimation Algorithm
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Cem Bozkus
2017-01-01
Full Text Available In recent years, vast amounts of data of different kinds, from pictures and videos from our cameras to software logs from sensor networks and Internet routers operating day and night, are being generated. This has led to new big data problems, which require new algorithms to handle these large volumes of data and as a result are very computationally demanding because of the volumes to process. In this paper, we parallelize one of these new algorithms, namely, the HyperLogLog algorithm, which estimates the number of different items in a large data set with minimal memory usage, as it lowers the typical memory usage of this type of calculation from O(n to O(1. We have implemented parallelizations based on OpenMP and OpenCL and evaluated them in a standard multicore system, an Intel Xeon Phi, and two GPUs from different vendors. The results obtained in our experiments, in which we reach a speedup of 88.6 with respect to an optimized sequential implementation, are very positive, particularly taking into account the need to run this kind of algorithm on large amounts of data.
Sensitivity Analysis of Aerosol Retrieval Algorithms Using Two-Channel Satellite Radiance Data
Geogdzhayev, Igor V.; Mishchenko, Michael I.
1999-01-01
We discuss the methodology of interpreting channel 1 and 2 AVHRR radiance data to retrieve tropospheric aerosol properties over the ocean and describe a detailed analysis of the sensitivity of monthly average retrievals to the assumed aerosol models. We use real AVHRR data and accurate numerical techniques for computing single and multiple scattering and spectral absorption of light in the vertically inhomogeneous atmosphere-ocean system. Our analysis shows that two-channel algorithms can provide significantly more accurate retrievals of the aerosol optical thickness than one-channel algorithms and that imperfect cloud screening is the largest source of errors in the retrieved optical thickness. Both underestimating and overestimating aerosol absorption as well as strong variability of the aerosol refractive index may lead to regional and/or seasonal biases in optical thickness retrievals. The Angstrom exponent appears to be the most invariant aerosol size characteristic and should be retrieved along with optical thickness as the second aerosol parameter.
Soft-input iterative channel estimation for bit-interleaved turbo-coded MIMO-OFDM systems
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Olutayo O. Oyerinde
2013-01-01
Full Text Available Bit-interleaved coded modulation (BICM is a robust multiplexing technique for achieving multiplexing gain in multiple-input multiple-output (MIMO-orthogonal frequency division multiplexing (OFDM systems. However, in order to benefit maximally from the various advantages offered by BICM-based MIMO-OFDM systems, availability of accurate MIMO channel state information (CSI at the receiver end of the system is essential. Without accurate MIMO CSI, accurate MIMO demapping and coherent detection and decoding of the transmitted message symbols at the system�s receiver would be impossible. In such cases, the multiplexing gain offered by the BICM technique, as well as the higher data rate made possible by the MIMO-OFDM system, is not benefitted from in full. In this paper, we propose a soft input based decision-directed channel estimation scheme for the provision of MIMO CSI for coherent detection of signals in MIMO-OFDM systems. The proposed channel estimator works in iterative mode with a MIMO demapper and a turbo decoder, and is based on the fast data projection method (FDPM and the variable step size normalised least mean square (VSSNLMS algorithm. Simulation results of the proposed estimator based on the FDPM and VSSNLMS algorithms indicate better performance in comparison with the same estimator employing minimum mean square error criteria and deflated projection approximation subspace tracking algorithms for both slow- and fast-fading channel scenarios. The proposed estimator would be suitable for use at the receiver end of MIMO-OFDM wireless communication systems operating in either slow- or fast-fading channels.
Jitter Estimation Algorithms for Detection of Pathological Voices
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Dárcio G. Silva
2009-01-01
Full Text Available This work is focused on the evaluation of different methods to estimate the amount of jitter present in speech signals. The jitter value is a measure of the irregularity of a quasiperiodic signal and is a good indicator of the presence of pathologies in the larynx such as vocal fold nodules or a vocal fold polyp. Given the irregular nature of the speech signal, each jitter estimation algorithm relies on its own model making a direct comparison of the results very difficult. For this reason, the evaluation of the different jitter estimation methods was target on their ability to detect pathological voices. Two databases were used for this evaluation: a subset of the MEEI database and a smaller database acquired in the scope of this work. The results showed that there were significant differences in the performance of the algorithms being evaluated. Surprisingly, in the largest database the best results were not achieved with the commonly used relative jitter, measured as a percentage of the glottal cycle, but with absolute jitter values measured in microseconds. Also, the new proposed measure for jitter, LocJitt, performed in general is equal to or better than the commonly used tools of MDVP and Praat.
Eigenvalue estimation of differential operators with a quantum algorithm
Szkopek, Thomas; Roychowdhury, Vwani; Yablonovitch, Eli; Abrams, Daniel S.
2005-12-01
We demonstrate how linear differential operators could be emulated by a quantum processor, should one ever be built, using the Abrams-Lloyd algorithm. Given a linear differential operator of order 2S , acting on functions ψ(x1,x2,…,xD) with D arguments, the computational cost required to estimate a low order eigenvalue to accuracy Θ(1/N2) is Θ((2(S+1)(1+1/ν)+D)lnN) qubits and O(N2(S+1)(1+1/ν)lncND) gate operations, where N is the number of points to which each argument is discretized, ν and c are implementation dependent constants of O(1) . Optimal classical methods require Θ(ND) bits and Ω(ND) gate operations to perform the same eigenvalue estimation. The Abrams-Lloyd algorithm thereby leads to exponential reduction in memory and polynomial reduction in gate operations, provided the domain has sufficiently large dimension D>2(S+1)(1+1/ν) . In the case of Schrödinger’s equation, ground state energy estimation of two or more particles can in principle be performed with fewer quantum mechanical gates than classical gates.
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Masoumeh Soflaei
2014-01-01
Full Text Available One of the most important problems of reliable communications in shallow water channels is intersymbol interference (ISI which is due to scattering from surface and reflecting from bottom. Using adaptive equalizers in receiver is one of the best suggested ways for overcoming this problem. In this paper, we apply the family of selective regressor affine projection algorithms (SR-APA and the family of selective partial update APA (SPU-APA which have low computational complexity that is one of the important factors that influences adaptive equalizer performance. We apply experimental data from Strait of Hormuz for examining the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE of SR-APA and SPU-APA decrease by 5.8 (dB and 5.5 (dB, respectively, in comparison with least mean square (LMS algorithm. Also the families of SPU-APA and SR-APA have better convergence speed than LMS type algorithm.
Channel Estimation and Optimal Power Allocation for a Multiple-Antenna OFDM System
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Yao Kung
2002-01-01
Full Text Available We propose combining channel estimation and optimal power allocation approaches for a multiple-antenna orthogonal frequency division multiplexing (OFDM system in high-speed transmission applications. We develop a least-square channel estimation approach, derive the performance bound of the estimator, and investigate the optimal training sequences for initial channel acquisition. Based on the channel estimates, the optimal power allocation solution which maximizes the bandwidth efficiency is derived under power and quality of service (Qos (symbol error rate constraints. It is shown that combining channel tracking and adaptive power allocation can dramatically enhance the outage capacity of an OFDM multiple-antenna system when severing fading occurs.
Directory of Open Access Journals (Sweden)
Xia Liu
2010-01-01
Full Text Available This paper reports investigations on the effect of antenna mutual coupling on performance of training-based Multiple-Input Multiple-Output (MIMO channel estimation. The influence of mutual coupling is assessed for two training-based channel estimation methods, Scaled Least Square (SLS and Minimum Mean Square Error (MMSE. It is shown that the accuracy of MIMO channel estimation is governed by the sum of eigenvalues of channel correlation matrix which in turn is influenced by the mutual coupling in transmitting and receiving array antennas. A water-filling-based procedure is proposed to optimize the training signal transmission to minimize the MIMO channel estimation errors.
Estimating Traffic Accidents in Turkey Using Differential Evolution Algorithm
Akgüngör, Ali Payıdar; Korkmaz, Ersin
2017-06-01
Estimating traffic accidents play a vital role to apply road safety procedures. This study proposes Differential Evolution Algorithm (DEA) models to estimate the number of accidents in Turkey. In the model development, population (P) and the number of vehicles (N) are selected as model parameters. Three model forms, linear, exponential and semi-quadratic models, are developed using DEA with the data covering from 2000 to 2014. Developed models are statistically compared to select the best fit model. The results of the DE models show that the linear model form is suitable to estimate the number of accidents. The statistics of this form is better than other forms in terms of performance criteria which are the Mean Absolute Percentage Errors (MAPE) and the Root Mean Square Errors (RMSE). To investigate the performance of linear DE model for future estimations, a ten-year period from 2015 to 2024 is considered. The results obtained from future estimations reveal the suitability of DE method for road safety applications.
PAPR Reduction Approach Based on Channel Estimation Pilots for Next Generations Broadcasting Systems
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Anh-Tai Ho
2011-01-01
Full Text Available A novel peak-to-average power ratio (PAPR reduction technique for orthogonal frequency division multiplexing (OFDM systems is addressed. Instead of using dedicated pilots for PAPR reduction as with tone reservation (TR method selected by the DVB-T2 standard, we propose to use existing pilots used for channel estimation. In this way, we avoid the use of reserved tone pilots and then improve the spectral efficiency of the system. In order to allow their recovery at the receiver, these pilots have to follow particular laws which permit their blind detection and avoid sending side information. In this work, we propose and investigate a multiplicative law operating in discrete frequency domain. The operation in discrete domain aims at reducing degradation due to detection and estimation error in continuous domain. Simulation results are performed using the new DVB-T2 standard parameters. Its performance is compared to the DVB-T2 PAPR gradient algorithm and to the second-order cone programming (SOCP competitive technique proposed in the literature. We show that the proposed technique is efficient in terms of PAPR reduction value and of spectral efficiency while the channel estimation performance is maintained.
Li, Yuanqing; Cichocki, Andrzej; Amari, Shun-Ichi
2006-03-01
In this paper, we use a two-stage sparse factorization approach for blindly estimating the channel parameters and then estimating source components for electroencephalogram (EEG) signals. EEG signals are assumed to be linear mixtures of source components, artifacts, etc. Therefore, a raw EEG data matrix can be factored into the product of two matrices, one of which represents the mixing matrix and the other the source component matrix. Furthermore, the components are sparse in the time-frequency domain, i.e., the factorization is a sparse factorization in the time frequency domain. It is a challenging task to estimate the mixing matrix. Our extensive analysis and computational results, which were based on many sets of EEG data, not only provide firm evidences supporting the above assumption, but also prompt us to propose a new algorithm for estimating the mixing matrix. After the mixing matrix is estimated, the source components are estimated in the time frequency domain using a linear programming method. In an example of the potential applications of our approach, we analyzed the EEG data that was obtained from a modified Sternberg memory experiment. Two almost uncorrelated components obtained by applying the sparse factorization method were selected for phase synchronization analysis. Several interesting findings were obtained, especially that memory-related synchronization and desynchronization appear in the alpha band, and that the strength of alpha band synchronization is related to memory performance.
National Aeronautics and Space Administration — Recent work has developed a number of architectures and algorithms for accurately estimating spacecraft and formation states. The estimation accuracy achievable...
Application of Genetic Algorithms for Parameter Estimation in Liquid Chromatography
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Orestes Llanes Santiago
2011-11-01
Full Text Available Normal 0 21 false false false ES X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} In chromatography, complex inverse problems related to the parameters estimation and process optimization are presented. Metaheuristics methods are known as general purpose approximated algorithms which seek and hopefully find good solutions at a reasonable computational cost. These methods are iterative process to perform a robust search of a solution space. Genetic algorithms are optimization techniques based on the principles of genetics and natural selection. They have demonstrated very good performance as global optimizers in many types of applications, including inverse problems. In this work, the effectiveness of genetic algorithms is investigated to estimate parameters in liquid chromatography.
An algorithm for estimating aerosol optical depth from HIMAWARI-8 data over Ocean
Lee, Kwon Ho
2016-04-01
The paper presents currently developing algorithm for aerosol detection and retrieval over ocean for the next generation geostationary satellite, HIMAWARI-8. Enhanced geostationary remote sensing observations are now enables for aerosol retrieval of dust, smoke, and ash, which began a new era of geostationary aerosol observations. Sixteen channels of the Advanced HIMAWARI Imager (AHI) onboard HIMAWARI-8 offer capabilities for aerosol remote sensing similar to those currently provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). Aerosols were estimated in detection processing from visible and infrared channel radiances, and in retrieval processing using the inversion-optimization of satellite-observed radiances with those calculated from radiative transfer model. The retrievals are performed operationally every ten minutes for pixel sizes of ~8 km. The algorithm currently under development uses a multichannel approach to estimate the effective radius, aerosol optical depth (AOD) simultaneously. The instantaneous retrieved AOD is evaluated by the MODIS level 2 operational aerosol products (C006), and the daily retrieved AOD was compared with ground-based measurements from the AERONET databases. The results show that the detection of aerosol and estimated AOD are in good agreement with the MODIS data and ground measurements with a correlation coefficient of ˜0.90 and a bias of 4%. These results suggest that the proposed method applied to the HIMAWARI-8 satellite data can accurately estimate continuous AOD. Acknowledgments This work was supported by "Development of Geostationary Meteorological Satellite Ground Segment(NMSC-2014-01)" program funded by National Meteorological Satellite Centre(NMSC) of Korea Meteorological Administration(KMA).
Estimation of MIMO channel capacity from phase-noise impaired measurements
DEFF Research Database (Denmark)
Pedersen, Troels; Yin, Xuefeng; Fleury, Bernard Henri
2008-01-01
that phase noise of the transmitter and receiver local oscillators, when it is assumed to be a white Gaussian random process, can cause large errors of the estimated channel capacity of a low-rank MIMO channel when the standard channel matrix estimator is used. Experimental evidence shows that consecutive...... phase noise samples affecting measurement samples collected with real TDMMIMO channel sounders are correlated. In this contribution a capacity estimator that accounts for the phase noise correlation is proposed. The estimator is based on a linear minimum mean square error estimate of the MIMO channel...... matrix. It is shown by means of Monte Carlo simulations assuming a measurementbased phase noise model, that the MIMO channel capacity can be estimated accurately for signal to noise ratios up to about 35 dB...
A Study on Channel Estimation Methods for Time-Domain Spreading MC-CDMA Systems
Nagate, Atsushi; Fujii, Teruya
As a candidate for the transmission technology of next generation mobile communication systems, time-domain spreading MC-CDMA systems have begun to attract much attention. In these systems, data and pilot symbols are spread in the time domain and code-multiplexed. To combat fading issues, we need to conduct channel estimation by using the code-multiplexed pilot symbols. Especially in next generation systems, frequency bands higher than those of current systems, which raise the maximum Doppler frequency, are expected to be used, so that a more powerful channel estimation method is expected. Considering this, we propose a channel estimation method for highly accurate channel estimation; it is a combination of a two-dimensional channel estimation method and an impulse response-based channel estimation method. We evaluate the proposed method by computer simulations.
Performance Analysis of Iterative Decoding Algorithms for PEG LDPC Codes in Nakagami Fading Channels
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O. Al Rasheed
2013-11-01
Full Text Available In this paper we give a comparative analysis of decoding algorithms of Low Density Parity Check (LDPC codes in a channel with the Nakagami distribution of the fading envelope. We consider the Progressive Edge-Growth (PEG method and Improved PEG method for the parity check matrix construction, which can be used to avoid short girths, small trapping sets and a high level of error floor. A comparative analysis of several classes of LDPC codes in various propagation conditions and decoded using different decoding algorithms is also presented.
Dowler, ASH; Doufexi, A; Nix, AR
2002-01-01
In this paper channel estimation techniques for a mobile fourth generation coherent orthogonal frequency division multiplexing (OFDM) system are proposed. Coherent detection dictates that a per-subband estimate of the frequency response of the channel is generated for each OFDM symbol. This is achieved by inserting pilot symbols amongst the data symbols in the OFDM modulation grid. With suitable interpolation, the channel estimate at all intermediate symbols can be generated. A number of chan...
Software algorithm and hardware design for real-time implementation of new spectral estimator.
Ciaccio, Edward J; Biviano, Angelo B; Garan, Hasan
2014-05-09
Real-time spectral analyzers can be difficult to implement for PC computer-based systems because of the potential for high computational cost, and algorithm complexity. In this work a new spectral estimator (NSE) is developed for real-time analysis, and compared with the discrete Fourier transform (DFT). Clinical data in the form of 216 fractionated atrial electrogram sequences were used as inputs. The sample rate for acquisition was 977 Hz, or approximately 1 millisecond between digital samples. Real-time NSE power spectra were generated for 16,384 consecutive data points. The same data sequences were used for spectral calculation using a radix-2 implementation of the DFT. The NSE algorithm was also developed for implementation as a real-time spectral analyzer electronic circuit board. The average interval for a single real-time spectral calculation in software was 3.29 μs for NSE versus 504.5 μs for DFT. Thus for real-time spectral analysis, the NSE algorithm is approximately 150× faster than the DFT. Over a 1 millisecond sampling period, the NSE algorithm had the capability to spectrally analyze a maximum of 303 data channels, while the DFT algorithm could only analyze a single channel. Moreover, for the 8 second sequences, the NSE spectral resolution in the 3-12 Hz range was 0.037 Hz while the DFT spectral resolution was only 0.122 Hz. The NSE was also found to be implementable as a standalone spectral analyzer board using approximately 26 integrated circuits at a cost of approximately $500. The software files used for analysis are included as a supplement, please see the Additional files 1 and 2. The NSE real-time algorithm has low computational cost and complexity, and is implementable in both software and hardware for 1 millisecond updates of multichannel spectra. The algorithm may be helpful to guide radiofrequency catheter ablation in real time.
Directory of Open Access Journals (Sweden)
Meher Krishna Patel
2017-01-01
Full Text Available This paper presents an adaptive multiuser transceiver scheme for DS-CDMA systems in which pilot symbols are added to users’ data to estimate complex channel fading coefficients. The performance of receiver antenna diversity with maximal ratio combining (MRC technique is analyzed for imperfect channel estimation in flat fading environments. The complex fading coefficients are estimated using least mean square (LMS algorithm and these coefficients are utilized by the maximal ratio combiner for generating the decision variable. Probability of error in closed form is derived. Further, the effect of pilot signal power on bit error rate (BER and BER performance of multiplexed pilot and data signal transmission scenario are investigated. We have compared the performance of added and multiplexed pilot-data systems and concluded the advantages of both systems. The proposed CDMA technique uses the chaotic sequence as spreading sequence. Assuming proper synchronization, the computer simulation results demonstrate the better bit error rate performance in the presence of channel estimator in the chaotic based CDMA system and the receiver antenna diversity technique further improves the performance of the proposed system. Also, no channel estimator is required if there is no phase distortion to the transmitted signal.
Algorithm for wind speed estimate with polarimetric radar
Directory of Open Access Journals (Sweden)
Ю. А. Авер’янова
2013-07-01
Full Text Available The connection of wind speed and drops behavior is substantiated as well as the drop behavior influence onto the polarization characteristics of electromagnetic waves. The expression to calculate the wind speed taking into account the Weber number for the critical regime of drop deformation is obtained. The critical regime of drop deformation is the regime when drop is divided into two parts. The dependency of critical wind speed on the drop diameter is calculated and shown. The concept o polarization spectrum that is introduced in the previous papers is used to estimate the dynamic processes in the atmosphere. At the moment when the drop is under the influence of the wind that is equal to the critical wind speed the drop will be divided into two parts. This process will be reflected as the appearance of the two equal components of polarization spectra of reflected electromagnetic waves at the orthogonal antennas of Doppler Polarimetric Radar. Owing the information about the correspondence of the polarization component energy level to the drop diameter it is possible to estimate the wind speed with the obtained dependency. The process of the wind speed estimate with polarimetric radar is presented with the developed common algorithm
Channel estimation for space-time trellis coded-OFDM systems based on nonoverlapping pilot structure
CSIR Research Space (South Africa)
Sokoya, O
2008-09-01
Full Text Available estimation [7], which is based on the pilot data sent at the transmitter and known a-priori at the receiver. The other is called blind channel estimation [8], which explores the statistical information of the channel and certain properties... of the transmitted signals. Though the blind estimation method has no overhead loss, it is only applicable to slowly time varying channel due to its need for long data record and has high complexity. Pilot symbols assisted channel estimation has been shown...
Algorithm for correcting the keratometric estimation error in normal eyes..
Camps, Vicente J; Pinero Llorens, David P; de Fez, Dolores; Coloma, Pilar; Caballero, Maria Teresa; Garcia, Celia; Miret, Juan J
2012-02-01
To obtain an accurate algorithm for calculating the keratometric index that minimizes the errors in the calculation of corneal power assuming only a single corneal surface in the range of corneal curvatures of the normal population. Corneal power was calculated by using the classical keratometric index and also by using the Gaussian equation. Differences between types of calculation of corneal power were determined and modeled by regression analysis. We proposed two options for the selection of the most appropriate keratometric index (n(k)) value for each specific case. First was the use of specific linear equations (depending on the ratio of the anterior to the posterior curvature, k ratio) according to the value of the central radius of curvature of the anterior corneal surface (r(1c)) in 0.1 mm steps and the theoretical eye model considered. The second was the use of a general simplified equation only requiring r(1c) (Gullstrand eye model, n(k) = -0.0064286r(1c) + 1.37688; Le Grand eye model, n(k) = -0.0063804r(1c) + 1.37806). The generalization of the keratometric index (n(k)) value is not an appropriate approximation for the estimation of the corneal power and it can lead to significant errors. We proposed a new algorithm depending on r(1c), with a maximal associated error in the calculation of the corneal power of 0.5 D and without requiring knowledge of the posterior corneal curvature.
Fast Parabola Detection Using Estimation of Distribution Algorithms
Sierra-Hernandez, Juan Manuel; Avila-Garcia, Maria Susana; Rojas-Laguna, Roberto
2017-01-01
This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications. PMID:28321264
Motion estimation for video coding efficient algorithms and architectures
Chakrabarti, Indrajit; Chatterjee, Sumit Kumar
2015-01-01
The need of video compression in the modern age of visual communication cannot be over-emphasized. This monograph will provide useful information to the postgraduate students and researchers who wish to work in the domain of VLSI design for video processing applications. In this book, one can find an in-depth discussion of several motion estimation algorithms and their VLSI implementation as conceived and developed by the authors. It records an account of research done involving fast three step search, successive elimination, one-bit transformation and its effective combination with diamond search and dynamic pixel truncation techniques. Two appendices provide a number of instances of proof of concept through Matlab and Verilog program segments. In this aspect, the book can be considered as first of its kind. The architectures have been developed with an eye to their applicability in everyday low-power handheld appliances including video camcorders and smartphones.
Fast Parabola Detection Using Estimation of Distribution Algorithms
Directory of Open Access Journals (Sweden)
Jose de Jesus Guerrero-Turrubiates
2017-01-01
Full Text Available This paper presents a new method based on Estimation of Distribution Algorithms (EDAs to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.
Reduced-Dimension Noncircular-Capon Algorithm for DOA Estimation of Noncircular Signals
Directory of Open Access Journals (Sweden)
Weihua Lv
2015-01-01
Full Text Available The problem of the direction of arrival (DOA estimation for the noncircular (NC signals, which have been widely used in communications, is investigated. A reduced-dimension NC-Capon algorithm is proposed hereby for the DOA estimation of noncircular signals. The proposed algorithm, which only requires one-dimensional search, can avoid the high computational cost within the two-dimensional NC-Capon algorithm. The angle estimation performance of the proposed algorithm is much better than that of the conventional Capon algorithm and very close to that of the two-dimensional NC-Capon algorithm, which has a much higher complexity than the proposed algorithm. Furthermore, the proposed algorithm can be applied to arbitrary arrays and works well without estimating the noncircular phases. The simulation results verify the effectiveness and improvement of the proposed algorithm.
A Two-Channel Training Algorithm for Hidden Markov Model and Its Application to Lip Reading
Directory of Open Access Journals (Sweden)
Foo Say Wei
2005-01-01
Full Text Available Hidden Markov model (HMM has been a popular mathematical approach for sequence classification such as speech recognition since 1980s. In this paper, a novel two-channel training strategy is proposed for discriminative training of HMM. For the proposed training strategy, a novel separable-distance function that measures the difference between a pair of training samples is adopted as the criterion function. The symbol emission matrix of an HMM is split into two channels: a static channel to maintain the validity of the HMM and a dynamic channel that is modified to maximize the separable distance. The parameters of the two-channel HMM are estimated by iterative application of expectation-maximization (EM operations. As an example of the application of the novel approach, a hierarchical speaker-dependent visual speech recognition system is trained using the two-channel HMMs. Results of experiments on identifying a group of confusable visemes indicate that the proposed approach is able to increase the recognition accuracy by an average of 20% compared with the conventional HMMs that are trained with the Baum-Welch estimation.
A Two-Channel Training Algorithm for Hidden Markov Model and Its Application to Lip Reading
Dong, Liang; Foo, Say Wei; Lian, Yong
2005-12-01
Hidden Markov model (HMM) has been a popular mathematical approach for sequence classification such as speech recognition since 1980s. In this paper, a novel two-channel training strategy is proposed for discriminative training of HMM. For the proposed training strategy, a novel separable-distance function that measures the difference between a pair of training samples is adopted as the criterion function. The symbol emission matrix of an HMM is split into two channels: a static channel to maintain the validity of the HMM and a dynamic channel that is modified to maximize the separable distance. The parameters of the two-channel HMM are estimated by iterative application of expectation-maximization (EM) operations. As an example of the application of the novel approach, a hierarchical speaker-dependent visual speech recognition system is trained using the two-channel HMMs. Results of experiments on identifying a group of confusable visemes indicate that the proposed approach is able to increase the recognition accuracy by an average of 20% compared with the conventional HMMs that are trained with the Baum-Welch estimation.
A Survey of Wireless Fair Queuing Algorithms with Location-Dependent Channel Errors
Directory of Open Access Journals (Sweden)
Anca VARGATU
2011-01-01
Full Text Available The rapid development of wireless networks has brought more and more attention to topics related to fair allocation of resources, creation of suitable algorithms, taking into account the special characteristics of wireless environment and insurance fair access to the transmission channel, with delay bound and throughput guaranteed. Fair allocation of resources in wireless networks requires significant challenges, because of errors that occur only in these networks, such as location-dependent and bursty channel errors. In wireless networks, frequently happens, because interference of radio waves, that a user experiencing bad radio conditions during a period of time, not to receive resources in that period. This paper analyzes some resource allocation algorithms for wireless networks with location dependent errors, specifying the base idea for each algorithm and the way how it works. The analyzed fair queuing algorithms differ by the way they treat the following aspects: how to select the flows which should receive additional services, how to allocate these resources, which is the proportion received by error free flows and how the flows affected by errors are compensated.
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.
The finite body triangulation: algorithms, subgraphs, homogeneity estimation and application.
Carson, Cantwell G; Levine, Jonathan S
2016-09-01
The concept of a finite body Dirichlet tessellation has been extended to that of a finite body Delaunay 'triangulation' to provide a more meaningful description of the spatial distribution of nonspherical secondary phase bodies in 2- and 3-dimensional images. A finite body triangulation (FBT) consists of a network of minimum edge-to-edge distances between adjacent objects in a microstructure. From this is also obtained the characteristic object chords formed by the intersection of the object boundary with the finite body tessellation. These two sets of distances form the basis of a parsimonious homogeneity estimation. The characteristics of the spatial distribution are then evaluated with respect to the distances between objects and the distances within them. Quantitative analysis shows that more physically representative distributions can be obtained by selecting subgraphs, such as the relative neighbourhood graph and the minimum spanning tree, from the finite body tessellation. To demonstrate their potential, we apply these methods to 3-dimensional X-ray computed tomographic images of foamed cement and their 2-dimensional cross sections. The Python computer code used to estimate the FBT is made available. Other applications for the algorithm - such as porous media transport and crack-tip propagation - are also discussed. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.
Genetic Algorithm-based Affine Parameter Estimation for Shape Recognition
Directory of Open Access Journals (Sweden)
Yuxing Mao
2014-06-01
Full Text Available Shape recognition is a classically difficult problem because of the affine transformation between two shapes. The current study proposes an affine parameter estimation method for shape recognition based on a genetic algorithm (GA. The contributions of this study are focused on the extraction of affine-invariant features, the individual encoding scheme, and the fitness function construction policy for a GA. First, the affine-invariant characteristics of the centroid distance ratios (CDRs of any two opposite contour points to the barycentre are analysed. Using different intervals along the azimuth angle, the different numbers of CDRs of two candidate shapes are computed as representations of the shapes, respectively. Then, the CDRs are selected based on predesigned affine parameters to construct the fitness function. After that, a GA is used to search for the affine parameters with optimal matching between candidate shapes, which serve as actual descriptions of the affine transformation between the shapes. Finally, the CDRs are resampled based on the estimated parameters to evaluate the similarity of the shapes for classification. The experimental results demonstrate the robust performance of the proposed method in shape recognition with translation, scaling, rotation and distortion.
Martin-Fernandez, Manuel; Revuelta, Javier
2017-01-01
This study compares the performance of two estimation algorithms of new usage, the Metropolis-Hastings Robins-Monro (MHRM) and the Hamiltonian MCMC (HMC), with two consolidated algorithms in the psychometric literature, the marginal likelihood via EM algorithm (MML-EM) and the Markov chain Monte Carlo (MCMC), in the estimation of multidimensional…
Discharge estimation in compound channels with fixed and mobile ...
Indian Academy of Sciences (India)
For the smooth main channel and smooth floodplain experiments, both nmc and nfp are equal to 0·0091 measured at bankfull level (h = 0·05 m) as given by Atabay (2001), Atabay &. Knight (2006). The mobile main channel Manning's n value was taken as 0·015 by Seckin (2004), its measured mean value from inbank flow ...
Multi channel thermal hydraulic analysis of gas cooled fast reactor using genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Drajat, R. Z.; Su' ud, Z.; Soewono, E.; Gunawan, A. Y. [Department of Mathematics, Institut Teknologi Bandung, Bandung 40132 (Indonesia); Department of Physics, Institut Teknologi Bandung, Bandung 40132 (Indonesia); Department of Mathematics, Institut Teknologi Bandung, Bandung 40132 (Indonesia)
2012-05-22
There are three analyzes to be done in the design process of nuclear reactor i.e. neutronic analysis, thermal hydraulic analysis and thermodynamic analysis. The focus in this article is the thermal hydraulic analysis, which has a very important role in terms of system efficiency and the selection of the optimal design. This analysis is performed in a type of Gas Cooled Fast Reactor (GFR) using cooling Helium (He). The heat from nuclear fission reactions in nuclear reactors will be distributed through the process of conduction in fuel elements. Furthermore, the heat is delivered through a process of heat convection in the fluid flow in cooling channel. Temperature changes that occur in the coolant channels cause a decrease in pressure at the top of the reactor core. The governing equations in each channel consist of mass balance, momentum balance, energy balance, mass conservation and ideal gas equation. The problem is reduced to finding flow rates in each channel such that the pressure drops at the top of the reactor core are all equal. The problem is solved numerically with the genetic algorithm method. Flow rates and temperature distribution in each channel are obtained here.
Low-sampling-rate ultra-wideband channel estimation using equivalent-time sampling
Ballal, Tarig
2014-09-01
In this paper, a low-sampling-rate scheme for ultra-wideband channel estimation is proposed. The scheme exploits multiple observations generated by transmitting multiple pulses. In the proposed scheme, P pulses are transmitted to produce channel impulse response estimates at a desired sampling rate, while the ADC samples at a rate that is P times slower. To avoid loss of fidelity, the number of sampling periods (based on the desired rate) in the inter-pulse interval is restricted to be co-prime with P. This condition is affected when clock drift is present and the transmitted pulse locations change. To handle this case, and to achieve an overall good channel estimation performance, without using prior information, we derive an improved estimator based on the bounded data uncertainty (BDU) model. It is shown that this estimator is related to the Bayesian linear minimum mean squared error (LMMSE) estimator. Channel estimation performance of the proposed sub-sampling scheme combined with the new estimator is assessed in simulation. The results show that high reduction in sampling rate can be achieved. The proposed estimator outperforms the least squares estimator in almost all cases, while in the high SNR regime it also outperforms the LMMSE estimator. In addition to channel estimation, a synchronization method is also proposed that utilizes the same pulse sequence used for channel estimation. © 2014 IEEE.
The generation of shared cryptographic keys through channel impulse response estimation at 60 GHz.
Energy Technology Data Exchange (ETDEWEB)
Young, Derek P.; Forman, Michael A.; Dowdle, Donald Ryan
2010-09-01
Methods to generate private keys based on wireless channel characteristics have been proposed as an alternative to standard key-management schemes. In this work, we discuss past work in the field and offer a generalized scheme for the generation of private keys using uncorrelated channels in multiple domains. Proposed cognitive enhancements measure channel characteristics, to dynamically change transmission and reception parameters as well as estimate private key randomness and expiration times. Finally, results are presented on the implementation of a system for the generation of private keys for cryptographic communications using channel impulse-response estimation at 60 GHz. The testbed is composed of commercial millimeter-wave VubIQ transceivers, laboratory equipment, and software implemented in MATLAB. Novel cognitive enhancements are demonstrated, using channel estimation to dynamically change system parameters and estimate cryptographic key strength. We show for a complex channel that secret key generation can be accomplished on the order of 100 kb/s.
Channel Estimation for Filter Bank Multicarrier Systems in Low SNR Environments
Energy Technology Data Exchange (ETDEWEB)
Driggs, Jonathan; Sibbett, Taylor; Moradiy, Hussein; Farhang-Boroujeny, Behrouz
2017-05-01
Channel estimation techniques are crucial for reliable communications. This paper is concerned with channel estimation in a filter bank multicarrier spread spectrum (FBMCSS) system. We explore two channel estimator options: (i) a method that makes use of a periodic preamble and mimics the channel estimation techniques that are widely used in OFDM-based systems; and (ii) a method that stays within the traditional realm of filter bank signal processing. For the case where the channel noise is white, both methods are analyzed in detail and their performance is compared against their respective Cramer-Rao Lower Bounds (CRLB). Advantages and disadvantages of the two methods under different channel conditions are given to provide insight to the reader as to when one will outperform the other.
Pilot power optimization for AF relaying using maximum likelihood channel estimation
Wang, Kezhi
2014-09-01
Bit error rates (BERs) for amplify-and-forward (AF) relaying systems with two different pilot-symbol-aided channel estimation methods, disintegrated channel estimation (DCE) and cascaded channel estimation (CCE), are derived in Rayleigh fading channels. Based on these BERs, the pilot powers at the source and at the relay are optimized when their total transmitting powers are fixed. Numerical results show that the optimized system has a better performance than other conventional nonoptimized allocation systems. They also show that the optimal pilot power in variable gain is nearly the same as that in fixed gain for similar system settings. andcopy; 2014 IEEE.
Directory of Open Access Journals (Sweden)
Meron Gurkiewicz
2007-08-01
Full Text Available The activity of trans-membrane proteins such as ion channels is the essence of neuronal transmission. The currently most accurate method for determining ion channel kinetic mechanisms is single-channel recording and analysis. Yet, the limitations and complexities in interpreting single-channel recordings discourage many physiologists from using them. Here we show that a genetic search algorithm in combination with a gradient descent algorithm can be used to fit whole-cell voltage-clamp data to kinetic models with a high degree of accuracy. Previously, ion channel stimulation traces were analyzed one at a time, the results of these analyses being combined to produce a picture of channel kinetics. Here the entire set of traces from all stimulation protocols are analysed simultaneously. The algorithm was initially tested on simulated current traces produced by several Hodgkin-Huxley-like and Markov chain models of voltage-gated potassium and sodium channels. Currents were also produced by simulating levels of noise expected from actual patch recordings. Finally, the algorithm was used for finding the kinetic parameters of several voltage-gated sodium and potassium channels models by matching its results to data recorded from layer 5 pyramidal neurons of the rat cortex in the nucleated outside-out patch configuration. The minimization scheme gives electrophysiologists a tool for reproducing and simulating voltage-gated ion channel kinetics at the cellular level.
Effects of Channel Estimation Errors in OFDM-MIMO-based Underwater Communications
Håkegård, Jan Erik; Grythe, Knut Harald
2009-01-01
State-of-the-art radio communication systems are in a large extent based on multi-carrier communication (OFDM) and multiple antennas (MIMO). In this paper the performance of such systems adapted to an underwater acoustic communication channel is assessed. The effect of the channel characteristics on an OFDM-MIMO scheme similar to that used in WiMAX (IEEE802.16e) is analyzed, in particular related to channel estimation error. Simulation results illustrate the relation between estimation error ...
A Meshsize Boosting Algorithm In Kernel Density Estimation ...
African Journals Online (AJOL)
KDE). This algorithm enjoys the property of a bias reduction technique like other existing boosting algorithms and also enjoys the property of less function evaluations when compared with other boosting schemes. Numerical examples are used ...
Joint Smoothed l0-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar
National Research Council Canada - National Science Library
Jing Liu; Weidong Zhou; Filbert H Juwono
2017-01-01
.... In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l0 -norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar...
Directory of Open Access Journals (Sweden)
Xiaoyang Liu
2017-01-01
Full Text Available In order to analyze the channel estimation performance of near space high altitude platform station (HAPS in wireless communication system, the structure and formation of HAPS are studied in this paper. The traditional Least Squares (LS channel estimation method and Singular Value Decomposition-Linear Minimum Mean-Squared (SVD-LMMS channel estimation method are compared and investigated. A novel channel estimation method and model are proposed. The channel estimation performance of HAPS is studied deeply. The simulation and theoretical analysis results show that the performance of the proposed method is better than the traditional methods. The lower Bit Error Rate (BER and higher Signal Noise Ratio (SNR can be obtained by the proposed method compared with the LS and SVD-LMMS methods.
DEFF Research Database (Denmark)
Gomez Gonzalvo, A.; Vegas Olmos, Juan José; Tafur Monroy, Idelfonso
2014-01-01
This paper presents a performance assessment of an algorithm for hybrid fiber-wireless photonic channel allocation in 5G using radio-over-fiber with active delivery. Simulations show reductions of network blocking probability in 98% of the tested cases......This paper presents a performance assessment of an algorithm for hybrid fiber-wireless photonic channel allocation in 5G using radio-over-fiber with active delivery. Simulations show reductions of network blocking probability in 98% of the tested cases...
de Gunst, M.C.M.; Schouten, J.G.
2005-01-01
We present a statistical method, and its accompanying algorithms, for the selection of a mathematical model of the gating mechanism of an ion channel and for the estimation of the parameters of this model. The method assumes a hidden Markov model that incorporates filtering, colored noise and
Abuzaid, Abdulrahman I.
2014-09-01
Efficient receiver designs for cooperative communication systems are becoming increasingly important. In previous work, cooperative networks communicated with the use of $L$ relays. As the receiver is constrained, it can only process $U$ out of $L$ relays. Channel shortening and reduced-rank techniques were employed to design the preprocessing matrix. In this paper, a receiver structure is proposed which combines the joint iterative optimization (JIO) algorithm and our proposed threshold selection criteria. This receiver structure assists in determining the optimal $U-{opt}$. Furthermore, this receiver provides the freedom to choose $U ≤ U-{opt}$ for each frame depending upon the tolerable difference allowed for mean square error (MSE). Our study and simulation results show that by choosing an appropriate threshold, it is possible to gain in terms of complexity savings without affecting the BER performance of the system. Furthermore, in this paper the effect of channel estimation errors is investigated on the MSE performance of the amplify-and-forward (AF) cooperative relaying system.
Optimizing ion channel models using a parallel genetic algorithm on graphical processors.
Ben-Shalom, Roy; Aviv, Amit; Razon, Benjamin; Korngreen, Alon
2012-01-01
We have recently shown that we can semi-automatically constrain models of voltage-gated ion channels by combining a stochastic search algorithm with ionic currents measured using multiple voltage-clamp protocols. Although numerically successful, this approach is highly demanding computationally, with optimization on a high performance Linux cluster typically lasting several days. To solve this computational bottleneck we converted our optimization algorithm for work on a graphical processing unit (GPU) using NVIDIA's CUDA. Parallelizing the process on a Fermi graphic computing engine from NVIDIA increased the speed ∼180 times over an application running on an 80 node Linux cluster, considerably reducing simulation times. This application allows users to optimize models for ion channel kinetics on a single, inexpensive, desktop "super computer," greatly reducing the time and cost of building models relevant to neuronal physiology. We also demonstrate that the point of algorithm parallelization is crucial to its performance. We substantially reduced computing time by solving the ODEs (Ordinary Differential Equations) so as to massively reduce memory transfers to and from the GPU. This approach may be applied to speed up other data intensive applications requiring iterative solutions of ODEs. Copyright © 2012 Elsevier B.V. All rights reserved.
Single-channel color image encryption using phase retrieve algorithm in fractional Fourier domain
Sui, Liansheng; Xin, Meiting; Tian, Ailing; Jin, Haiyan
2013-12-01
A single-channel color image encryption is proposed based on a phase retrieve algorithm and a two-coupled logistic map. Firstly, a gray scale image is constituted with three channels of the color image, and then permuted by a sequence of chaotic pairs generated by the two-coupled logistic map. Secondly, the permutation image is decomposed into three new components, where each component is encoded into a phase-only function in the fractional Fourier domain with a phase retrieve algorithm that is proposed based on the iterative fractional Fourier transform. Finally, an interim image is formed by the combination of these phase-only functions and encrypted into the final gray scale ciphertext with stationary white noise distribution by using chaotic diffusion, which has camouflage property to some extent. In the process of encryption and decryption, chaotic permutation and diffusion makes the resultant image nonlinear and disorder both in spatial domain and frequency domain, and the proposed phase iterative algorithm has faster convergent speed. Additionally, the encryption scheme enlarges the key space of the cryptosystem. Simulation results and security analysis verify the feasibility and effectiveness of this method.
Automatic Estimation of the Dynamics of Channel Conductance Using a Recurrent Neural Network
Directory of Open Access Journals (Sweden)
Masaaki Takahashi
2009-01-01
Full Text Available In order to simulate neuronal electrical activities, we must estimate the dynamics of channel conductances from physiological experimental data. However, this approach requires the formulation of differential equations that express the time course of channel conductance. On the other hand, if the dynamics are automatically estimated, neuronal activities can be easily simulated. By using a recurrent neural network (RNN, it is possible to estimate the dynamics of channel conductances without formulating the differential equations. In the present study, we estimated the dynamics of the Na+ and K+ conductances of a squid giant axon using two different fully connected RNNs and were able to reproduce various neuronal activities of the axon. The reproduced activities were an action potential, a threshold, a refractory phenomenon, a rebound action potential, and periodic action potentials with a constant stimulation. RNNs can be trained using channels other than the Na+ and K+ channels. Therefore, using our RNN estimation method, the dynamics of channel conductance can be automatically estimated and the neuronal activities can be simulated using the channel RNNs. An RNN can be a useful tool to estimate the dynamics of the channel conductance of a neuron, and by using the method presented here, it is possible to simulate neuronal activities more easily than by using the previous methods.
Duan, Hanjun; Wu, Haifeng; Zeng, Yu; Chen, Yuebin
2016-03-26
In a passive ultra-high frequency (UHF) radio-frequency identification (RFID) system, tag collision is generally resolved on a medium access control (MAC) layer. However, some of collided tag signals could be recovered on a physical (PHY) layer and, thus, enhance the identification efficiency of the RFID system. For the recovery on the PHY layer, channel estimation is a critical issue. Good channel estimation will help to recover the collided signals. Existing channel estimates work well for two collided tags. When the number of collided tags is beyond two, however, the existing estimates have more estimation errors. In this paper, we propose a novel channel estimate for the UHF RFID system. It adopts an orthogonal matrix based on the information of preambles which is known for a reader and applies a minimum-mean-square-error (MMSE) criterion to estimate channels. From the estimated channel, we could accurately separate the collided signals and recover them. By means of numerical results, we show that the proposed estimate has lower estimation errors and higher separation efficiency than the existing estimates.
Directory of Open Access Journals (Sweden)
Hanjun Duan
2016-03-01
Full Text Available In a passive ultra-high frequency (UHF radio-frequency identification (RFID system, tag collision is generally resolved on a medium access control (MAC layer. However, some of collided tag signals could be recovered on a physical (PHY layer and, thus, enhance the identification efficiency of the RFID system. For the recovery on the PHY layer, channel estimation is a critical issue. Good channel estimation will help to recover the collided signals. Existing channel estimates work well for two collided tags. When the number of collided tags is beyond two, however, the existing estimates have more estimation errors. In this paper, we propose a novel channel estimate for the UHF RFID system. It adopts an orthogonal matrix based on the information of preambles which is known for a reader and applies a minimum-mean-square-error (MMSE criterion to estimate channels. From the estimated channel, we could accurately separate the collided signals and recover them. By means of numerical results, we show that the proposed estimate has lower estimation errors and higher separation efficiency than the existing estimates.
A Lightning Channel Retrieval Algorithm for the North Alabama Lightning Mapping Array (LMA)
Koshak, William; Arnold, James E. (Technical Monitor)
2002-01-01
A new multi-station VHF time-of-arrival (TOA) antenna network is, at the time of this writing, coming on-line in Northern Alabama. The network, called the Lightning Mapping Array (LMA), employs GPS timing and detects VHF radiation from discrete segments (effectively point emitters) that comprise the channel of lightning strokes within cloud and ground flashes. The network will support on-going ground validation activities of the low Earth orbiting Lightning Imaging Sensor (LIS) satellite developed at NASA Marshall Space Flight Center (MSFC) in Huntsville, Alabama. It will also provide for many interesting and detailed studies of the distribution and evolution of thunderstorms and lightning in the Tennessee Valley, and will offer many interesting comparisons with other meteorological/geophysical wets associated with lightning and thunderstorms. In order to take full advantage of these benefits, it is essential that the LMA channel mapping accuracy (in both space and time) be fully characterized and optimized. In this study, a new revised channel mapping retrieval algorithm is introduced. The algorithm is an extension of earlier work provided in Koshak and Solakiewicz (1996) in the analysis of the NASA Kennedy Space Center (KSC) Lightning Detection and Ranging (LDAR) system. As in the 1996 study, direct algebraic solutions are obtained by inverting a simple linear system of equations, thereby making computer searches through a multi-dimensional parameter domain of a Chi-Squared function unnecessary. However, the new algorithm is developed completely in spherical Earth-centered coordinates (longitude, latitude, altitude), rather than in the (x, y, z) cartesian coordinates employed in the 1996 study. Hence, no mathematical transformations from (x, y, z) into spherical coordinates are required (such transformations involve more numerical error propagation, more computer program coding, and slightly more CPU computing time). The new algorithm also has a more realistic
Formal Computer Validation of the Quantum Phase Estimation Algorithm
Witzel, Wayne; Rudinger, Kenneth; Sarovar, Mohan; Carr, Robert
While peer review and scientific consensus provide some assurance to the validity of ideas, people do make mistakes that can slip through the cracks. A plethora of formal methods tools exist and are in use in a variety of settings where high assurance is demanded. Existing tools, however, require a great deal of expertise and lack versatility, demanding a non-trivial translation between a high-level description of a problem and the formal system. Our software, called Prove-It, allows a nearly direct translation between human-recognizable formulations and the underlying formal system. While Prove-It is not designed for particularly efficient automation, a primary goal of other formal methods tools, it is extremely flexible in following a desired line of reasoning (proof structure). This approach is particularly valuable for validating proofs that are already known. We will demonstrate a validation of the Quantum Phase Estimation Algorithm using Prove-It. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under Contract DE-AC04-94AL85000. This work was supported by the Laboratory Directed Research and Development program at Sandia National Laboratories.
A Fast Algorithm for the Computation of HAC Covariance Matrix Estimators
Directory of Open Access Journals (Sweden)
Jochen Heberle
2017-01-01
Full Text Available This paper considers the algorithmic implementation of the heteroskedasticity and autocorrelation consistent (HAC estimation problem for covariance matrices of parameter estimators. We introduce a new algorithm, mainly based on the fast Fourier transform, and show via computer simulation that our algorithm is up to 20 times faster than well-established alternative algorithms. The cumulative effect is substantial if the HAC estimation problem has to be solved repeatedly. Moreover, the bandwidth parameter has no impact on this performance. We provide a general description of the new algorithm as well as code for a reference implementation in R.
Directory of Open Access Journals (Sweden)
Chuii Khim Chong
2012-06-01
Full Text Available This paper introduces an improved Differential Evolution algorithm (IDE which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation algorithms face obstacles due to the noisy data and difficulty of the system in estimating myriad of parameters, and require longer computational time to estimate the relevant parameters. The proposed algorithm (IDE in this paper is a hybrid of a Differential Evolution algorithm (DE and a Kalman Filter (KF. The outcome of IDE is proven to be superior than Genetic Algorithm (GA and DE. The results of IDE from experiments show estimated optimal kinetic parameters values, shorter computation time and increased accuracy for simulated results compared with other estimation algorithms
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.
Fractional Poisson-Nernst-Planck Model for Ion Channels I: Basic Formulations and Algorithms.
Chen, Duan
2017-11-01
In this work, we propose a fractional Poisson-Nernst-Planck model to describe ion permeation in gated ion channels. Due to the intrinsic conformational changes, crowdedness in narrow channel pores, binding and trapping introduced by functioning units of channel proteins, ionic transport in the channel exhibits a power-law-like anomalous diffusion dynamics. We start from continuous-time random walk model for a single ion and use a long-tailed density distribution function for the particle jump waiting time, to derive the fractional Fokker-Planck equation. Then, it is generalized to the macroscopic fractional Poisson-Nernst-Planck model for ionic concentrations. Necessary computational algorithms are designed to implement numerical simulations for the proposed model, and the dynamics of gating current is investigated. Numerical simulations show that the fractional PNP model provides a more qualitatively reasonable match to the profile of gating currents from experimental observations. Meanwhile, the proposed model motivates new challenges in terms of mathematical modeling and computations.
Takeuchi, Keigo
2012-01-01
User selection (US) with Zero-forcing beamforming (ZF-BF) is considered in fast fading Gaussian vector broadcast channels (VBCs) with perfect channel state information (CSI) at the transmitter. A novel criterion for US is proposed, which depends on both CSI and the data symbols, while the conventional criteria only depend on CSI. Since the optimization of US based on the proposed criterion is infeasible, a greedy algorithm of data-dependent US is proposed to perform the optimization approximately. An overhead issue arises in fast fading channels: On every update of US, the transmitter may inform each user whether he/she has been selected, using a certain fraction of resources. This overhead results in a significant rate loss for fast fading channels. In order to circumvent this overhead issue, iterative detection and decoding schemes are derived on the basis of belief propagation (BP). The proposed iterative schemes require no information about whether each user has been selected. The proposed US scheme is co...
Gong, Li-Hua; He, Xiang-Tao; Tan, Ru-Chao; Zhou, Zhi-Hong
2017-09-01
In order to obtain high-quality color images, it is important to keep the hue component unchanged while emphasize the intensity or saturation component. As a public color model, Hue-Saturation Intensity (HSI) model is commonly used in image processing. A new single channel quantum color image encryption algorithm based on HSI model and quantum Fourier transform (QFT) is investigated, where the color components of the original color image are converted to HSI and the logistic map is employed to diffuse the relationship of pixels in color components. Subsequently, quantum Fourier transform is exploited to fulfill the encryption. The cipher-text is a combination of a gray image and a phase matrix. Simulations and theoretical analyses demonstrate that the proposed single channel quantum color image encryption scheme based on the HSI model and quantum Fourier transform is secure and effective.
Fast thresholded multi-channel Landweber algorithm for wavelet-regularized multi-angle deconvolution
Chacko, Nikhil; Liebling, Michael
2013-09-01
3D deconvolution in optical wide eld microscopy aims at recovering optical sections through thick objects. Acquiring data from multiple, mutually-tilted directions helps ll the missing cone of information in the optical transfer function, which normally renders the deconvolution problem particularly ill-posed. Here, we propose a fast-converging iterative deconvolution method for multi-angle deconvolution microscopy. Specically, we formulate the imaging problem using a lter-bank structure, and present a multi-channel variation of a thresholded Landweber deconvolution algorithm with wavelet-sparsity regularization. Decomposition of the minimization problem into subband-dependent terms ensures fast convergence. We demonstrate the applicability of the algorithm via simulation results.
Directory of Open Access Journals (Sweden)
Jérôme Viéron
2004-03-01
Full Text Available This paper introduces source-channel adaptive rate control (SARC, a new congestion control algorithm for layered video transmission in large multicast groups. In order to solve the well-known feedback implosion problem in large multicast groups, we first present a mechanism for filtering RTCP receiver reports sent from receivers to the whole session. The proposed filtering mechanism provides a classification of receivers according to a predefined similarity measure. An end-to-end source and FEC rate control based on this distributed feedback aggregation mechanism coupled with a video layered coding system is then described. The number of layers, their rate, and their levels of protection are adapted dynamically to aggregated feedbacks. The algorithms have been validated with the NS2 network simulator.
Hybrid fuzzy charged system search algorithm based state estimation in distribution networks
Directory of Open Access Journals (Sweden)
Sachidananda Prasad
2017-06-01
Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.
Banerjee, Kinshuk; Das, Biswajit; Gangopadhyay, Gautam
2013-04-28
In this paper, we have explored generic criteria of cooperative behavior in ion channel kinetics treating it on the same footing with multistate receptor-ligand binding in a compact theoretical framework. We have shown that the characterization of cooperativity of ion channels in terms of the Hill coefficient violates the standard Hill criteria defined for allosteric cooperativity of ligand binding. To resolve the issue, an alternative measure of cooperativity is proposed here in terms of the cooperativity index that sets a unified criteria for both the systems. More importantly, for ion channel this index can be very useful to describe the cooperative kinetics as it can be readily determined from the experimentally measured ionic current combined with theoretical modelling. We have analyzed the correlation between the voltage value and slope of the voltage-activation curve at the half-activation point and consequently determined the standard free energy of activation of the ion channel using two well-established mechanisms of cooperativity, namely, Koshland-Nemethy-Filmer (KNF) and Monod-Wyman-Changeux (MWC) models. Comparison of the theoretical results for both the models with appropriate experimental data of mutational perturbation of Shaker K(+) channel supports the experimental fact that the KNF model is more suitable to describe the cooperative behavior of this class of ion channels, whereas the performance of the MWC model is unsatisfactory. We have also estimated the mechanistic performance through standard free energy of channel activation for both the models and proposed a possible functional disadvantage in the MWC scheme.
Banerjee, Kinshuk; Das, Biswajit; Gangopadhyay, Gautam
2013-04-01
In this paper, we have explored generic criteria of cooperative behavior in ion channel kinetics treating it on the same footing with multistate receptor-ligand binding in a compact theoretical framework. We have shown that the characterization of cooperativity of ion channels in terms of the Hill coefficient violates the standard Hill criteria defined for allosteric cooperativity of ligand binding. To resolve the issue, an alternative measure of cooperativity is proposed here in terms of the cooperativity index that sets a unified criteria for both the systems. More importantly, for ion channel this index can be very useful to describe the cooperative kinetics as it can be readily determined from the experimentally measured ionic current combined with theoretical modelling. We have analyzed the correlation between the voltage value and slope of the voltage-activation curve at the half-activation point and consequently determined the standard free energy of activation of the ion channel using two well-established mechanisms of cooperativity, namely, Koshland-Nemethy-Filmer (KNF) and Monod-Wyman-Changeux (MWC) models. Comparison of the theoretical results for both the models with appropriate experimental data of mutational perturbation of Shaker K^+ channel supports the experimental fact that the KNF model is more suitable to describe the cooperative behavior of this class of ion channels, whereas the performance of the MWC model is unsatisfactory. We have also estimated the mechanistic performance through standard free energy of channel activation for both the models and proposed a possible functional disadvantage in the MWC scheme.
Evaluating Prognostics Performance for Algorithms Incorporating Uncertainty Estimates
National Aeronautics and Space Administration — Uncertainty Representation and Management (URM) are an integral part of the prognostic system development.1As capabilities of prediction algorithms evolve, research...
Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation.
Lee, Min Seok; Park, Sang Wook; Kang, Moon Gi
2017-05-28
In this paper, a spatio-spectral-temporal filter considering an inter-channel correlation is proposed for the denoising of a color filter array (CFA) sequence acquired by CCD/CMOS image sensors. Owing to the alternating under-sampled grid of the CFA pattern, the inter-channel correlation must be considered in the direct denoising process. The proposed filter is applied in the spatial, spectral, and temporal domain, considering the spatio-tempo-spectral correlation. First, nonlocal means (NLM) spatial filtering with patch-based difference (PBD) refinement is performed by considering both the intra-channel correlation and inter-channel correlation to overcome the spatial resolution degradation occurring with the alternating under-sampled pattern. Second, a motion-compensated temporal filter that employs inter-channel correlated motion estimation and compensation is proposed to remove the noise in the temporal domain. Then, a motion adaptive detection value controls the ratio of the spatial filter and the temporal filter. The denoised CFA sequence can thus be obtained without motion artifacts. Experimental results for both simulated and real CFA sequences are presented with visual and numerical comparisons to several state-of-the-art denoising methods combined with a demosaicing method. Experimental results confirmed that the proposed frameworks outperformed the other techniques in terms of the objective criteria and subjective visual perception in CFA sequences.
Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation
Lee, Min Seok; Park, Sang Wook; Kang, Moon Gi
2017-01-01
In this paper, a spatio-spectral-temporal filter considering an inter-channel correlation is proposed for the denoising of a color filter array (CFA) sequence acquired by CCD/CMOS image sensors. Owing to the alternating under-sampled grid of the CFA pattern, the inter-channel correlation must be considered in the direct denoising process. The proposed filter is applied in the spatial, spectral, and temporal domain, considering the spatio-tempo-spectral correlation. First, nonlocal means (NLM) spatial filtering with patch-based difference (PBD) refinement is performed by considering both the intra-channel correlation and inter-channel correlation to overcome the spatial resolution degradation occurring with the alternating under-sampled pattern. Second, a motion-compensated temporal filter that employs inter-channel correlated motion estimation and compensation is proposed to remove the noise in the temporal domain. Then, a motion adaptive detection value controls the ratio of the spatial filter and the temporal filter. The denoised CFA sequence can thus be obtained without motion artifacts. Experimental results for both simulated and real CFA sequences are presented with visual and numerical comparisons to several state-of-the-art denoising methods combined with a demosaicing method. Experimental results confirmed that the proposed frameworks outperformed the other techniques in terms of the objective criteria and subjective visual perception in CFA sequences. PMID:28555044
Groundwater recharge rate and zone structure estimation using PSOLVER algorithm.
Ayvaz, M Tamer; Elçi, Alper
2014-01-01
The quantification of groundwater recharge is an important but challenging task in groundwater flow modeling because recharge varies spatially and temporally. The goal of this study is to present an innovative methodology to estimate groundwater recharge rates and zone structures for regional groundwater flow models. Here, the unknown recharge field is partitioned into a number of zones using Voronoi Tessellation (VT). The identified zone structure with the recharge rates is associated through a simulation-optimization model that couples MODFLOW-2000 and the hybrid PSOLVER optimization algorithm. Applicability of this procedure is tested on a previously developed groundwater flow model of the Tahtalı Watershed. Successive zone structure solutions are obtained in an additive manner and penalty functions are used in the procedure to obtain realistic and plausible solutions. One of these functions constrains the optimization by forcing the sum of recharge rates for the grid cells that coincide with the Tahtalı Watershed area to be equal to the areal recharge rate determined in the previous modeling by a separate precipitation-runoff model. As a result, a six-zone structure is selected as the best zone structure that represents the areal recharge distribution. Comparison to results of a previous model for the same study area reveals that the proposed procedure significantly improves model performance with respect to calibration statistics. The proposed identification procedure can be thought of as an effective way to determine the recharge zone structure for groundwater flow models, in particular for situations where tangible information about groundwater recharge distribution does not exist. © 2013, National Ground Water Association.
High-dimensional quantum channel estimation using classical light
CSIR Research Space (South Africa)
Mabena, Chemist M
2017-11-01
Full Text Available A method is proposed to characterize a high-dimensional quantum channel with the aid of classical light. It uses a single nonseparable input optical field that contains correlations between spatial modes and wavelength to determine the effect...
An Accurate FFPA-PSR Estimator Algorithm and Tool for Software Effort Estimation
Directory of Open Access Journals (Sweden)
Senthil Kumar Murugesan
2015-01-01
Full Text Available Software companies are now keen to provide secure software with respect to accuracy and reliability of their products especially related to the software effort estimation. Therefore, there is a need to develop a hybrid tool which provides all the necessary features. This paper attempts to propose a hybrid estimator algorithm and model which incorporates quality metrics, reliability factor, and the security factor with a fuzzy-based function point analysis. Initially, this method utilizes a fuzzy-based estimate to control the uncertainty in the software size with the help of a triangular fuzzy set at the early development stage. Secondly, the function point analysis is extended by the security and reliability factors in the calculation. Finally, the performance metrics are added with the effort estimation for accuracy. The experimentation is done with different project data sets on the hybrid tool, and the results are compared with the existing models. It shows that the proposed method not only improves the accuracy but also increases the reliability, as well as the security, of the product.
An Accurate FFPA-PSR Estimator Algorithm and Tool for Software Effort Estimation.
Murugesan, Senthil Kumar; Balasubramanian, Chidhambara Rajan
2015-01-01
Software companies are now keen to provide secure software with respect to accuracy and reliability of their products especially related to the software effort estimation. Therefore, there is a need to develop a hybrid tool which provides all the necessary features. This paper attempts to propose a hybrid estimator algorithm and model which incorporates quality metrics, reliability factor, and the security factor with a fuzzy-based function point analysis. Initially, this method utilizes a fuzzy-based estimate to control the uncertainty in the software size with the help of a triangular fuzzy set at the early development stage. Secondly, the function point analysis is extended by the security and reliability factors in the calculation. Finally, the performance metrics are added with the effort estimation for accuracy. The experimentation is done with different project data sets on the hybrid tool, and the results are compared with the existing models. It shows that the proposed method not only improves the accuracy but also increases the reliability, as well as the security, of the product.
CSIR Research Space (South Africa)
Sun, Z
2013-09-01
Full Text Available Net radiation is a key component of the energy balance, whose estimation accuracy has an impact on energy flux estimates from satellite data. In typical remote sensing evapotranspiration (ET) algorithms, the outgoing shortwave and longwave...
Directory of Open Access Journals (Sweden)
Yuxiang He
2018-01-01
Full Text Available This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE that would objectively blend real-time satellite quantitative precipitation estimates (SQPE with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a five-year period between 2003–2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS (National Weather Service operations over two regions: (I Inside radar effective coverage and (II immediately outside radar coverage. The outcomes of the evaluation indicate (a ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and (b blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.
Rotated Walsh-Hadamard Spreading with Robust Channel Estimation for a Coded MC-CDMA System
Directory of Open Access Journals (Sweden)
Raulefs Ronald
2004-01-01
Full Text Available We investigate rotated Walsh-Hadamard spreading matrices for a broadband MC-CDMA system with robust channel estimation in the synchronous downlink. The similarities between rotated spreading and signal space diversity are outlined. In a multiuser MC-CDMA system, possible performance improvements are based on the chosen detector, the channel code, and its Hamming distance. By applying rotated spreading in comparison to a standard Walsh-Hadamard spreading code, a higher throughput can be achieved. As combining the channel code and the spreading code forms a concatenated code, the overall minimum Hamming distance of the concatenated code increases. This asymptotically results in an improvement of the bit error rate for high signal-to-noise ratio. Higher convolutional channel code rates are mostly generated by puncturing good low-rate channel codes. The overall Hamming distance decreases significantly for the punctured channel codes. Higher channel code rates are favorable for MC-CDMA, as MC-CDMA utilizes diversity more efficiently compared to pure OFDMA. The application of rotated spreading in an MC-CDMA system allows exploiting diversity even further. We demonstrate that the rotated spreading gain is still present for a robust pilot-aided channel estimator. In a well-designed system, rotated spreading extends the performance by using a maximum likelihood detector with robust channel estimation at the receiver by about 1 dB.
2-D DOA Estimation of LFM Signals Based on Dechirping Algorithm and Uniform Circle Array
Directory of Open Access Journals (Sweden)
K. B. Cui
2017-04-01
Full Text Available Based on Dechirping algorithm and uniform circle array(UCA, a new 2-D direction of arrival (DOA estimation algorithm of linear frequency modulation (LFM signals is proposed in this paper. The algorithm uses the thought of Dechirping and regards the signal to be estimated which is received by the reference sensor as the reference signal and proceeds the difference frequency treatment with the signal received by each sensor. So the signal to be estimated becomes a single-frequency signal in each sensor. Then we transform the single-frequency signal to an isolated impulse through Fourier transform (FFT and construct a new array data model based on the prominent parts of the impulse. Finally, we respectively use multiple signal classification (MUSIC algorithm and rotational invariance technique (ESPRIT algorithm to realize 2-D DOA estimation of LFM signals. The simulation results verify the effectiveness of the algorithm proposed.
Conveyance estimation in channels with emergent bank vegetation
African Journals Online (AJOL)
2009-03-20
Mar 20, 2009 ... A quadratic equation for fb in terms of flow depth was fitted through these points, and used to determine fb for each vegetated channel depth. The resulting values of fb ranged from 0.022 to 0.044 with 10 of the 12 values occurring between 0.022 and 0.028. Resistance coefficients for the bed and the stem ...
Comparison of parameter estimation algorithms in hydrological modelling
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan
2006-01-01
for these types of models, although at a more expensive computational cost. The main purpose of this study is to investigate the performance of a global and a local parameter optimization algorithm, respectively, the Shuffled Complex Evolution (SCE) algorithm and the gradient-based Gauss......Local search methods have been applied successfully in calibration of simple groundwater models, but might fail in locating the optimum for models of increased complexity, due to the more complex shape of the response surface. Global search algorithms have been demonstrated to perform well......-Marquardt-Levenberg algorithm (implemented in the PEST software), when applied to a steady-state and a transient groundwater model. The results show that PEST can have severe problems in locating the global optimum and in being trapped in local regions of attractions. The global SCE procedure is, in general, more effective...
Liang Yang,
2013-06-01
In this paper, we consider the performance of a two-way amplify-and-forward relaying network (AF TWRN) in the presence of unequal power co-channel interferers (CCI). Specifically, we first consider AF TWRN with an interference-limited relay and two noisy-nodes with channel estimation errors and CCI. We derive the approximate signal-to-interference plus noise ratio expressions and then use them to evaluate the outage probability, error probability, and achievable rate. Subsequently, to investigate the joint effects of the channel estimation error and CCI on the system performance, we extend our analysis to a multiple-relay network and derive several asymptotic performance expressions. For comparison purposes, we also provide the analysis for the relay selection scheme under the total power constraint at the relays. For AF TWRN with channel estimation error and CCI, numerical results show that the performance of the relay selection scheme is not always better than that of the all-relay participating case. In particular, the relay selection scheme can improve the system performance in the case of high power levels at the sources and small powers at the relays.
A Novel Modification of PSO Algorithm for SML Estimation of DOA.
Chen, Haihua; Li, Shibao; Liu, Jianhang; Liu, Fen; Suzuki, Masakiyo
2016-12-19
This paper addresses the issue of reducing the computational complexity of Stochastic Maximum Likelihood (SML) estimation of Direction-of-Arrival (DOA). The SML algorithm is well-known for its high accuracy of DOA estimation in sensor array signal processing. However, its computational complexity is very high because the estimation of SML criteria is a multi-dimensional non-linear optimization problem. As a result, it is hard to apply the SML algorithm to real systems. The Particle Swarm Optimization (PSO) algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation. However, the conventional PSO algorithm suffers two defects, namely, too many particles and too many iteration times. Therefore, the computational complexity of SML estimation using conventional PSO algorithm is still a little high. To overcome these two defects and to reduce computational complexity further, this paper proposes a novel modification of the conventional PSO algorithm for SML estimation and we call it Joint-PSO algorithm. The core idea of the modification lies in that it uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space. Since this initialization space is already close to the solution of SML, fewer particles and fewer iteration times are needed. As a result, the computational complexity can be greatly reduced. In simulation, we compare the proposed algorithm with the conventional PSO algorithm, the classic Altering Minimization (AM) algorithm and Genetic algorithm (GA). Simulation results show that our proposed algorithm is one of the most efficient solving algorithms and it shows great potential for the application of SML in real systems.
A Novel Modification of PSO Algorithm for SML Estimation of DOA
Directory of Open Access Journals (Sweden)
Haihua Chen
2016-12-01
Full Text Available This paper addresses the issue of reducing the computational complexity of Stochastic Maximum Likelihood (SML estimation of Direction-of-Arrival (DOA. The SML algorithm is well-known for its high accuracy of DOA estimation in sensor array signal processing. However, its computational complexity is very high because the estimation of SML criteria is a multi-dimensional non-linear optimization problem. As a result, it is hard to apply the SML algorithm to real systems. The Particle Swarm Optimization (PSO algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation. However, the conventional PSO algorithm suffers two defects, namely, too many particles and too many iteration times. Therefore, the computational complexity of SML estimation using conventional PSO algorithm is still a little high. To overcome these two defects and to reduce computational complexity further, this paper proposes a novel modification of the conventional PSO algorithm for SML estimation and we call it Joint-PSO algorithm. The core idea of the modification lies in that it uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT and stochastic Cramer-Rao bound (CRB to determine a novel initialization space. Since this initialization space is already close to the solution of SML, fewer particles and fewer iteration times are needed. As a result, the computational complexity can be greatly reduced. In simulation, we compare the proposed algorithm with the conventional PSO algorithm, the classic Altering Minimization (AM algorithm and Genetic algorithm (GA. Simulation results show that our proposed algorithm is one of the most efficient solving algorithms and it shows great potential for the application of SML in real systems.
Yang, Liang
2013-04-01
In this paper, we consider the performance of a two-way amplify-and-forward relaying network (AF TWRN) in the presence of unequal power co-channel interferers (CCI). Specifically, we consider AF TWRN with an interference-limited relay and two noisy-nodes with channel estimation error and CCI. We derive the approximate signal-to-interference plus noise ratio expressions and then use these expressions to evaluate the outage probability and error probability. Numerical results show that the approximate closed-form expressions are very close to the exact ones. © 2013 IEEE.
J. Grahl; S. Minner; P.A.N. Bosman (Peter); Z. Michalewicz; P. Siarry
2008-01-01
htmlabstractThis chapter serves as an introduction to estimation of distribution algorithms (EDAs). Estimation of distribution algorithms are a new paradigm in evolutionary computation. They combine statistical learning with population-based search in order to automatically identify and exploit
The MISO Wiretap Channel with Noisy Main Channel Estimation in the High Power Regime
Rezki, Zouheir
2017-02-07
We improve upon our previous upper bound on the secrecy capacity of the wiretap channel with multiple transmit antennas and single-antenna receivers, with noisy main channel state information (CSI) at the transmitter (CSI-T). Specifically, we show that if the main CSI error does not scale with the power budget at the transmitter P̅, then the secrecy capacity is )bounded above essentially by log log (P̅ yielding a secure degree of freedom (sdof) equal to zero. However, if the main CSI error scales as O(P̅-β), for β ∈ [0,1], then the sdof is equal to β.
Huang, Chengjun; Chen, Xiang; Cao, Shuai; Qiu, Bensheng; Zhang, Xu
2017-08-01
Objective. To realize accurate muscle force estimation, a novel framework is proposed in this paper which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle. Approach. Surface electromyographic (sEMG) signals from the biceps brachii muscle during isometric elbow flexion were collected with a high-density (HD) electrode grid (128 channels) and the external force at three contraction levels was measured at the wrist synchronously. The sEMG envelope matrix was factorized into a matrix of basis vectors with each column representing an activation pattern and a matrix of time-varying coefficients by a nonnegative matrix factorization (NMF) algorithm. The activation pattern with the highest activation intensity, which was defined as the sum of the absolute values of the time-varying coefficient curve, was considered as the major activation pattern, and its channels with high weighting factors were selected to extract the input activation signal of a force estimation model based on the polynomial fitting technique. Main results. Compared with conventional methods using the whole channels of the grid, the proposed method could significantly improve the quality of force estimation and reduce the electrode number. Significance. The proposed method provides a way to find proper electrode placement for force estimation, which can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.
An MCMC Algorithm for Estimating the Q-matrix in a Bayesian Framework
Chung, Mengta; Johnson, Matthew S.
2018-01-01
The purpose of this research is to develop an MCMC algorithm for estimating the Q-matrix. Based on the DINA model, the algorithm starts with estimating correlated attributes. Using a saturated model and a binary decimal conversion, the algorithm transforms possible attribute patterns to a Multinomial distribution. Along with the likelihood of an attribute pattern, a Dirichlet distribution, constructed using Gamma distributions, is used as the prior to sample from the posterior. Correlated att...
National Research Council Canada - National Science Library
Romero-Aguirre, E; Parra-Michel, R; Carrasco-Alvarez, Roberto; Orozco-Lugo, A. G
2012-01-01
... (APs) for DDST channel estimation are presented. Both architectures, designed under full-hardware paradigm, were described using Verilog HDL, targeted in Xilinx Virtex-5 and they were compared with existent approaches...
Cramer-Rao Bound for Blind Channel Estimators in Redundant Block Transmission Systems
Li, Yen-Huan; Yeh, Ping-Cheng
2011-01-01
In this paper, we derive the Cramer-Rao bound (CRB) for blind channel estimation in redundant block transmission systems, a lower bound for the mean squared error of any blind channel estimators. The derived CRB is valid for any full-rank linear redundant precoder, including both zero-padded (ZP) and cyclic-prefixed (CP) precoders. A simple form of CRBs for multiple complex parameters is also derived and presented which facilitates the CRB derivation of the problem of interest. A comparison is made between the derived CRBs and performances of existing subspace-based blind channel estimators for both CP and ZP systems. Numerical results show that there is still some room for performance improvement of blind channel estimators.
Pilot-Symbol-Assisted Channel Estimation for Space-Time Coded OFDM Systems
Directory of Open Access Journals (Sweden)
Williams Douglas B
2002-01-01
Full Text Available Space-time coded orthogonal frequency division multiplexing (OFDM transmitter diversity techniques have been shown to provide an efficient means of achieving near optimal diversity gain in frequency-selective fading channels. For these systems, knowledge of the channel parameters is required at the receivers for diversity combining and decoding. In this paper, we propose a low complexity, bandwidth efficient, pilot-symbol-assisted (PSA channel estimator for multiple transmitter OFDM systems. The pilot symbols are constructed to be nonoverlapping in frequency to allow simultaneous sounding of the multiple channels. The time-varying channel responses are tracked by interpolating a set of estimates obtained through periodically transmitted pilot symbols. Simulations are used to verify the effectiveness of the proposed estimator and to examine its limitations. It is also shown that the PSA channel estimator has a lower computational complexity and better performance than a previously proposed decision-directed minimum mean square error MMSE channel estimator for OFDM transmitter diversity systems.
Mehdinejadiani, Behrouz
2017-08-01
This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation.
Mehdinejadiani, Behrouz
2017-08-01
This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation. Copyright © 2017 Elsevier B.V. All rights reserved.
A fast forward/backward semi-blind channel estimation for MIMO STC-OFDM systems
Chang, Lena; Cheng, Ching-Min; Tang, Zay-Shing
2013-09-01
In the study, we propose an efficient subspace-based semiblind channel estimation for multiple-input-multiple-output (MIMO) space-time code (STC) orthogonal frequency-division multiplexing (OFDM) systems. We first proposed a forward-backward estimation (FBE) method which can improve the channel estimation accuracy by using both the forward and backward receiving data. Then, based on the symmetric property of the forward and backward smoothed correlation matrix, we develop a fast forward-backward (FFB) estimation method which estimates the noise subspace by performing eigen-decomposition of two half dimensionality sub-matrices obtained from the forward and backward smoothed correlation matrix. FFB achieves the same performance as the FBE but only requires one-fourth computation complexity of FBE. Computer simulations demonstrate the effectiveness and accuracy in channel estimation of the proposed FFB for the MIMO STC-OFDM systems.
Directory of Open Access Journals (Sweden)
Bansal Shonak
2017-05-01
Full Text Available Nowadays, nature–inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP–complete problems. This paper proposes three approaches to find near–optimal Golomb ruler sequences based on nature–inspired algorithms in a reasonable time. The optimal Golomb ruler (OGR sequences found their application in channel–allocation method that allows suppression of the crosstalk due to four–wave mixing in optical wavelength division multiplexing systems. The simulation results conclude that the proposed nature–inspired metaheuristic optimization algorithms are superior to the existing conventional and nature–inspired algorithms to find near–OGRs in terms of ruler length, total optical channel bandwidth, computation time, and computational complexity. Based on the simulation results, the performance of proposed different nature–inspired metaheuristic algorithms are being compared by using statistical tests. The statistical test results conclude the superiority of the proposed nature–inspired optimization algorithms.
DEFF Research Database (Denmark)
Pittalà, Fabio; Hauske, Fabian N.; Ye, Yabin
2012-01-01
Efficient channel estimation for signal equalization and OPM based on short CAZAC sequences with QPSK and 8PSK constellation formats is demonstrated in a 224-Gb/s PDM 16-QAM optical linear transmission system.......Efficient channel estimation for signal equalization and OPM based on short CAZAC sequences with QPSK and 8PSK constellation formats is demonstrated in a 224-Gb/s PDM 16-QAM optical linear transmission system....
Impact of Channel Estimation Errors on Multiuser Detection via the Replica Method
Directory of Open Access Journals (Sweden)
Li Husheng
2005-01-01
Full Text Available For practical wireless DS-CDMA systems, channel estimation is imperfect due to noise and interference. In this paper, the impact of channel estimation errors on multiuser detection (MUD is analyzed under the framework of the replica method. System performance is obtained in the large system limit for optimal MUD, linear MUD, and turbo MUD, and is validated by numerical results for finite systems.
A Modified Rife Algorithm for Off-Grid DOA Estimation Based on Sparse Representations.
Chen, Tao; Wu, Huanxin; Guo, Limin; Liu, Lutao
2015-11-24
In this paper we address the problem of off-grid direction of arrival (DOA) estimation based on sparse representations in the situation of multiple measurement vectors (MMV). A novel sparse DOA estimation method which changes MMV problem to SMV is proposed. This method uses sparse representations based on weighted eigenvectors (SRBWEV) to deal with the MMV problem. MMV problem can be changed to single measurement vector (SMV) problem by using the linear combination of eigenvectors of array covariance matrix in signal subspace as a new SMV for sparse solution calculation. So the complexity of this proposed algorithm is smaller than other DOA estimation algorithms of MMV. Meanwhile, it can overcome the limitation of the conventional sparsity-based DOA estimation approaches that the unknown directions belong to a predefined discrete angular grid, so it can further improve the DOA estimation accuracy. The modified Rife algorithm for DOA estimation (MRife-DOA) is simulated based on SRBWEV algorithm. In this proposed algorithm, the largest and sub-largest inner products between the measurement vector or its residual and the atoms in the dictionary are utilized to further modify DOA estimation according to the principle of Rife algorithm and the basic idea of coarse-to-fine estimation. Finally, simulation experiments show that the proposed algorithm is effective and can reduce the DOA estimation error caused by grid effect with lower complexity.
A Modified Rife Algorithm for Off-Grid DOA Estimation Based on Sparse Representations
Directory of Open Access Journals (Sweden)
Tao Chen
2015-11-01
Full Text Available In this paper we address the problem of off-grid direction of arrival (DOA estimation based on sparse representations in the situation of multiple measurement vectors (MMV. A novel sparse DOA estimation method which changes MMV problem to SMV is proposed. This method uses sparse representations based on weighted eigenvectors (SRBWEV to deal with the MMV problem. MMV problem can be changed to single measurement vector (SMV problem by using the linear combination of eigenvectors of array covariance matrix in signal subspace as a new SMV for sparse solution calculation. So the complexity of this proposed algorithm is smaller than other DOA estimation algorithms of MMV. Meanwhile, it can overcome the limitation of the conventional sparsity-based DOA estimation approaches that the unknown directions belong to a predefined discrete angular grid, so it can further improve the DOA estimation accuracy. The modified Rife algorithm for DOA estimation (MRife-DOA is simulated based on SRBWEV algorithm. In this proposed algorithm, the largest and sub-largest inner products between the measurement vector or its residual and the atoms in the dictionary are utilized to further modify DOA estimation according to the principle of Rife algorithm and the basic idea of coarse-to-fine estimation. Finally, simulation experiments show that the proposed algorithm is effective and can reduce the DOA estimation error caused by grid effect with lower complexity.
Appoximation Formula for Estimation of Waiting-Time in Multiple-Channel Queuing Systems
DEFF Research Database (Denmark)
Maaløe, Erik
1973-01-01
The article deals with two approxiamation formulae for estimation of W, Waiting-times in a M/E/R queuing system.m First is shown how W for the multiple-channel system is approximately an Rth part of the mean waiting time in a single channel system. A second approximation applies the exact ratio...... for W(multi-channel/W(single channel) and M/M/1-systems to general M/Ek/R-systems. This is particularly illustrated in the case og M/M/R and M/D/R-systems as a general warning against general practices...
Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior
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Akinori Asahara
2015-04-01
Full Text Available To improve decision making, real-time population density must be known. However, calculating the point density of a huge dataset in real time is impractical in terms of processing time. Accordingly, a fast algorithm for estimating the distribution of the density of moving points is proposed. The algorithm, which is based on variational Bayesian estimation, takes a parametric approach to speed up the estimation process. Although the parametric approach has a drawback, that is the processes to be carried out on the server are very slow, the proposed algorithm overcomes the drawback by using the result of an estimation of an adjacent past density distribution.
Landemaine, Valentin; Cerdan, Olivier; Laignel, Benoit; Fournier, Matthieu; Copard, Yoann
2014-05-01
Sediment exports in rivers constitute the essential of materials transfer from the land surface to the ocean and contribute significantly to the transfer of nutrients, pesticides, heavy metals which can affect water quality. Such problems of water pollution are particularly present at the Norman loess plateaus because soil erosion is a frequent phenomena and mudslides are common. In this context, the quantification of sediment load, as well as the short and long term variability analysis are a key component for any sustainable management project of water resources. The quantification of sediment fluxes is based on turbidity, suspended sediment concentrations (SSC) and discharge measurements. These measurements must be made with sufficient high frequency for integrating temporal variability of SSC and flows. However, the cost of a high frequency monitoring limits their use at large scale. In France, discharges are monitored using daily frequency (Banque Hydro), while SSC are measured in monthly or bimonthly frequency under the national water quality survey system (RNB). With these low frequency measurements, an algorithm must be used to reconstruct SSC temporal variability and to estimate a sediment flux. Many estimation algorithms have been developed in recent decades, from the simplest to the most elaborate, but no consensus has been reached on the use of a particular algorithm because of the complexity of SSC-discharge relationship. In this study, the analysis focuses on eight Channel coastal watersheds and nine Seine watersheds in the downstream part. We have a several years of high-frequency measurements on nine watersheds with highly variable area (10 km² to 10,000 km²) and low-frequency measurements for all watersheds. From these data, we compared the statistical performance of eleven algorithms to estimate sediment fluxes conventionally used in the literature. These algorithms are: averaging estimator, ratio estimator, linear interpolation, rating curve
An importance sampling algorithm for estimating extremes of perpetuity sequences
Collamore, Jeffrey F.
2012-09-01
In a wide class of problems in insurance and financial mathematics, it is of interest to study the extremal events of a perpetuity sequence. This paper addresses the problem of numerically evaluating these rare event probabilities. Specifically, an importance sampling algorithm is described which is efficient in the sense that it exhibits bounded relative error, and which is optimal in an appropriate asymptotic sense. The main idea of the algorithm is to use a "dual" change of measure, which is employed to an associated Markov chain over a randomly-stopped time interval. The algorithm also makes use of the so-called forward sequences generated to the given stochastic recursion, together with elements of Markov chain theory.
RBF multiuser detector with channel estimation capability in a synchronous MC-CDMA system.
Ko, K; Choi, S; Kang, C; Hong, D
2001-01-01
The authors propose a multiuser detector with channel estimation capability using a radial basis function (RBF) network in a synchronous multicarrier-code division multiple access (MC-CDMA) system. The authors propose to connect an RBF network to the frequency domain to effectively utilize the frequency diversity. Simulations were performed over frequency-selective and multi-path fading channels. These simulations confirmed that the proposed receiver can be used both for the channel estimation and as a multi-user receiver, thus permitting an increase in the number of active users.
Intra-symbol frequency-domain averaging based channel estimation for coherent optical OFDM.
Liu, Xiang; Buchali, Fred
2008-12-22
We present an efficient channel estimation method for coherent optical OFDM (CO-OFDM) based on intra-symbol frequency-domain averaging (ISFA), and systematically study its robustness against transmission impairments such as optical noise, chromatic dispersion (CD), polarization-mode dispersion (PMD), polarization-dependent loss (PDL), and fiber nonlinearity. Numerical simulations are performed for a 112-Gb/s polarization-division multiplexed (PDM) CO-OFDM signal, and the ISFAbased channel estimation and the subsequent channel compensation are found to be highly robust against these transmission impairments in typical optical transport systems.
Sparse Channel Estimation Including the Impact of the Transceiver Filters with Application to OFDM
DEFF Research Database (Denmark)
Barbu, Oana-Elena; Pedersen, Niels Lovmand; Manchón, Carles Navarro
2014-01-01
Traditionally, the dictionary matrices used in sparse wireless channel estimation have been based on the discrete Fourier transform, following the assumption that the channel frequency response (CFR) can be approximated as a linear combination of a small number of multipath components, each one...... being contributed by a specific propagation path. In practical communication systems, however, the channel response experienced by the receiver includes additional effects to those induced by the propagation channel. This composite channel embodies, in particular, the impact of the transmit (shaping...... results obtained in an OFDM transmission scenario demonstrate the superior accuracy of a sparse estimator that uses our proposed dictionary rather than the classical Fourier dictionary, and its robustness against a mismatch in the assumed transmit filter characteristics....
Ahmed, Sajid
2017-05-12
The estimation of angular-location and range of a target is a joint optimization problem. In this work, to estimate these parameters, by meticulously evaluating the phase of the received samples, low complexity sequential and joint estimation algorithms are proposed. We use a single-input and multiple-output (SIMO) system and transmit frequency-modulated continuous-wave signal. In the proposed algorithm, it is shown that by ignoring very small value terms in the phase of the received samples, fast-Fourier-transform (FFT) and two-dimensional FFT can be exploited to estimate these parameters. Sequential estimation algorithm uses FFT and requires only one received snapshot to estimate the angular-location. Joint estimation algorithm uses two-dimensional FFT to estimate the angular-location and range of the target. Simulation results show that joint estimation algorithm yields better mean-squared-error (MSE) for the estimation of angular-location and much lower run-time compared to conventional MUltiple SIgnal Classification (MUSIC) algorithm.
Shanafield, Margaret; Niswonger, Richard G.; Prudic, David E.; Pohll, Greg; Susfalk, Richard; Panday, Sorab
2014-01-01
Infiltration along ephemeral channels plays an important role in groundwater recharge in arid regions. A model is presented for estimating spatial variability of seepage due to streambed heterogeneity along channels based on measurements of streamflow-front velocities in initially dry channels. The diffusion-wave approximation to the Saint-Venant equations, coupled with Philip's equation for infiltration, is connected to the groundwater model MODFLOW and is calibrated by adjusting the saturated hydraulic conductivity of the channel bed. The model is applied to portions of two large water delivery canals, which serve as proxies for natural ephemeral streams. Estimated seepage rates compare well with previously published values. Possible sources of error stem from uncertainty in Manning's roughness coefficients, soil hydraulic properties and channel geometry. Model performance would be most improved through more frequent longitudinal estimates of channel geometry and thalweg elevation, and with measurements of stream stage over time to constrain wave timing and shape. This model is a potentially valuable tool for estimating spatial variability in longitudinal seepage along intermittent and ephemeral channels over a wide range of bed slopes and the influence of seepage rates on groundwater levels.
Joint Angles and Mutual Coupling Estimation Algorithm for Bistatic MIMO Radar
Directory of Open Access Journals (Sweden)
Jianfeng Li
2012-01-01
Full Text Available We study the problem of angle estimation for a bistatic multiple-input multiple-output (MIMO radar with unknown mutual coupling and proposed a joint algorithm for angles and mutual coupling estimation with the characteristics of uniform linear arrays and subspaces exploitation. We primarily obtain an initial estimate of DOA and DOD, then employ the local one-dimensional searching to estimate exactly DOA and DOD, and finally evaluate the parameters of mutual coupling coefficients via the estimated angles. Exploiting twice of the one-dimensional local searching, our method has much lower computational cost than the algorithm in (Liu and Liao (2012, and automatically obtains the paired two-dimensional angle estimation. Slightly better performance for angle estimation has been achieved via our scheme in contrast to (Liu and Liao (2012, while the two methods indicate very close performance of mutual coupling estimation. The simulation results verify the algorithmic effectiveness of our scheme.
An importance sampling algorithm for estimating extremes of perpetuity sequences
DEFF Research Database (Denmark)
Collamore, Jeffrey F.
2012-01-01
In a wide class of problems in insurance and financial mathematics, it is of interest to study the extremal events of a perpetuity sequence. This paper addresses the problem of numerically evaluating these rare event probabilities. Specifically, an importance sampling algorithm is described which...
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.
Subjective and Objective Quality Assessment of Single-Channel Speech Separation Algorithms
DEFF Research Database (Denmark)
Mowlaee, Pejman; Saeidi, Rahim; Christensen, Mads Græsbøll
2012-01-01
are expected to carry on to other applications beyond ASR. In this paper, in addition to conventional speech quality metrics (PESQ and SNRloss), we also evaluate the separation systems output using different source separation metrics: blind source separation evaluation (BSS EVAL) and perceptual evaluation...... that PESQ and PEASS quality metrics predict well the subjective quality of separated signals obtained by the separation systems. From the results it is observed that the short-time objective intelligibility (STOI) measure predict the speech intelligibility results.......Previous studies on performance evaluation of single-channel speech separation (SCSS) algorithms mostly focused on automatic speech recognition (ASR) accuracy as their performance measure. Assessing the separated signals by different metrics other than this has the benefit that the results...
Preamble-based channel estimation in single-relay networks using FBMC/OQAM
Mavrokefalidis, Christos; Kofidis, Eleftherios; Rontogiannis, Athanasios A.; Theodoridis, Sergios
2014-12-01
Preamble-based channel estimation in filter bank-based multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM) has been extensively studied in the last few years, due to the many advantages this modulation scheme can offer over cyclic prefix (CP)-based orthogonal frequency division multiplexing (OFDM) and in view of the interesting challenges posed on the channel estimator by the interference effect inherent in such an FBMC system. In particular, preambles of short duration and of both the block ( full) and comb ( sparse) types were designed so as to minimize the channel estimation mean squared error (MSE) subject to a given transmit energy. In the light of the important role that relay-based cooperative networks are expected to play in future wireless communication systems, it is of interest to consider FBMC/OQAM, and in particular questions associated to preamble-based channel estimation, in such a context as well. The goal of this paper is to address these problems and come up with optimal solutions that extend existing results in a single relay-based cooperative network. Both low and medium frequency selective channels are considered. In addition to optimal preamble and estimator design, the equalization/detection task is studied, shedding light to a relay-generated interference effect and proposing a simple way to come over it. The reported simulation results corroborate the analysis and reveal interesting behavior with respect to channel frequency selectivity and signal-to-noise ratio.
Estimation of TOA based MUSIC algorithm and cross correlation algorithm of appropriate interval
Lin, Wei; Liu, Jun; Zhou, Yineng; Huang, Jiyan
2017-03-01
Localization of mobile station (MS) has now gained considerable attention due to its wide applications in military, environmental, health and commercial systems. Phrase angle and encode data of MSK system model are two critical parameters in time-of-arrival (TOA) localization technique; nevertheless, precise value of phrase angle and encode data are not easy to achieved in general. In order to meet the actual situation, we should consider the condition that phase angle and encode data is unknown. In this paper, a novel TOA localization method, which combine MUSIC algorithm and cross correlation algorithm in an appropriate interval, is proposed. Simulations show that the proposed method has better performance than music algorithm and cross correlation algorithm of the whole interval.
Rise Over Thermal Estimation Algorithm Optimization and Implementation
Irshad, Saba; Nepal, Purna Chandra
2013-01-01
The uplink load for the scheduling of Enhanced-Uplink (E-UL) channels determine the achievable data rate for Wideband Code Division Multiple Access (WCDMA) systems, therefore its accurate measurement carries a prime significance. The uplink load also known as Rise-over-Thermal (RoT), which is the quotient of the Received Total Wideband Power (RTWP) and the Thermal Noise Power floor. It is a major parameter which is calculated at each Transmission Time Interval (TTI) for maintaining cell cover...
Wang, Jun; Zhou, Bihua; Zhou, Shudao
2016-01-01
This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior.
Manifold absolute pressure estimation using neural network with hybrid training algorithm.
Muslim, Mohd Taufiq; Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli
2017-01-01
In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.
Manifold absolute pressure estimation using neural network with hybrid training algorithm.
Directory of Open Access Journals (Sweden)
Mohd Taufiq Muslim
Full Text Available In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM algorithm, Bayesian Regularization (BR algorithm and Particle Swarm Optimization (PSO algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS. The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.
Fast adaptive particle spectrum fitting algorithm based on moment-estimated initial parameters.
Shi, Rui; Tuo, Xianguo; Zheng, Honglong; Li, Huailiang; Xu, Yangyang; Wang, Qibiao; Deng, Chao
2017-11-01
An algorithm based on moment estimation is presented to determine the initial parameters of the particle spectrum peak shape function for the iteration fitting procedure. The algorithm calculates the mean value, variance, and third-order central moment by using the spectrum peak data, solves the parameters of the fitting function, and then provides them as the initial values to the Levenberg-Marquardt algorithm to ensure convergence and optimized fitting. The effectiveness of the proposed algorithm was tested by gamma and alpha spectra. The algorithm can be used in automated peak curve fitting and spectral analysis. Copyright © 2017 Elsevier Ltd. All rights reserved.
Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar
2011-12-01
This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.
Analysis of Inverse Crosstalk Channel Estimation Using SNR Feedback
Whiting, P.A.; Kramer, G.; Nuzman, C.J.; Ashikhmin, A.; van Wijngaarden, A.J.; Zivkovic, Miroslav
Digital subscriber line (DSL) data rates for short loops are typically limited by crosstalk between adjacent lines rather than by background noise. Precoding can reduce crosstalk in the downstream from the access node to the customer premises equipment significantly if an accurate estimate of the
A FPC-ROOT Algorithm for 2D-DOA Estimation in Sparse Array
Directory of Open Access Journals (Sweden)
Wenhao Zeng
2016-01-01
Full Text Available To improve the performance of two-dimensional direction-of-arrival (2D DOA estimation in sparse array, this paper presents a Fixed Point Continuation Polynomial Roots (FPC-ROOT algorithm. Firstly, a signal model for DOA estimation is established based on matrix completion and it can be proved that the proposed model meets Null Space Property (NSP. Secondly, left and right singular vectors of received signals matrix are achieved using the matrix completion algorithm. Finally, 2D DOA estimation can be acquired through solving the polynomial roots. The proposed algorithm can achieve high accuracy of 2D DOA estimation in sparse array, without solving autocorrelation matrix of received signals and scanning of two-dimensional spectral peak. Besides, it decreases the number of antennas and lowers computational complexity and meanwhile avoids the angle ambiguity problem. Computer simulations demonstrate that the proposed FPC-ROOT algorithm can obtain the 2D DOA estimation precisely in sparse array.
Ogawa, Takahiro; Haseyama, Miki
2013-03-01
A missing texture reconstruction method based on an error reduction (ER) algorithm, including a novel estimation scheme of Fourier transform magnitudes is presented in this brief. In our method, Fourier transform magnitude is estimated for a target patch including missing areas, and the missing intensities are estimated by retrieving its phase based on the ER algorithm. Specifically, by monitoring errors converged in the ER algorithm, known patches whose Fourier transform magnitudes are similar to that of the target patch are selected from the target image. In the second approach, the Fourier transform magnitude of the target patch is estimated from those of the selected known patches and their corresponding errors. Consequently, by using the ER algorithm, we can estimate both the Fourier transform magnitudes and phases to reconstruct the missing areas.
Nagy, Ivan
2017-01-01
This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a scheme for constructing the estimation algorithm for a mixture of components modeled by distributions with reproducible statistics. It offers the recursive estimation of dynamic mixtures, which are free of iterative processes and close to analytical solutions as much as possible. In addition, these methods can be used online and simultaneously perform learning, which improves their efficiency during estimation. The book includes detailed program codes for solving the presented theoretical tasks. Codes are implemented in the open source platform for engineering computations. The program codes given serve to illustrate the theory and demonstrate the work of the included algorithms.
Yao, Yunjun; Liang, Shunlin; Yu, Jian; Zhao, Shaohua; Lin, Yi; Jia, Kun; Zhang, Xiaotong; Cheng, Jie; Xie, Xianhong; Sun, Liang; Wang, Xuanyu; Zhang, Lilin
2017-04-01
Accurate estimates of terrestrial latent heat of evaporation (LE) for different biomes are essential to assess energy, water and carbon cycles. Different satellite- based Priestley-Taylor (PT) algorithms have been developed to estimate LE in different biomes. However, there are still large uncertainties in LE estimates for different PT algorithms. In this study, we evaluated differences in estimating terrestrial water flux in different biomes from three satellite-based PT algorithms using ground-observed data from eight eddy covariance (EC) flux towers of China. The results reveal that large differences in daily LE estimates exist based on EC measurements using three PT algorithms among eight ecosystem types. At the forest (CBS) site, all algorithms demonstrate high performance with low root mean square error (RMSE) (less than 16 W/m2) and high squared correlation coefficient (R2) (more than 0.9). At the village (HHV) site, the ATI-PT algorithm has the lowest RMSE (13.9 W/m2), with bias of 2.7 W/m2 and R2 of 0.66. At the irrigated crop (HHM) site, almost all models algorithms underestimate LE, indicating these algorithms may not capture wet soil evaporation by parameterization of the soil moisture. In contrast, the SM-PT algorithm shows high values of R2 (comparable to those of ATI-PT and VPD-PT) at most other (grass, wetland, desert and Gobi) biomes. There are no obvious differences in seasonal LE estimation using MODIS NDVI and LAI at most sites. However, all meteorological or satellite-based water-related parameters used in the PT algorithm have uncertainties for optimizing water constraints. This analysis highlights the need to improve PT algorithms with regard to water constraints.
Estimate-Merge-Technique-based algorithms to track an underwater ...
Indian Academy of Sciences (India)
D V A N Ravi Kumar
2017-07-04
Jul 4, 2017 ... These novel methods have an advantage of less root mean square estimation error in position and velocity compared with the EKF and .... This technique has the added advantage of causing less numerical difficulties. ... such a way that the mean square error in the estimation is minimum, whereas the MLE ...
Directory of Open Access Journals (Sweden)
Tingting Jin
2017-04-01
Full Text Available Multichannel synthetic aperture radar (SAR is a significant breakthrough to the inherent limitation between high-resolution and wide-swath (HRWS compared with conventional SAR. Moving target indication (MTI is an important application of spaceborne HRWS SAR systems. In contrast to previous studies of SAR MTI, the HRWS SAR mainly faces the problem of under-sampled data of each channel, causing single-channel imaging and processing to be infeasible. In this study, the estimation of velocity is equivalent to the estimation of the cone angle according to their relationship. The maximum likelihood (ML based algorithm is proposed to estimate the radial velocity in the existence of Doppler ambiguities. After that, the signal reconstruction and compensation for the phase offset caused by radial velocity are processed for a moving target. Finally, the traditional imaging algorithm is applied to obtain a focused moving target image. Experiments are conducted to evaluate the accuracy and effectiveness of the estimator under different signal-to-noise ratios (SNR. Furthermore, the performance is analyzed with respect to the motion ship that experiences interference due to different distributions of sea clutter. The results verify that the proposed algorithm is accurate and efficient with low computational complexity. This paper aims at providing a solution to the velocity estimation problem in the future HRWS SAR systems with multiple receive channels.
Analysis of the Command and Control Segment (CCS) attitude estimation algorithm
Stockwell, Catherine
1993-01-01
This paper categorizes the qualitative behavior of the Command and Control Segment (CCS) differential correction algorithm as applied to attitude estimation using simultaneous spin axis sun angle and Earth cord length measurements. The categories of interest are the domains of convergence, divergence, and their boundaries. Three series of plots are discussed that show the dependence of the estimation algorithm on the vehicle radius, the sun/Earth angle, and the spacecraft attitude. Common qualitative dynamics to all three series are tabulated and discussed. Out-of-limits conditions for the estimation algorithm are identified and discussed.
A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles
Directory of Open Access Journals (Sweden)
Zheng Chen
2016-09-01
Full Text Available This paper focuses on state of charge (SOC estimation for the battery packs of electric vehicles (EVs. By modeling a battery based on the equivalent circuit model (ECM, the adaptive extended Kalman filter (AEKF method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.
A Kalman-based Fundamental Frequency Estimation Algorithm
DEFF Research Database (Denmark)
Shi, Liming; Nielsen, Jesper Kjær; Jensen, Jesper Rindom
2017-01-01
Fundamental frequency estimation is an important task in speech and audio analysis. Harmonic model-based methods typically have superior estimation accuracy. However, such methods usually as- sume that the fundamental frequency and amplitudes are station- ary over a short time frame. In this paper...... model and formulated as a compact nonlinear matrix form, which is further used to derive an extended Kalman filter. Detailed and continuous fundamental frequency and ampli- tude estimates for speech, the sustained vowel /a/ and solo musical tones with vibrato are demonstrated....
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.
Directory of Open Access Journals (Sweden)
E. E. Miandoab
2016-06-01
Full Text Available The inherent uncertainty to factors such as technology and creativity in evolving software development is a major challenge for the management of software projects. To address these challenges the project manager, in addition to examining the project progress, may cope with problems such as increased operating costs, lack of resources, and lack of implementation of key activities to better plan the project. Software Cost Estimation (SCE models do not fully cover new approaches. And this lack of coverage is causing problems in the consumer and producer ends. In order to avoid these problems, many methods have already been proposed. Model-based methods are the most familiar solving technique. But it should be noted that model-based methods use a single formula and constant values, and these methods are not responsive to the increasing developments in the field of software engineering. Accordingly, researchers have tried to solve the problem of SCE using machine learning algorithms, data mining algorithms, and artificial neural networks. In this paper, a hybrid algorithm that combines COA-Cuckoo optimization and K-Nearest Neighbors (KNN algorithms is used. The so-called composition algorithm runs on six different data sets and is evaluated based on eight evaluation criteria. The results show an improved accuracy of estimated cost.
Directory of Open Access Journals (Sweden)
Małgorzata Stramska
2013-02-01
Full Text Available The quasi-synoptic view available from satellites has been broadly used in recent years to observe in near-real time the large-scale dynamics of marine ecosystems and to estimate primary productivity in the world ocean. However, the standard global NASA ocean colour algorithms generally do not produce good results in the Baltic Sea. In this paper, we compare the ability of seven algorithms to estimate depth-integrated daily primary production (PP, mg C m-2 in the Baltic Sea. All the algorithms use surface chlorophyll concentration, sea surface temperature, photosynthetic available radiation, latitude, longitude and day of the year as input data. Algorithm-derived PP is then compared with PP estimates obtained from 14C uptake measurements. The results indicate that the best agreement between the modelled and measured PP in the Baltic Sea is obtained with the DESAMBEM algorithm. This result supports the notion that a regional approach should be used in the interpretation of ocean colour satellite data in the Baltic Sea.
Renbiao Wu; Wenyi Wang
2013-01-01
DOA (Direction of Arrival) estimation is a major problem in array signal processing applications. Recently, compressive sensing algorithms, including convex relaxation algorithms and greedy algorithms, have been recognized as a kind of novel DOA estimation algorithm. However, the success of these algorithms is limited by the RIP (Restricted Isometry Property) condition or the mutual coherence of measurement matrix. In the DOA estimation problem, the columns of measurement matrix are steering ...
Directory of Open Access Journals (Sweden)
Farnoosh Talaei
2017-12-01
Full Text Available This paper presents a reduced rank channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM high speed railway (HSR systems. Conventional interpolation based channel estimators require high pilot load for robust estimation of the rapidly time varying frequency-selective MIMO-OFDM HSR channel. To relax the high pilot overhead requirement, we take advantage of the channel’s restriction to low dimensional subspaces due to the time, frequency and spatial correlation and propose a low complexity linear minimum mean square error (LMMSE channel estimator. The channel estimator utilizes a four-dimensional (4D basis expansion channel model obtained from band-limited generalized discrete prolate spheroidal (GDPS sequences. Simulation results validate that the mean square estimation error of the proposed estimator is smaller than that of the conventional interpolation based least square (LS estimator and the performance is robust to different delay, Doppler and angular spreads.
Minimum Mean-Square Error Single-Channel Signal Estimation
DEFF Research Database (Denmark)
Beierholm, Thomas
2008-01-01
are expressed and in the way the estimator is approximated. The starting point of the first method is prior probability density functions for both signal and noise and it is assumed that their Laplace transforms (moment generating functions) are available. The corresponding posterior mean integral that defines...... and a saddlepoint approximation based on two coalescing saddlepoints is derived. For practical reasons the method is limited to low dimensional problems and the results are not easily extended to the multivariate case. In the second approach signal and noise are specified by generative models and approximate...
National Aeronautics and Space Administration — Recent work on distributed multi-spacecraft systems has resulted in a number of architectures and algorithms for accurate estimation of spacecraft and formation...
Performance of Pilot Aided Channel Estimation Technique in 2D Spreading Based Systems
Directory of Open Access Journals (Sweden)
J. Blumenstein
2010-12-01
Full Text Available It has recently been observed that the combination of the OFDM (Orthogonal Frequency Division Multiplex and the CDMA (Code Division Multiple Access can achieve notably lower BER (Bit Error Rate performance in comparison with OFDM itself. The paper reports on the actual topic of the efficient channel estimation in 2D spreading based systems e.q. VSF-OFCDM (Variable Spreading Factor - Orthogonal Frequency Multiple Acces. The different methods for acquisition of channel state information from pilot carriers are used. The simulations are made for different ETSI channel models.
EFFICIENT BLOCK MATCHING ALGORITHMS FOR MOTION ESTIMATION IN H.264/AVC
Directory of Open Access Journals (Sweden)
P. Muralidhar
2015-02-01
Full Text Available In Scalable Video Coding (SVC, motion estimation and inter-layer prediction play an important role in elimination of temporal and spatial redundancies between consecutive layers. This paper evaluates the performance of widely accepted block matching algorithms used in various video compression standards, with emphasis on the performance of the algorithms for a didactic scalable video codec. Many different implementations of Fast Motion Estimation Algorithms have been proposed to reduce motion estimation complexity. The block matching algorithms have been analyzed with emphasis on Peak Signal to Noise Ratio (PSNR and computations using MATLAB. In addition to the above comparisons, a survey has been done on Spiral Search Motion Estimation Algorithms for Video Coding. A New Modified Spiral Search (NMSS motion estimation algorithm has been proposed with lower computational complexity. The proposed algorithm achieves 72% reduction in computation with a minimal (<1dB reduction in PSNR. A brief introduction to the entire flow of video compression H.264/SVC is also presented in this paper.
A FPC-ROOT Algorithm for 2D-DOA Estimation in Sparse Array
Wenhao Zeng; Hongtao Li; Xiaohua Zhu; Chaoyu Wang
2016-01-01
To improve the performance of two-dimensional direction-of-arrival (2D DOA) estimation in sparse array, this paper presents a Fixed Point Continuation Polynomial Roots (FPC-ROOT) algorithm. Firstly, a signal model for DOA estimation is established based on matrix completion and it can be proved that the proposed model meets Null Space Property (NSP). Secondly, left and right singular vectors of received signals matrix are achieved using the matrix completion algorithm. Finally, 2D DOA estimat...
Preamble and pilot symbol design for channel estimation in OFDM systems with null subcarriers
Directory of Open Access Journals (Sweden)
Ohno Shuichi
2011-01-01
Full Text Available Abstract In this article, design of preamble for channel estimation and pilot symbols for pilot-assisted channel estimation in orthogonal frequency division multiplexing system with null subcarriers is studied. Both the preambles and pilot symbols are designed to minimize the l 2 or the l ∞ norm of the channel estimate mean-squared errors (MSE in frequency-selective environments. We use convex optimization technique to find optimal power distribution to the preamble by casting the MSE minimization problem into a semidefinite programming problem. Then, using the designed optimal preamble as an initial value, we iteratively select the placement and optimally distribute power to the selected pilot symbols. Design examples consistent with IEEE 802.11a as well as IEEE 802.16e are provided to illustrate the superior performance of our proposed method over the equi-spaced equi-powered pilot symbols and the partially equi-spaced pilot symbols.
SFO compensation by pilot-aided channel estimation for real-time DDO-OFDM system
Deng, Rui; He, Jing; Chen, Ming; Chen, Lin
2015-11-01
In this paper, we experimentally demonstrated a pilot-aided and linear interpolated channel estimation technique in the real-time direct-detection optical orthogonal frequency division multiplexing (DDO-OFDM) system using a cost-effective directly modulated laser (DML). It has been verified that the pilot-aided and linear interpolated channel estimation technique can help to compensate the sampling frequency offset (SFO) effect. The experimental results show that, based on the pilot-aided and linear interpolated channel estimation technique, even at a SFO of 170 ppm, a 16-QAM-OFDM signal can be successfully transmitted over 100-km SSMF under the hard-decision forward-error-correction (HD-FEC) threshold with a bit error rate of 3.8×10-3. And the effect of up to ~25 ppm SFO can be negligible.
Capacity of MIMO-OFDM with Pilot-Aided Channel Estimation
Directory of Open Access Journals (Sweden)
Cosovic Ivan
2007-01-01
Full Text Available An analytical framework is established to dimension the pilot grid for MIMO-OFDM operating in time-variant frequency selective channels. The optimum placement of pilot symbols in terms of overhead and power allocation is identified that maximizes the training-based capacity for MIMO-OFDM schemes without channel knowledge at the transmitter. For pilot-aided channel estimation (PACE with perfect interpolation, we show that the maximum capacity is achieved by placing pilots with maximum equidistant spacing given by the sampling theorem, if pilots are appropriately boosted. Allowing for realizable and possibly suboptimum estimators where interpolation is not perfect, we present a semianalytical method which finds the best pilot allocation strategy for the particular estimator.
Capacity of MIMO-OFDM with Pilot-Aided Channel Estimation
Directory of Open Access Journals (Sweden)
Gunther Auer
2008-03-01
Full Text Available An analytical framework is established to dimension the pilot grid for MIMO-OFDM operating in time-variant frequency selective channels. The optimum placement of pilot symbols in terms of overhead and power allocation is identified that maximizes the training-based capacity for MIMO-OFDM schemes without channel knowledge at the transmitter. For pilot-aided channel estimation (PACE with perfect interpolation, we show that the maximum capacity is achieved by placing pilots with maximum equidistant spacing given by the sampling theorem, if pilots are appropriately boosted. Allowing for realizable and possibly suboptimum estimators where interpolation is not perfect, we present a semianalytical method which finds the best pilot allocation strategy for the particular estimator.
A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation
DEFF Research Database (Denmark)
Hansen, Thomas Lundgaard; Badiu, Mihai Alin; Fleury, Bernard Henri
2014-01-01
This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous...
Model Order Reduction Algorithm for Estimating the Absorption Spectrum
Energy Technology Data Exchange (ETDEWEB)
Van Beeumen, Roel [Computational; Williams-Young, David B. [Department; Kasper, Joseph M. [Department; Yang, Chao [Computational; Ng, Esmond G. [Computational; Li, Xiaosong [Department
2017-09-20
The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propagator’s eigenspectrum for medium-to-large sized systems, traditional approaches based on the partial diagonalization of the propagator often encounter oscillatory and stagnating convergence. Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location. This generality comes at a seemingly high computational cost associated with solving a large linear system for each spectral shift in some discretization of the spectral domain of interest. In this work, we present a novel, adaptive solution to this high computational overhead based on model order reduction techniques via interpolation. Model order reduction reduces the computational complexity of mathematical models and is ubiquitous in the simulation of dynamical systems and control theory. The efficiency and effectiveness of the proposed algorithm in the ab initio prediction of X-ray absorption spectra is demonstrated using a test set of challenging water clusters which are spectrally dense in the neighborhood of the oxygen K-edge. On the basis of a single, user defined tolerance we automatically determine the order of the reduced models and approximate the absorption spectrum up to the given tolerance. We also illustrate that, for the systems studied, the automatically determined model order increases logarithmically with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window. Furthermore, we observed that the computational cost of the proposed algorithm only scales quadratically with respect to the
A survey on OFDM channel estimation techniques based on denoising strategies
Directory of Open Access Journals (Sweden)
Pallaviram Sure
2017-04-01
Full Text Available Channel estimation forms the heart of any orthogonal frequency division multiplexing (OFDM based wireless communication receiver. Frequency domain pilot aided channel estimation techniques are either least squares (LS based or minimum mean square error (MMSE based. LS based techniques are computationally less complex. Unlike MMSE ones, they do not require a priori knowledge of channel statistics (KCS. However, the mean square error (MSE performance of the channel estimator incorporating MMSE based techniques is better compared to that obtained with the incorporation of LS based techniques. To enhance the MSE performance using LS based techniques, a variety of denoising strategies have been developed in the literature, which are applied on the LS estimated channel impulse response (CIR. The advantage of denoising threshold based LS techniques is that, they do not require KCS but still render near optimal MMSE performance similar to MMSE based techniques. In this paper, a detailed survey on various existing denoising strategies, with a comparative discussion of these strategies is presented.
Directory of Open Access Journals (Sweden)
Sonia Aïssa
2008-05-01
Full Text Available This paper investigates the effects of channel estimation error at the receiver on the achievable rate of distributed space-time block coded transmission. We consider that multiple transmitters cooperate to send the signal to the receiver and derive lower and upper bounds on the mutual information of distributed space-time block codes (D-STBCs when the channel gains and channel estimation error variances pertaining to different transmitter-receiver links are unequal. Then, assessing the gap between these two bounds, we provide a limiting value that upper bounds the latter at any input transmit powers, and also show that the gap is minimum if the receiver can estimate the channels of different transmitters with the same accuracy. We further investigate positioning the receiving node such that the mutual information bounds of D-STBCs and their robustness to the variations of the subchannel gains are maximum, as long as the summation of these gains is constant. Furthermore, we derive the optimum power transmission strategy to achieve the outage capacity lower bound of D-STBCs under arbitrary numbers of transmit and receive antennas, and provide closed-form expressions for this capacity metric. Numerical simulations are conducted to corroborate our analysis and quantify the effects of imperfect channel estimation.
Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems
Li, Yongzhi; Tao, Cheng; Seco-Granados, Gonzalo; Mezghani, Amine; Swindlehurst, A. Lee; Liu, Liu
2017-08-01
This paper considers channel estimation and system performance for the uplink of a single-cell massive multiple-input multiple-output (MIMO) system. Each receive antenna of the base station (BS) is assumed to be equipped with a pair of one-bit analog-to-digital converters (ADCs) to quantize the real and imaginary part of the received signal. We first propose an approach for channel estimation that is applicable for both flat and frequency-selective fading, based on the Bussgang decomposition that reformulates the nonlinear quantizer as a linear functionwith identical first- and second-order statistics. The resulting channel estimator outperforms previously proposed approaches across all SNRs. We then derive closed-form expressions for the achievable rate in flat fading channels assuming low SNR and a large number of users for the maximal ratio and zero forcing receivers that takes channel estimation error due to both noise and one-bit quantization into account. The closed-form expressions in turn allow us to obtain insight into important system design issues such as optimal resource allocation, maximal sum spectral efficiency, overall energy efficiency, and number of antennas. Numerical results are presented to verify our analytical results and demonstrate the benefit of optimizing system performance accordingly.
Lichtner, Gregor; Golebiewski, Anna; Schneider, Martin H; von Dincklage, Falk
2015-05-22
The nociceptive flexion reflex (NFR) is a widely used tool to investigate spinal nociception for scientific and diagnostic purposes, but its clinical use is currently limited due to the painful measurement procedure, especially restricting its applicability for patients suffering from chronic pain disorders. Here we introduce a less painful algorithm to assess the NFR threshold. Application of this new algorithm leads to a reduction of subjective pain ratings by over 30% compared to the standard algorithm. We show that the reflex threshold estimates resulting from application of the new algorithm can be used interchangeably with those of the standard algorithm after adjusting for the constant difference between the algorithms. Furthermore, we show that the new algorithm can be applied at shorter interstimulus intervals than are commonly used with the standard algorithm, since reflex threshold values remain unchanged and no habituation effects occur when reducing the interstimulus interval for the new algorithm down to 3s. Finally we demonstrate the utility of the new algorithm to investigate the modulation of nociception through different states of attention. Taken together, the here presented new algorithm could increase the utility of the NFR for investigation of nociception in subjects who were previously not able to endure the measurement procedure, such as chronic pain patients. Copyright © 2015 Elsevier B.V. All rights reserved.
Valderrama, Joaquin T.; de la Torre, Angel; Van Dun, Bram
2018-02-01
Objective. Artifact reduction in electroencephalogram (EEG) signals is usually necessary to carry out data analysis appropriately. Despite the large amount of denoising techniques available with a multichannel setup, there is a lack of efficient algorithms that remove (not only detect) blink-artifacts from a single channel EEG, which is of interest in many clinical and research applications. This paper describes and evaluates the iterative template matching and suppression (ITMS), a new method proposed for detecting and suppressing the artifact associated with the blink activity from a single channel EEG. Approach. The approach of ITMS consists of (a) an iterative process in which blink-events are detected and the blink-artifact waveform of the analyzed subject is estimated, (b) generation of a signal modeling the blink-artifact, and (c) suppression of this signal from the raw EEG. The performance of ITMS is compared with the multi-window summation of derivatives within a window (MSDW) technique using both synthesized and real EEG data. Main results. Results suggest that ITMS presents an adequate performance in detecting and suppressing blink-artifacts from a single channel EEG. When applied to the analysis of cortical auditory evoked potentials (CAEPs), ITMS provides a significant quality improvement in the resulting responses, i.e. in a cohort of 30 adults, the mean correlation coefficient improved from 0.37 to 0.65 when the blink-artifacts were detected and suppressed by ITMS. Significance. ITMS is an efficient solution to the problem of denoising blink-artifacts in single-channel EEG applications, both in clinical and research fields. The proposed ITMS algorithm is stable; automatic, since it does not require human intervention; low-invasive, because the EEG segments not contaminated by blink-artifacts remain unaltered; and easy to implement, as can be observed in the Matlab script implemeting the algorithm provided as supporting material.
Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms.
Wang, Huiwei; Liao, Xiaofeng; Wang, Zidong; Huang, Tingwen; Chen, Guo
2016-01-01
In this paper, we present an asynchronous algorithm to estimate the unknown parameter under an unreliable network which allows new sensors to join and old sensors to leave, and can tolerate link failures. Each sensor has access to partially informative measurements when it is awakened. In addition, the proposed algorithm can avoid the interference among messages and effectively reduce the accumulated measurement and quantization errors. Based on the theory of stochastic approximation, we prove that our proposed algorithm almost surely converges to the unknown parameter. Finally, we present a numerical example to assess the performance and the communication cost of the algorithm. Copyright © 2015 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Iman Yousefi
2015-01-01
Full Text Available This paper presents parameter estimation of Permanent Magnet Synchronous Motor (PMSM using a combinatorial algorithm. Nonlinear fourth-order space state model of PMSM is selected. This model is rewritten to the linear regression form without linearization. Noise is imposed to the system in order to provide a real condition, and then combinatorial Orthogonal Projection Algorithm and Recursive Least Squares (OPA&RLS method is applied in the linear regression form to the system. Results of this method are compared to the Orthogonal Projection Algorithm (OPA and Recursive Least Squares (RLS methods to validate the feasibility of the proposed method. Simulation results validate the efficacy of the proposed algorithm.
Optimization Algorithms for Catching Data Manipulators in Power System Estimation Loops
Liao, Mang; Chakrabortty, Aranya
2016-01-01
In this paper we develop a set of algorithms that can detect the identities of malicious data-manipulators in distributed optimization loops for estimating oscillation modes in large power system models. The estimation is posed in terms of a consensus problem among multiple local estimators that jointly solve for the characteristic polynomial of the network model. If any of these local estimates are compromised by a malicious attacker, resulting in an incorrect value of the consensus variable...
Genetic Algorithms for a Parameter Estimation of a Fermentation Process Model: A Comparison
Directory of Open Access Journals (Sweden)
Olympia Roeva
2005-12-01
Full Text Available In this paper the problem of a parameter estimation using genetic algorithms is examined. A case study considering the estimation of 6 parameters of a nonlinear dynamic model of E. coli fermentation is presented as a test problem. The parameter estimation problem is stated as a nonlinear programming problem subject to nonlinear differential-algebraic constraints. This problem is known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based local optimization methods fail to arrive satisfied solutions. To overcome their limitations, the use of different genetic algorithms as stochastic global optimization methods is explored. These algorithms are proved to be very suitable for the optimization of highly non-linear problems with many variables. Genetic algorithms can guarantee global optimality and robustness. These facts make them advantageous in use for parameter identification of fermentation models. A comparison between simple, modified and multi-population genetic algorithms is presented. The best result is obtained using the modified genetic algorithm. The considered algorithms converged very closely to the cost value but the modified algorithm is in times faster than other two.
Experimental Results of Novel DoA Estimation Algorithms for Compact Reconfigurable Antennas
Directory of Open Access Journals (Sweden)
Henna Paaso
2017-01-01
Full Text Available Reconfigurable antenna systems have gained much attention for potential use in the next generation wireless systems. However, conventional direction-of-arrival (DoA estimation algorithms for antenna arrays cannot be used directly in reconfigurable antennas due to different design of the antennas. In this paper, we present an adjacent pattern power ratio (APPR algorithm for two-port composite right/left-handed (CRLH reconfigurable leaky-wave antennas (LWAs. Additionally, we compare the performances of the APPR algorithm and LWA-based MUSIC algorithms. We study how the computational complexity and the performance of the algorithms depend on number of selected radiation patterns. In addition, we evaluate the performance of the APPR and MUSIC algorithms with numerical simulations as well as with real world indoor measurements having both line-of-sight and non-line-of-sight components. Our performance evaluations show that the DoA estimates are in a considerably good agreement with the real DoAs, especially with the APPR algorithm. In summary, the APPR and MUSIC algorithms for DoA estimation along with the planar and compact LWA layout can be a valuable solution to enhance the performance of the wireless communication in the next generation systems.
A blind algorithm for reverberation-time estimation using subband decomposition of speech signals.
Prego, Thiago de M; de Lima, Amaro A; Netto, Sergio L; Lee, Bowon; Said, Amir; Schafer, Ronald W; Kalker, Ton
2012-04-01
An algorithm for blind estimation of reverberation time (RT) in speech signals is proposed. Analysis is restricted to the free-decaying regions of the signal, where the reverberation effect dominates, yielding a more accurate RT estimate at a reduced computational cost. A spectral decomposition is performed on the reverberant signal and partial RT estimates are determined in all signal subbands, providing more data to the statistical-analysis stage of the algorithm, which yields the final RT estimate. Algorithm performance is assessed using two distinct speech databases, achieving 91% and 97% correlation with the RTs measured by a standard nonblind method, indicating that the proposed method blindly estimates the RT in a reliable and consistent manner.
Beamspace Unitary ESPRIT Algorithm for Angle Estimation in Bistatic MIMO Radar
Directory of Open Access Journals (Sweden)
Dang Xiaofang
2015-01-01
Full Text Available The beamspace unitary ESPRIT (B-UESPRIT algorithm for estimating the joint direction of arrival (DOA and the direction of departure (DOD in bistatic multiple-input multiple-output (MIMO radar is proposed. The conjugate centrosymmetrized DFT matrix is utilized to retain the rotational invariance structure in the beamspace transformation for both the receiving array and the transmitting array. Then the real-valued unitary ESPRIT algorithm is used to estimate DODs and DOAs which have been paired automatically. The proposed algorithm does not require peak searching, presents low complexity, and provides a significant better performance compared to some existing methods, such as the element-space ESPRIT (E-ESPRIT algorithm and the beamspace ESPRIT (B-ESPRIT algorithm for bistatic MIMO radar. Simulation results are conducted to show these conclusions.
Directory of Open Access Journals (Sweden)
Nenggen Ding
2010-01-01
Full Text Available A recursive least square (RLS algorithm for estimation of vehicle sideslip angle and road friction coefficient is proposed. The algorithm uses the information from sensors onboard vehicle and control inputs from the control logic and is intended to provide the essential information for active safety systems such as active steering, direct yaw moment control, or their combination. Based on a simple two-degree-of-freedom (DOF vehicle model, the algorithm minimizes the squared errors between estimated lateral acceleration and yaw acceleration of the vehicle and their measured values. The algorithm also utilizes available control inputs such as active steering angle and wheel brake torques. The proposed algorithm is evaluated using an 8-DOF full vehicle simulation model including all essential nonlinearities and an integrated active front steering and direct yaw moment control on dry and slippery roads.
Renewable Energy Power Generation Estimation Using Consensus Algorithm
Ahmad, Jehanzeb; Najm-ul-Islam, M.; Ahmed, Salman
2017-08-01
At the small consumer level, Photo Voltaic (PV) panel based grid tied systems are the most common form of Distributed Energy Resources (DER). Unlike wind which is suitable for only selected locations, PV panels can generate electricity almost anywhere. Pakistan is currently one of the most energy deficient countries in the world. In order to mitigate this shortage the Government has recently announced a policy of net-metering for residential consumers. After wide spread adoption of DERs, one of the issues that will be faced by load management centers would be accurate estimate of the amount of electricity being injected in the grid at any given time through these DERs. This becomes a critical issue once the penetration of DER increases beyond a certain limit. Grid stability and management of harmonics becomes an important consideration where electricity is being injected at the distribution level and through solid state controllers instead of rotating machinery. This paper presents a solution using graph theoretic methods for the estimation of total electricity being injected in the grid in a wide spread geographical area. An agent based consensus approach for distributed computation is being used to provide an estimate under varying generation conditions.
Simple and Efficient Algorithm for Improving the MDL Estimator of the Number of Sources
Directory of Open Access Journals (Sweden)
Dayan A. Guimarães
2014-10-01
Full Text Available We propose a simple algorithm for improving the MDL (minimum description length estimator of the number of sources of signals impinging on multiple sensors. The algorithm is based on the norms of vectors whose elements are the normalized and nonlinearly scaled eigenvalues of the received signal covariance matrix and the corresponding normalized indexes. Such norms are used to discriminate the largest eigenvalues from the remaining ones, thus allowing for the estimation of the number of sources. The MDL estimate is used as the input data of the algorithm. Numerical results unveil that the so-called norm-based improved MDL (iMDL algorithm can achieve performances that are better than those achieved by the MDL estimator alone. Comparisons are also made with the well-known AIC (Akaike information criterion estimator and with a recently-proposed estimator based on the random matrix theory (RMT. It is shown that our algorithm can also outperform the AIC and the RMT-based estimator in some situations.
Directory of Open Access Journals (Sweden)
Chaowei Wang
2013-01-01
Full Text Available This paper proposes an advanced dynamic framed-slotted ALOHA algorithm based on Bayesian estimation and probability response (BE-PDFSA to improve the performance of radio frequency identification (RFID system. The Bayesian estimation is introduced to improve the accuracy of the estimation algorithm for lacking a large number of observations in one query. The probability response is used to adjust responsive probability of the unrecognized tags to make the responsive tag number equal to the frame length. In this way, we can solve the problem of high collision rate with the increase of tag number and improve the throughput of the whole system. From the simulation results, we can see that the algorithm we proposed can greatly improve the stability of RFID system compared with DFSA and other commonly used algorithms.
Motion Estimation Using the Firefly Algorithm in Ultrasonic Image Sequence of Soft Tissue
Directory of Open Access Journals (Sweden)
Chih-Feng Chao
2015-01-01
Full Text Available Ultrasonic image sequence of the soft tissue is widely used in disease diagnosis; however, the speckle noises usually influenced the image quality. These images usually have a low signal-to-noise ratio presentation. The phenomenon gives rise to traditional motion estimation algorithms that are not suitable to measure the motion vectors. In this paper, a new motion estimation algorithm is developed for assessing the velocity field of soft tissue in a sequence of ultrasonic B-mode images. The proposed iterative firefly algorithm (IFA searches for few candidate points to obtain the optimal motion vector, and then compares it to the traditional iterative full search algorithm (IFSA via a series of experiments of in vivo ultrasonic image sequences. The experimental results show that the IFA can assess the vector with better efficiency and almost equal estimation quality compared to the traditional IFSA method.
Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems: Preprint
Energy Technology Data Exchange (ETDEWEB)
Wang, Dexin; Yang, Liuqing; Florita, Anthony; Alam, S.M. Shafiul; Elgindy, Tarek; Hodge, Bri-Mathias
2016-08-01
The deregulation of the power system and the incorporation of generation from renewable energy sources recessitates faster state estimation in the smart grid. Distributed state estimation (DSE) has become a promising and scalable solution to this urgent demand. In this paper, we investigate the regionalization algorithms for the power system, a necessary step before distributed state estimation can be performed. To the best of the authors' knowledge, this is the first investigation on automatic regionalization (AR). We propose three spectral clustering based AR algorithms. Simulations show that our proposed algorithms outperform the two investigated manual regionalization cases. With the help of AR algorithms, we also show how the number of regions impacts the accuracy and convergence speed of the DSE and conclude that the number of regions needs to be chosen carefully to improve the convergence speed of DSEs.
Directory of Open Access Journals (Sweden)
Qian Sun
2017-06-01
Full Text Available The Multiple Mobile Robot (MMR cooperative system is becoming a focus of study in various fields due to its advantages, such as high efficiency and good fault tolerance. However, the uncertainty and nonlinearity problems severely limit the cooperative localization accuracy of the MMR system. Thus, to solve the problems mentioned above, this manuscript presents a cooperative localization algorithm for MMR systems based on Cubature Kalman Filter (CKF and adaptive Variance Component Estimation (VCE methods. In this novel algorithm, a nonlinear filter named CKF is used to enhance the cooperative localization accuracy and reduce the computational load. On the other hand, the adaptive VCE method is introduced to eliminate the effects of unknown system noise. Furthermore, the performance of the proposed algorithm is compared with that of the cooperative localization algorithm based on normal CKF by utilizing the real experiment data. In addition, the results demonstrate that the proposed algorithm outperforms the CKF cooperative localization algorithm both in accuracy and consistency.
PERFORMANCE ANALYSIS OF PILOT BASED CHANNEL ESTIMATION TECHNIQUES IN MB OFDM SYSTEMS
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M. Madheswaran
2011-12-01
Full Text Available Ultra wideband (UWB communication is mainly used for short range of communication in wireless personal area networks. Orthogonal Frequency Division Multiplexing (OFDM is being used as a key physical layer technology for Fourth Generation (4G wireless communication. OFDM based communication gives high spectral efficiency and mitigates Inter-symbol Interference (ISI in a wireless medium. In this paper the IEEE 802.15.3a based Multiband OFDM (MB OFDM system is considered. The pilot based channel estimation techniques are considered to analyze the performance of MB OFDM systems over Liner Time Invariant (LTI Channel models. In this paper, pilot based Least Square (LS and Least Minimum Mean Square Error (LMMSE channel estimation technique has been considered for UWB OFDM system. In the proposed method, the estimated Channel Impulse Responses (CIRs are filtered in the time domain for the consideration of the channel delay spread. Also the performance of proposed system has been analyzed for different modulation techniques for various pilot density patterns.
Khan, Fahd Ahmed
2012-04-01
New coherent receivers are derived for a pilot-symbol-aided distributed space-time block-coded system with imperfect channel state information which do not perform channel estimation at the destination by using the received pilot signals directly for decoding. The derived receivers are based on new metrics that use distribution of the channels and the noise to achieve improved symbol-error-rate (SER) performance. The SER performance of the derived receivers is further improved by utilizing the decision history in the receivers. The decision history is also incorporated in the existing Euclidean metric to improve its performance. Simulation results show that, for 16-quadrature-amplitude-modulation in a Rayleigh fading channel, a performance gain of up to 2.5 dB can be achieved for the new receivers compared with the conventional mismatched coherent receiver. © 2012 IEEE.
Adaptive Variance Scaling in Continuous Multi-Objective Estimation-of-Distribution Algorithms
P.A.N. Bosman (Peter); D. Thierens (Dirk); D. Thierens (Dirk)
2007-01-01
htmlabstractRecent research into single-objective continuous Estimation-of-Distribution Algorithms (EDAs) has shown that when maximum-likelihood estimations are used for parametric distributions such as the normal distribution, the EDA can easily suffer from premature convergence. In this paper we
A Note on Parameter Estimation for Lazarsfeld's Latent Class Model Using the EM Algorithm.
Everitt, B. S.
1984-01-01
Latent class analysis is formulated as a problem of estimating parameters in a finite mixture distribution. The EM algorithm is used to find the maximum likelihood estimates, and the case of categorical variables with more than two categories is considered. (Author)
Capacity estimation and verification of quantum channels with arbitrarily correlated errors.
Pfister, Corsin; Rol, M Adriaan; Mantri, Atul; Tomamichel, Marco; Wehner, Stephanie
2018-01-02
The central figure of merit for quantum memories and quantum communication devices is their capacity to store and transmit quantum information. Here, we present a protocol that estimates a lower bound on a channel's quantum capacity, even when there are arbitrarily correlated errors. One application of these protocols is to test the performance of quantum repeaters for transmitting quantum information. Our protocol is easy to implement and comes in two versions. The first estimates the one-shot quantum capacity by preparing and measuring in two different bases, where all involved qubits are used as test qubits. The second verifies on-the-fly that a channel's one-shot quantum capacity exceeds a minimal tolerated value while storing or communicating data. We discuss the performance using simple examples, such as the dephasing channel for which our method is asymptotically optimal. Finally, we apply our method to a superconducting qubit in experiment.
On the BER and capacity analysis of MIMO MRC systems with channel estimation error
Yang, Liang
2011-10-01
In this paper, we investigate the effect of channel estimation error on the capacity and bit-error rate (BER) of a multiple-input multiple-output (MIMO) transmit maximal ratio transmission (MRT) and receive maximal ratio combining (MRC) systems over uncorrelated Rayleigh fading channels. We first derive the ergodic (average) capacity expressions for such systems when power adaptation is applied at the transmitter. The exact capacity expression for the uniform power allocation case is also presented. Furthermore, to investigate the diversity order of MIMO MRT-MRC scheme, we derive the BER performance under a uniform power allocation policy. We also present an asymptotic BER performance analysis for the MIMO MRT-MRC system with multiuser diversity. The numerical results are given to illustrate the sensitivity of the main performance to the channel estimation error and the tightness of the approximate cutoff value. © 2011 IEEE.
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Hayley Evers-King
2017-08-01
Full Text Available Particulate Organic Carbon (POC plays a vital role in the ocean carbon cycle. Though relatively small compared with other carbon pools, the POC pool is responsible for large fluxes and is linked to many important ocean biogeochemical processes. The satellite ocean-color signal is influenced by particle composition, size, and concentration and provides a way to observe variability in the POC pool at a range of temporal and spatial scales. To provide accurate estimates of POC concentration from satellite ocean color data requires algorithms that are well validated, with uncertainties characterized. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean color data provided by the Ocean Color Climate Change Initiative (OC-CCI and validated against the largest database of in situ POC measurements currently available. The results of this validation exercise indicate satisfactory levels of performance from several algorithms (highest performance was observed from the algorithms of Loisel et al., 2002; Stramski et al., 2008 and uncertainties that are within the requirements of the user community. Estimates of the standing stock of the POC can be made by applying these algorithms, and yield an estimated mixed-layer integrated global stock of POC between 0.77 and 1.3 Pg C of carbon. Performance of the algorithms vary regionally, suggesting that blending of region-specific algorithms may provide the best way forward for generating global POC products.
Iterative optimization algorithm with parameter estimation for the ambulance location problem.
Kim, Sun Hoon; Lee, Young Hoon
2016-12-01
The emergency vehicle location problem to determine the number of ambulance vehicles and their locations satisfying a required reliability level is investigated in this study. This is a complex nonlinear issue involving critical decision making that has inherent stochastic characteristics. This paper studies an iterative optimization algorithm with parameter estimation to solve the emergency vehicle location problem. In the suggested algorithm, a linear model determines the locations of ambulances, while a hypercube simulation is used to estimate and provide parameters regarding ambulance locations. First, we suggest an iterative hypercube optimization algorithm in which interaction parameters and rules for the hypercube and optimization are identified. The interaction rules employed in this study enable our algorithm to always find the locations of ambulances satisfying the reliability requirement. We also propose an iterative simulation optimization algorithm in which the hypercube method is replaced by a simulation, to achieve computational efficiency. The computational experiments show that the iterative simulation optimization algorithm performs equivalently to the iterative hypercube optimization. The suggested algorithms are found to outperform existing algorithms suggested in the literature.
On Frequency Offset Estimation Using the iNET Preamble in Frequency Selective Fading Channels
2014-03-01
ASM fields; (bottom) the relationship between the indexes of the received samples r(n), the signal samples s(n), the preamble samples p (n) and the short...frequency offset estimators for SOQPSK-TG equipped with the iNET preamble and operating in ISI channels. Four of the five estimators exam - ined here are...sync marker ( ASM ), and data bits (an LDPC codeword). The availability of a preamble introduces the possibility of data-aided synchro- nization in
Preamble-aided time delay estimation in frequency selective channels for wireless OFDM systems
Directory of Open Access Journals (Sweden)
Qun Yu
2014-08-01
Full Text Available In this Letter, an improved method for estimating the time delay in preamble-aided orthogonal frequency division multiplexing systems is presented. It uses a conventional preamble structure and combines cross-correlation techniques to achieve estimations of time delay and the number of multipaths without any additional overhead. Computer simulations results show that the proposed method is of near-ideal property in frequency-selected channels.
A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays
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Kittipong Hiriotappa
2017-01-01
Full Text Available Knowing traffic congestion and its impact on travel time in advance is vital for proactive travel planning as well as advanced traffic management. This paper proposes a streaming algorithm to estimate temporal and spatial extent of delays online which can be deployed with roadside sensors. First, the proposed algorithm uses streaming input from individual sensors to detect a deviation from normal traffic patterns, referred to as anomalies, which is used as an early indication of delay occurrence. Then, a group of consecutive sensors that detect anomalies are used to temporally and spatially estimate extent of delay associated with the detected anomalies. Performance evaluations are conducted using a real-world data set collected by roadside sensors in Bangkok, Thailand, and the NGSIM data set collected in California, USA. Using NGSIM data, it is shown qualitatively that the proposed algorithm can detect consecutive occurrences of shockwaves and estimate their associated delays. Then, using a data set from Thailand, it is shown quantitatively that the proposed algorithm can detect and estimate delays associated with both recurring congestion and incident-induced nonrecurring congestion. The proposed algorithm also outperforms the previously proposed streaming algorithm.
DEFF Research Database (Denmark)
Pittalà, Fabio; Msallem, Majdi; Hauske, Fabian N.
2012-01-01
We propose a non-weighted feed-forward equalization method with filter update by averaging channel estimations based on short CAZAC sequences. Three averaging methods are presented and tested by simulations in a time-varying 2×2 MIMO optical system....
Channel estimation method for FBMC/OQAM system based on pilot
Directory of Open Access Journals (Sweden)
GONG Shuai
2016-04-01
Full Text Available Compared with orthogonal frequency division multiplexing technology (OFDM,filter bank multicarrier (FBMC technique has higher spectrum using rate and easier achieved pulse shaping.It was seen as a potential replacement for OFDM as future 5G physical layer of alternative technical solutions.However,in practical applications of the estimation of the channelFBMC with offset quadrature amplitude modulation (FBMC/OQAM system,the FBMC/OQAM system in the filter only orthogonal in the real domain satisfies,there is a natural interference in the imaginary domain,so the classical orthogonal frequency divided multiplex channel estimation methods cannot be directly applied.Aiming at this problem,this paper proposes a new discrete pilot based channel estimation method,in which will be the pilot caused by interference of the data block partition domain processing,in different regions use different interference cancellation scheme,stepwise elimination the imaginary part of interference.The simulation shows that this scheme makes channel estimation performance has been significantly improved,and improve the performance of FBMC/OQAM system.
Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika
2014-01-01
Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.
Low-sampling-rate ultra-wideband channel estimation using a bounded-data-uncertainty approach
Ballal, Tarig
2014-01-01
This paper proposes a low-sampling-rate scheme for ultra-wideband channel estimation. In the proposed scheme, P pulses are transmitted to produce P observations. These observations are exploited to produce channel impulse response estimates at a desired sampling rate, while the ADC operates at a rate that is P times less. To avoid loss of fidelity, the interpulse interval, given in units of sampling periods of the desired rate, is restricted to be co-prime with P. This condition is affected when clock drift is present and the transmitted pulse locations change. To handle this situation and to achieve good performance without using prior information, we derive an improved estimator based on the bounded data uncertainty (BDU) model. This estimator is shown to be related to the Bayesian linear minimum mean squared error (LMMSE) estimator. The performance of the proposed sub-sampling scheme was tested in conjunction with the new estimator. It is shown that high reduction in sampling rate can be achieved. The proposed estimator outperforms the least squares estimator in most cases; while in the high SNR regime, it also outperforms the LMMSE estimator. © 2014 IEEE.
Suyama, Satoshi; Suzuki, Hiroshi; Fukawa, Kazuhiko; Zhang, Li
This paper applies iterative multiuser detection employing a new channel estimation scheme to multicarrier interleave-division multiple access (MC-IDMA), called OFDM-IDMA, which is expected to offer improved spectral efficiency in mobile communications. The MC-IDMA transmitter uses both a low-rate channel code and an individual chip interleaver for each user. The MC-IDMA receiver, which this paper focuses upon, repeats the iterative multiuser detection and soft decision-directed channel estimation (SDCE) by exploiting log-likelihood ratios (LLRs) of the coded bits which the maximum a posteriori (MAP)-based channel decoders for all users provide. SDCE estimates channel impulse responses of all users by the least-mean-square (LMS) algorithm, which aims to minimize the mean squared error between the received signal and its replica. This paper investigates the performance of MC-IDMA employing SDCE and compares it with those of three MC-CDMA techniques. Computer simulations demonstrate that MC-IDMA employing SDCE outperforms time-spread MC-CDMA and frequency-spread MC-CDMA, and that it can achieve almost the same bit error rate performance as chip-interleaved MC-CDMA while requiring lower complexity.
Arabzadeh, Vida; Niaki, S. T. A.; Arabzadeh, Vahid
2017-10-01
One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg-Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.
Zhao, Binjuan; Wang, Yu; Chen, Huilong; Qiu, Jing; Hou, Duohua
2015-03-01
Computational fluid dynamics (CFD) can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm (MOGA) and artificial neural networks (ANN) for a double-channel pump's impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of
Directory of Open Access Journals (Sweden)
V. Jayaraj
2010-08-01
Full Text Available A Non-linear adaptive decision based algorithm with robust motion estimation technique is proposed for removal of impulse noise, Gaussian noise and mixed noise (impulse and Gaussian with edge and fine detail preservation in images and videos. The algorithm includes detection of corrupted pixels and the estimation of values for replacing the corrupted pixels. The main advantage of the proposed algorithm is that an appropriate filter is used for replacing the corrupted pixel based on the estimation of the noise variance present in the filtering window. This leads to reduced blurring and better fine detail preservation even at the high mixed noise density. It performs both spatial and temporal filtering for removal of the noises in the filter window of the videos. The Improved Cross Diamond Search Motion Estimation technique uses Least Median Square as a cost function, which shows improved performance than other motion estimation techniques with existing cost functions. The results show that the proposed algorithm outperforms the other algorithms in the visual point of view and in Peak Signal to Noise Ratio, Mean Square Error and Image Enhancement Factor.
Bruning, Eric C.; Thomas, Ronald J.; Krehbiel, Paul R.; Rison, William; Carey, Larry D.; Koshak, William; Peterson, Harold; MacGorman, Donald R.
2013-01-01
We will use VHF Lightning Mapping Array data to estimate NOx per flash and per unit channel length, including the vertical distribution of channel length. What s the best way to find channel length from VHF sources? This paper presents the rationale for the fractal method, which is closely related to the box-covering method.
MOHAMMED, M. A. SI; BOUSSADIA, H.; BELLAR, A.; ADNANE, A.
2017-01-01
This paper presents a brief synthesis and useful performance analysis of different attitude filtering algorithms (attitude determination algorithms, attitude estimation algorithms, and nonlinear observers) applied to Low Earth Orbit Satellite in terms of accuracy, convergence time, amount of memory, and computation time. This latter is calculated in two ways, using a personal computer and also using On-board computer 750 (OBC 750) that is being used in many SSTL Earth observation missions. The use of this comparative study could be an aided design tool to the designer to choose from an attitude determination or attitude estimation or attitude observer algorithms. The simulation results clearly indicate that the nonlinear Observer is the more logical choice.
Energy Technology Data Exchange (ETDEWEB)
Lee, Jung Uk [Samsung Electroics, Suwon (Korea, Republic of); Sun, Ju Young; Won, Mooncheol [Chungnam Nat' l Univ., Daejeon (Korea, Republic of)
2013-12-15
In this paper, we propose a real-time algorithm for estimating the relative position of a person with respect to a robot (camera) using a monocular camera. The algorithm detects the head and shoulder regions of a person using HOG (Histogram of Oriented Gradient) feature vectors and an SVM (Support Vector Machine) classifier. The size and location of the detected area are used for calculating the relative distance and angle between the person and the camera on a robot. To increase the speed of the algorithm, we use a GPU and NVIDIA's CUDA library; the resulting algorithm speed is ∼ 15 Hz. The accuracy of the algorithm is compared with the output of a SICK laser scanner.
Shan, Yuqi; Liu, Xingnian; Yang, Kejun; Liu, Chao
2017-10-01
For overbank flows, submerged flexible vegetation on floodplains increases channel resistance and decreases channel conveyance capability. This study presents an analytical model for estimating the stage-discharge relationship in a meandering compound channel with dense, submerged, flexible vegetation on floodplains under high flow conditions. The mean velocity within a canopy was linked to the depth-averaged velocity, and a relationship between the two velocities was proposed. The governing equation was deduced in curvilinear coordinates, and the lateral shear stresses were found to be negligible, as validated by our experimental measurements in a large-scale meandering channel. Then, analytical solutions of subarea discharges and total discharge were derived by ignoring lateral shear stresses. Measurements from two flume experiments and one field study were used to verify the proposed model. The field case involved a natural river with both submerged and emergent grass on the floodplains. Good agreement between predictions and measurements indicated that the model accurately predicted subarea discharges and the stage-discharge relationships in a meandering compound channel with submerged vegetation. Finally, the predictions of this model were sensitive to the secondary flow parameters in the main channel but insensitive to those on the floodplains.
Joint Symbol Timing and CFO Estimation for OFDM/OQAM Systems in Multipath Channels
Directory of Open Access Journals (Sweden)
Angelo Petrella
2010-01-01
Full Text Available The problem of data-aided synchronization for orthogonal frequency division multiplexing (OFDM systems based on offset quadrature amplitude modulation (OQAM in multipath channels is considered. In particular, the joint maximum-likelihood (ML estimator for carrier-frequency offset (CFO, amplitudes, phases, and delays, exploiting a short known preamble, is derived. The ML estimators for phases and amplitudes are in closed form. Moreover, under the assumption that the CFO is sufficiently small, a closed form approximate ML (AML CFO estimator is obtained. By exploiting the obtained closed form solutions a cost function whose peaks provide an estimate of the delays is derived. In particular, the symbol timing (i.e., the delay of the first multipath component is obtained by considering the smallest estimated delay. The performance of the proposed joint AML estimator is assessed via computer simulations and compared with that achieved by the joint AML estimator designed for AWGN channel and that achieved by a previously derived joint estimator for OFDM systems.
Final Report: Sampling-Based Algorithms for Estimating Structure in Big Data.
Energy Technology Data Exchange (ETDEWEB)
Matulef, Kevin Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-02-01
The purpose of this project was to develop sampling-based algorithms to discover hidden struc- ture in massive data sets. Inferring structure in large data sets is an increasingly common task in many critical national security applications. These data sets come from myriad sources, such as network traffic, sensor data, and data generated by large-scale simulations. They are often so large that traditional data mining techniques are time consuming or even infeasible. To address this problem, we focus on a class of algorithms that do not compute an exact answer, but instead use sampling to compute an approximate answer using fewer resources. The particular class of algorithms that we focus on are streaming algorithms , so called because they are designed to handle high-throughput streams of data. Streaming algorithms have only a small amount of working storage - much less than the size of the full data stream - so they must necessarily use sampling to approximate the correct answer. We present two results: * A streaming algorithm called HyperHeadTail , that estimates the degree distribution of a graph (i.e., the distribution of the number of connections for each node in a network). The degree distribution is a fundamental graph property, but prior work on estimating the degree distribution in a streaming setting was impractical for many real-world application. We improve upon prior work by developing an algorithm that can handle streams with repeated edges, and graph structures that evolve over time. * An algorithm for the task of maintaining a weighted subsample of items in a stream, when the items must be sampled according to their weight, and the weights are dynamically changing. To our knowledge, this is the first such algorithm designed for dynamically evolving weights. We expect it may be useful as a building block for other streaming algorithms on dynamic data sets.
Robust Vision-Based Pose Estimation Algorithm for AN Uav with Known Gravity Vector
Kniaz, V. V.
2016-06-01
Accurate estimation of camera external orientation with respect to a known object is one of the central problems in photogrammetry and computer vision. In recent years this problem is gaining an increasing attention in the field of UAV autonomous flight. Such application requires a real-time performance and robustness of the external orientation estimation algorithm. The accuracy of the solution is strongly dependent on the number of reference points visible on the given image. The problem only has an analytical solution if 3 or more reference points are visible. However, in limited visibility conditions it is often needed to perform external orientation with only 2 visible reference points. In such case the solution could be found if the gravity vector direction in the camera coordinate system is known. A number of algorithms for external orientation estimation for the case of 2 known reference points and a gravity vector were developed to date. Most of these algorithms provide analytical solution in the form of polynomial equation that is subject to large errors in the case of complex reference points configurations. This paper is focused on the development of a new computationally effective and robust algorithm for external orientation based on positions of 2 known reference points and a gravity vector. The algorithm implementation for guidance of a Parrot AR.Drone 2.0 micro-UAV is discussed. The experimental evaluation of the algorithm proved its computational efficiency and robustness against errors in reference points positions and complex configurations.
Directory of Open Access Journals (Sweden)
Kaifeng Yang
2014-01-01
Full Text Available A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.
Yang, Kaifeng; Mu, Li; Yang, Dongdong; Zou, Feng; Wang, Lei; Jiang, Qiaoyong
2014-01-01
A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.
A Survey on Distributed Estimation and Control Applications Using Linear Consensus Algorithms
Garin, Federica; Schenato, Luca
In this chapter we present a popular class of distributed algorithms, known as linear consensus algorithms, which have the ability to compute the global average of local quantities. These algorithms are particularly suitable in the context of multi-agent systems and networked control systems, i.e. control systems that are physically distributed and cooperate by exchanging information through a communication network. We present the main results available in the literature about the analysis and design of linear consensus algorithms,for both synchronous and asynchronous implementations. We then show that many control, optimization and estimation problems such as least squares, sensor calibration, vehicle coordination and Kalman filtering can be cast as the computation of some sort of averages, therefore being suitable for consensus algorithms. We finally conclude by presenting very recent studies about the performance of many of these control and estimation problems, which give rise to novel metrics for the consensus algorithms. These indexes of performance are rather different from more traditional metrics like the rate of convergence and have fundamental consequences on the design of consensus algorithms.
Pilot-Assisted Adaptive Channel Estimation for Coded MC-CDMA with ICI Cancellation
Yui, Tatsunori; Tomeba, Hiromichi; Adachi, Fumiyuki
One of the promising wireless access techniques for the next generation mobile communications systems is multi-carrier code division multiple access (MC-CDMA). MC-CDMA can provide good transmission performance owing to the frequency diversity effect in a severe frequency-selective fading channel. However, the bit error rate (BER) performance of coded MC-CDMA is inferior to that of orthogonal frequency division multiplexing (OFDM) due to the residual inter-code interference (ICI) after frequency-domain equalization (FDE). Recently, we proposed a frequency-domain soft interference cancellation (FDSIC) to reduce the residual ICI and confirmed by computer simulation that the MC-CDMA with FDSIC provides better BER performance than OFDM. However, ideal channel estimation was assumed. In this paper, we propose adaptive decision-feedback channel estimation (ADFCE) and evaluate by computer simulation the average BER and throughput performances of turbo-coded MC-CDMA with FDSIC. We show that even if a practical channel estimation is used, MC-CDMA with FDSIC can still provide better performance than OFDM.
A fast algorithm for direction of arrival estimation in multipath environments
Tayem, Nizar; Naraghi-Pour, Mort
2007-04-01
A new spectral direction of arrival (DOA) estimation algorithm is proposed that can rapidly estimate the DOA of non-coherent as well as coherent incident signals. As such the algorithm is effective for DOA estimation in multi-path environments. The proposed method constructs a data model based on a Hermitian Toeplitz matrix whose rank is related to the DOA of incoming signals and is not affected if the incoming sources are highly correlated. The data is rearranged in such a way that extends the dimensionality of the noise space. Consequently, the signal and noise spaces can be estimated more accurately. The proposed method has several advantages over the well-known classical subspace algorithms such as MUSIC and ESPRIT, as well as the Matrix Pencil (MP) method. In particular, the proposed method is suitable for real-time applications since it does not require multiple snapshots in order to estimate the DOA's. Moreover, no forward/backward spatial smoothing of the covariance matrix is needed, resulting in reduced computational complexity. Finally, the proposed method can estimate the DOA of coherent sources. The simulation results verify that the proposed method outperforms the MUSIC, ESPRIT and Matrix Pencil algorithms.
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
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Lazzús, Juan A., E-mail: jlazzus@dfuls.cl; Rivera, Marco; López-Caraballo, Carlos H.
2016-03-11
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.
Directory of Open Access Journals (Sweden)
O.V. Kostyrka
2016-09-01
Full Text Available At the organization of a covert communication channel a number of requirements are imposed on used steganography algorithms among which one of the main are: resistance to attacks against the built-in message, reliability of perception of formed steganography message, significant throughput of a steganography communication channel. Aim: The aim of this research is to modify the steganography method, developed by the author earlier, which will allow to increase the throughput of the corresponding covert communication channel when saving resistance to attacks against the built-in message and perception reliability of the created steganography message, inherent to developed method. Materials and Methods: Modifications of a steganography method that is steady against attacks against the built-in message which is carrying out the inclusion and decoding of the sent (additional information in spatial domain of the image allowing to increase the throughput of the organized communication channel are offered. Use of spatial domain of the image allows to avoid accumulation of an additional computational error during the inclusion/decoding of additional information due to “transitions” from spatial domain of the image to the area of conversion and back that positively affects the efficiency of decoding. Such methods are considered as attacks against the built-in message: imposing of different noise on a steganography message, filtering, lossy compression of a ste-ganography message where the JPEG and JPEG2000 formats with different quality coefficients for saving of a steganography message are used. Results: It is shown that algorithmic implementations of the offered methods modifications remain steady against the perturbing influences, including considerable, provide reliability of perception of the created steganography message, increase the throughput of the created steganography communication channel in comparison with the algorithm implementing
Nanosatellite Attitude Estimation from Vector Measurements Using SVD-Aided UKF Algorithm
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Cilden Demet
2017-03-01
Full Text Available The integrated Singular Value Decomposition (SVD and Unscented Kalman Filter (UKF method can recursively estimate the attitude and attitude rates of a nanosatellite. At first, Wahba’s loss function is minimized using the SVD and the optimal attitude angles are determined on the basis of the magnetometer and Sun sensor measurements. Then, the UKF makes use of the SVD’s attitude estimates as measurement results and provides more accurate attitude information as well as the attitude rate estimates. The elements of “Rotation angle error covariance matrix” calculated for the SVD estimations are used in the UKF as the measurement noise covariance values. The algorithm is compared with the SVD and UKF only methods for estimating the attitude from vector measurements. Possible algorithm switching ideas are discussed especially for the eclipse period, when the Sun sensor measurements are not available.
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Önen Erol
2010-01-01
Full Text Available We consider the estimation of time-varying channels for Cooperative Orthogonal Frequency Division Multiplexing (CO-OFDM systems. In the next generation mobile wireless communication systems, significant Doppler frequency shifts are expected the channel frequency response to vary in time. A time-invariant channel is assumed during the transmission of a symbol in the previous studies on CO-OFDM systems, which is not valid in high mobility cases. Estimation of channel parameters is required at the receiver to improve the performance of the system. We estimate the model parameters of the channel from a time-frequency representation of the received signal. We present two approaches for the CO-OFDM channel estimation problem where in the first approach, individual channels are estimated at the relay and destination whereas in the second one, the cascaded source-relay-destination channel is estimated at the destination. Simulation results show that the individual channel estimation approach has better performance in terms of MSE and BER; however it has higher computational cost compared to the cascaded approach.
Indian Academy of Sciences (India)
positive numbers. The word 'algorithm' was most often associated with this algorithm till 1950. It may however be pOinted out that several non-trivial algorithms such as synthetic (polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used.
Joint Smoothed l0-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar
Liu, Jing; Zhou, Weidong; Juwono, Filbert H.
2017-01-01
Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l0-norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l0-norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l1-norm minimization based methods, such as l1-SVD (singular value decomposition), RV (real-valued) l1-SVD and RV l1-SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance. PMID:28481309
Joint Smoothed l₀-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar.
Liu, Jing; Zhou, Weidong; Juwono, Filbert H
2017-05-08
Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l 0 -norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l 0 -norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l 1 -norm minimization based methods, such as l 1 -SVD (singular value decomposition), RV (real-valued) l 1 -SVD and RV l 1 -SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance.
Meng, Luming; Sheong, Fu Kit; Zeng, Xiangze; Zhu, Lizhe; Huang, Xuhui
2017-07-01
Constructing Markov state models from large-scale molecular dynamics simulation trajectories is a promising approach to dissect the kinetic mechanisms of complex chemical and biological processes. Combined with transition path theory, Markov state models can be applied to identify all pathways connecting any conformational states of interest. However, the identified pathways can be too complex to comprehend, especially for multi-body processes where numerous parallel pathways with comparable flux probability often coexist. Here, we have developed a path lumping method to group these parallel pathways into metastable path channels for analysis. We define the similarity between two pathways as the intercrossing flux between them and then apply the spectral clustering algorithm to lump these pathways into groups. We demonstrate the power of our method by applying it to two systems: a 2D-potential consisting of four metastable energy channels and the hydrophobic collapse process of two hydrophobic molecules. In both cases, our algorithm successfully reveals the metastable path channels. We expect this path lumping algorithm to be a promising tool for revealing unprecedented insights into the kinetic mechanisms of complex multi-body processes.
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Jong-Seob Baek
2008-01-01
Full Text Available This paper presents a new block iterative/adaptive frequency-domain channel estimation scheme, in which a channel frequency response (CFR is estimated iteratively by the proposed weighted element-wise block adaptive frequency-domain channel estimation (WEB-CE scheme using the soft information obtained by a soft-input soft-output (SISO decoder. In the WEB-CE, an equalizer coefficient is calculated by minimizing a weighted conditional squared-norm of the a posteriori error vector with respect to its correction term. Simulation results verify the superiority of the WEB-CE in a time-varying typical urban (TU channel.
A New Algorithm for Joint Range-DOA-Frequency Estimation of Near-Field Sources
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Jian-Feng Chen
2004-03-01
Full Text Available This paper studies the joint estimation problem of ranges, DOAs, and frequencies of near-field narrowband sources and proposes a new computationally efficient algorithm, which employs a symmetric uniform linear array, uses eigenvalues together with the corresponding eigenvectors of two properly designed matrices to estimate signal parameters, and does not require searching for spectral peak or pairing among parameters. In addition, the proposed algorithm can be applied in arbitrary Gaussian noise environment since it is based on the fourth-order cumulants, which is verified by extensive computer simulations.
Extended lubrication theory: improved estimates of flow in channels with variable geometry.
Tavakol, Behrouz; Froehlicher, Guillaume; Holmes, Douglas P; Stone, Howard A
2017-10-01
Lubrication theory is broadly applicable to the flow characterization of thin fluid films and the motion of particles near surfaces. We offer an extension to lubrication theory by starting with Stokes equations and considering higher-order terms in a systematic perturbation expansion to describe the fluid flow in a channel with features of a modest aspect ratio. Experimental results qualitatively confirm the higher-order analytical solutions, while numerical results are in very good agreement with the higher-order analytical results. We show that the extended lubrication theory is a robust tool for an accurate estimate of pressure drop in channels with shape changes on the order of the channel height, accounting for both smooth and sharp changes in geometry.
Ban, Takahiko; Migita, Shinji; Uenuma, Mutsunori; Okamoto, Naofumi; Ishikawa, Yasuaki; Uraoka, Yukiharu; Yamashita, Ichiro; Yamamoto, Shin-ichi
2017-03-01
Metal nanoparticles (NPs) embedded in junctionless field-effect transistors (JL-FETs) with a channel length of about sub-10-nm are fabricated and demonstrated. The anisotropic wet etching of a silicon-on-insulator (SOI) substrate was utilized to form V-grooves and define a nanometer-scale channel. Metal NPs are selectively placed onto the bottom of a V-groove using a bio nano process (BNP). A JL-FET is applied to a floating gate memory and used to study the impacts of charges close to the short channel. Low-voltage operation and memory behavior of broad threshold voltage appear. It is estimated by simulation that positive and negative charges equivalent to approximately 10 electrons are accumulated in one NP. It is expected that the JL-FETs can overcome the scaling limitations of floating gate memories.
Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection
Polychronaki, G. E.; Ktonas, P. Y.; Gatzonis, S.; Siatouni, A.; Asvestas, P. A.; Tsekou, H.; Sakas, D.; Nikita, K. S.
2010-08-01
Fractal dimension (FD) is a natural measure of the irregularity of a curve. In this study the performances of three waveform FD estimation algorithms (i.e. Katz's, Higuchi's and the k-nearest neighbour (k-NN) algorithm) were compared in terms of their ability to detect the onset of epileptic seizures in scalp electroencephalogram (EEG). The selection of parameters involved in FD estimation, evaluation of the accuracy of the different algorithms and assessment of their robustness in the presence of noise were performed based on synthetic signals of known FD. When applied to scalp EEG data, Katz's and Higuchi's algorithms were found to be incapable of producing consistent changes of a single type (either a drop or an increase) during seizures. On the other hand, the k-NN algorithm produced a drop, starting close to the seizure onset, in most seizures of all patients. The k-NN algorithm outperformed both Katz's and Higuchi's algorithms in terms of robustness in the presence of noise and seizure onset detection ability. The seizure detection methodology, based on the k-NN algorithm, yielded in the training data set a sensitivity of 100% with 10.10 s mean detection delay and a false positive rate of 0.27 h-1, while the corresponding values in the testing data set were 100%, 8.82 s and 0.42 h-1, respectively. The above detection results compare favourably to those of other seizure onset detection methodologies applied to scalp EEG in the literature. The methodology described, based on the k-NN algorithm, appears to be promising for the detection of the onset of epileptic seizures based on scalp EEG.
Aniba, Ghassane
2011-04-01
This paper presents an optimal adaptive modulation (AM) algorithm designed using a cross-layer approach which combines truncated automatic repeat request (ARQ) protocol and packet combining. Transmissions are performed over multiple-input multiple-output (MIMO) Nakagami fading channels, and retransmitted packets are not necessarily modulated using the same modulation format as in the initial transmission. Compared to traditional approach, cross-layer design based on the coupling across the physical and link layers, has proven to yield better performance in wireless communications. However, there is a lack for the performance analysis and evaluation of such design when the ARQ protocol is used in conjunction with packet combining. Indeed, previous works addressed the link layer performance of AM with truncated ARQ but without packet combining. In addition, previously proposed AM algorithms are not optimal and can provide poor performance when packet combining is implemented. Herein, we first show that the packet loss rate (PLR) resulting from the combining of packets modulated with different constellations can be well approximated by an exponential function. This model is then used in the design of an optimal AM algorithm for systems employing packet combining, truncated ARQ and MIMO antenna configurations, considering transmission over Nakagami fading channels. Numerical results are provided for operation with or without packet combining, and show the enhanced performance and efficiency of the proposed algorithm in comparison with existing ones. © 2011 IEEE.
Estimation of Channel-Forming Discharge and Large-Event Geomorphic Response Using HEC-RAS
Hamilton, P.; Strom, K.; Hosseiny, S. M. H.
2015-12-01
The goal of the present work was to consider the functionality and applicability of HEC-RAS sediment transport simulations in two situations. The first was as a mode for obtaining quick estimates of the effective discharge, one measure of channel-forming discharge, and the second was as a mode to quickly estimate sediment transport and the commensurate potential erosion and deposition during large flood events. Though there are many other sediment transport and morphodynamic models available, e.g., CCHE1D, Nays2DH, we were interested in using HEC-RAS since this is the model of choice for many regulatory bodies, e.g., FEMA, cities, and counties. This makes using the sediment transport capability of HEC-RAS a natural extension of models that already otherwise exist and are well calibrated. In first looking at the utility of these models, we wanted to estimate the effective discharge of streams. Effective discharge is one way of defining the channel-forming discharge for a stream and is therefore an important parameter in natural channel design and restoration efforts. By running this range of floods, one can easily obtain an estimate for recurrence interval most responsible for moving the majority of sediment over a long time period. Results were compared to data collected within our research group on the Brazos River (TX). Effective discharge is an important estimate, particularly in understanding the equilibrium channel condition. Nevertheless, large floods are contemporaneously catastrophic and understanding their potential effects is desirable. Finally, we performed some sensitivity analysis to better understand the underlying assumptions of the various sediment transport model options and how they might affect the outcome of the aforementioned computations.
A smoothing algorithm for joint input-state estimation in structural dynamics
Maes, K.; Gillijns, S.; Lombaert, G.
2018-01-01
This paper presents a recursive algorithm where a time delay is considered in the estimation of the forces applied to a structure and the corresponding system states. In particular when the measured response is not collocated with the estimated forces, essential information on the estimated forces and/or system states is contained in the response at L consecutive time steps following the time step where the estimation is performed. The main focus in this paper is on the reduction in estimation uncertainty that can be achieved by so-called smoothing, i.e. by considering a time delay in the estimation. When the calculation of the gain matrices is included in the recursive estimation, the calculation time of the algorithm largely increases with the time delay. It is shown that a prior calculation of the steady-state gain matrices allows for a significant reduction of the calculation time. The presented algorithm is first verified using numerical simulations. Next, a validation is performed using data obtained from a field test on a footbridge.
An Iterated Local Search Algorithm for Estimating the Parameters of the Gamma/Gompertz Distribution
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Behrouz Afshar-Nadjafi
2014-01-01
Full Text Available Extensive research has been devoted to the estimation of the parameters of frequently used distributions. However, little attention has been paid to estimation of parameters of Gamma/Gompertz distribution, which is often encountered in customer lifetime and mortality risks distribution literature. This distribution has three parameters. In this paper, we proposed an algorithm for estimating the parameters of Gamma/Gompertz distribution based on maximum likelihood estimation method. Iterated local search (ILS is proposed to maximize likelihood function. Finally, the proposed approach is computationally tested using some numerical examples and results are analyzed.
R. L. Czaplewski
2009-01-01
The minimum variance multivariate composite estimator is a relatively simple sequential estimator for complex sampling designs (Czaplewski 2009). Such designs combine a probability sample of expensive field data with multiple censuses and/or samples of relatively inexpensive multi-sensor, multi-resolution remotely sensed data. Unfortunately, the multivariate composite...
Carrier frequency offset estimation for an acoustic-electric channel using 16 QAM modulation
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.
Estimation of channel characteristics of narrow bipolar events based on the transmission-line model
Zhu, Baoyou; Zhou, Helin; Ma, Ming; Lv, Fanchao; Tao, Shanchang
2010-10-01
Narrow bipolar event (NBE) is a distinct class of intracloud lightning discharge, which is associated with the strongest radio frequency emissions and produces typical narrow bipolar radiation field waveforms. On the basis of the transmission-line model, we introduce a direct technique to measure the time taken by the current front to propagate along the channel from distant radiation field pulses; the channel length of the NBE can then be estimated by multiplying this time by an assumed propagation speed. Our method involves integrating over the initial half cycle of narrow bipolar waveform of the NBE. The ratio of the integral result to the initial peak amplitude makes a good approximation to the time taken by the current front to travel along the channel, even though the current amplitude suffers heavy attenuation along the propagating channel. This method can be applied to all NBEs which produce narrow bipolar radiation field waveforms. Besides, if both the far radiation field and the near-electrostatic field measurements were available, one could combine the method here and that of Eack (2004) to obtain the channel length of the NBE.
A Fast Elitism Gaussian Estimation of Distribution Algorithm and Application for PID Optimization
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Qingyang Xu
2014-01-01
Full Text Available Estimation of distribution algorithm (EDA is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.
A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.
Xu, Qingyang; Zhang, Chengjin; Zhang, Li
2014-01-01
Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.
Peng, Bo; Wang, Yuqi; Hall, Timothy J; Jiang, Jingfeng
2017-04-01
Our primary objective of this paper was to extend a previously published 2-D coupled subsample tracking algorithm for 3-D speckle tracking in the framework of ultrasound breast strain elastography. In order to overcome heavy computational cost, we investigated the use of a graphic processing unit (GPU) to accelerate the 3-D coupled subsample speckle tracking method. The performance of the proposed GPU implementation was tested using a tissue-mimicking phantom and in vivo breast ultrasound data. The performance of this 3-D subsample tracking algorithm was compared with the conventional 3-D quadratic subsample estimation algorithm. On the basis of these evaluations, we concluded that the GPU implementation of this 3-D subsample estimation algorithm can provide high-quality strain data (i.e., high correlation between the predeformation and the motion-compensated postdeformation radio frequency echo data and high contrast-to-noise ratio strain images), as compared with the conventional 3-D quadratic subsample algorithm. Using the GPU implementation of the 3-D speckle tracking algorithm, volumetric strain data can be achieved relatively fast (approximately 20 s per volume [2.5 cm ×2.5 cm ×2.5 cm]).
Comparison Study on the Battery SoC Estimation with EKF and UKF Algorithms
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Hongwen He
2013-09-01
Full Text Available The battery state of charge (SoC, whose estimation is one of the basic functions of battery management system (BMS, is a vital input parameter in the energy management and power distribution control of electric vehicles (EVs. In this paper, two methods based on an extended Kalman filter (EKF and unscented Kalman filter (UKF, respectively, are proposed to estimate the SoC of a lithium-ion battery used in EVs. The lithium-ion battery is modeled with the Thevenin model and the model parameters are identified based on experimental data and validated with the Beijing Driving Cycle. Then space equations used for SoC estimation are established. The SoC estimation results with EKF and UKF are compared in aspects of accuracy and convergence. It is concluded that the two algorithms both perform well, while the UKF algorithm is much better with a faster convergence ability and a higher accuracy.
Inverse estimation of properties for charring material using a hybrid genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Chang, Hee Chul; Yoon, Kyung Beom; Kim, Tae Kuk [Chung Ang University, Seoul (Korea, Republic of); Park, Won Hee; Lee, Duck Hee; Jung, Woo Sung [Korea Railroad Research Institute, Uiwang (Korea, Republic of)
2011-06-15
Fire characteristics can be analyzed more realistically by using more accurate material properties related to the fire dynamics and one way to acquire these fire properties is to use one of the inverse property estimation techniques. In this study an optimization algorithm which is frequently applied for the inverse heat transfer problems is selected to demonstrate the procedure of obtaining fire properties of a solid charring material with relatively simple chemical structure. Thermal decomposition is occurred at the surface of the test plate by receiving the radiative energy from external heat sources and in this process the heat transfer through the test plate can be simplified by an unsteady one dimensional problem. The input parameters for the analyses are the surface temperature and mass loss rate of the char plate which are determined from the actual experiment of from the unsteady one-dimensional analysis with a given set of eight properties. The performance of hybrid genetic algorithm (HGA) is compare with a basic genetic algorithm (GA) in order to examine its performance. This comparison is carried out for the inverse property problem of estimating the fire properties related to the reaction pyrolysis of some relatively simple materials; redwood and red oak. Results show that the hybrid genetic algorithm has better performance in estimating the eight pyrolysis properties than the genetic algorithm.
Yang, Ji Seung; Cai, Li
2014-01-01
The main purpose of this study is to improve estimation efficiency in obtaining maximum marginal likelihood estimates of contextual effects in the framework of nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins-Monro algorithm (MH-RM). Results indicate that the MH-RM algorithm can produce estimates and standard…
Zhang, Xiaofei; Zhou, Min; Li, Jianfeng
2013-04-19
In this paper, we combine the acoustic vector-sensor array parameter estimation problem with the parallel profiles with linear dependencies (PARALIND) model, which was originally applied to biology and chemistry. Exploiting the PARALIND decomposition approach, we propose a blind coherent two-dimensional direction of arrival (2D-DOA) estimation algorithm for arbitrarily spaced acoustic vector-sensor arrays subject to unknown locations. The proposed algorithm works well to achieve automatically paired azimuth and elevation angles for coherent and incoherent angle estimation of acoustic vector-sensor arrays, as well as the paired correlated matrix of the sources. Our algorithm, in contrast with conventional coherent angle estimation algorithms such as the forward backward spatial smoothing (FBSS) estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, not only has much better angle estimation performance, even for closely-spaced sources, but is also available for arbitrary arrays. Simulation results verify the effectiveness of our algorithm.
Lee, Kyeong-sang; Choi, Sungwon; Seo, Minji; Lee, Chang suk; Seong, Noh-hun; Han, Kyung-Soo
2016-10-01
Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth's energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer's Accuracy), UA (User's Accuracy) and CI (Comparison Index) as validation criteria
A High Accurate and Low Bias SNR Estimator: Algorithm and Implementation
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A. J. Liu
2011-12-01
Full Text Available Signal to noise ratio (SNR estimators are required for many radio engineering applications. In this paper, a SNR estimator based on the first and second order moments is derived and examined for constant envelop modulations over additive white Gaussian noise (AWGN channel. In the first, a modification method is proposed to reduce the bias of conventional first and second order moments based SNR estimator. Then, in order to reduce hardware implementation complicity, multi segments of cubic polynomial are adopted to approximate the expression of the proposed SNR estimator which doesn’t have a close form solution. The approximate expression is very precise in the SNR range -10dB ~ 20 dB as illustrated by numerical simulation. Besides, practical hardware circuit is proposed for FPGA implementation. Simulation results show that the proposed SNR estimator has the lowest normalized bias and variance when compared with two other classic estimators.
Majeed, Muhammad Usman
2017-07-19
Steady-state elliptic partial differential equations (PDEs) are frequently used to model a diverse range of physical phenomena. The source and boundary data estimation problems for such PDE systems are of prime interest in various engineering disciplines including biomedical engineering, mechanics of materials and earth sciences. Almost all existing solution strategies for such problems can be broadly classified as optimization-based techniques, which are computationally heavy especially when the problems are formulated on higher dimensional space domains. However, in this dissertation, feedback based state estimation algorithms, known as state observers, are developed to solve such steady-state problems using one of the space variables as time-like. In this regard, first, an iterative observer algorithm is developed that sweeps over regular-shaped domains and solves boundary estimation problems for steady-state Laplace equation. It is well-known that source and boundary estimation problems for the elliptic PDEs are highly sensitive to noise in the data. For this, an optimal iterative observer algorithm, which is a robust counterpart of the iterative observer, is presented to tackle the ill-posedness due to noise. The iterative observer algorithm and the optimal iterative algorithm are then used to solve source localization and estimation problems for Poisson equation for noise-free and noisy data cases respectively. Next, a divide and conquer approach is developed for three-dimensional domains with two congruent parallel surfaces to solve the boundary and the source data estimation problems for the steady-state Laplace and Poisson kind of systems respectively. Theoretical results are shown using a functional analysis framework, and consistent numerical simulation results are presented for several test cases using finite difference discretization schemes.
2014-09-01
5). This suggests that the RBF- SVR-based model may be a little more resilient to noise and variability in the EEG or driver performance training...Estimating Driver Performance Using Multiple Electroencephalography ( EEG )-Based Regression Algorithms by Gregory Apker, Brent Lance, Scott...Proving Ground, MD 21005-5425 ARL-TR-7074 September 2014 Estimating Driver Performance Using Multiple Electroencephalography ( EEG )-Based
Directory of Open Access Journals (Sweden)
MILIVOJEVIC, Z. N.
2010-02-01
Full Text Available In this paper the fundamental frequency estimation results of the MP3 modeled speech signal are analyzed. The estimation of the fundamental frequency was performed by the Picking-Peaks algorithm with the implemented Parametric Cubic Convolution (PCC interpolation. The efficiency of PCC was tested for Catmull-Rom, Greville and Greville two-parametric kernel. Depending on MSE, a window that gives optimal results was chosen.
Research of Subgraph Estimation Page Rank Algorithm for Web Page Rank
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LI Lan-yin
2017-04-01
Full Text Available The traditional PageRank algorithm can not efficiently perform large data Webpage scheduling problem. This paper proposes an accelerated algorithm named topK-Rank，which is based on PageRank on the MapReduce platform. It can find top k nodes efficiently for a given graph without sacrificing accuracy. In order to identify top k nodes，topK-Rank algorithm prunes unnecessary nodes and edges in each iteration to dynamically construct subgraphs，and iteratively estimates lower/upper bounds of PageRank scores through subgraphs. Theoretical analysis shows that this method guarantees result exactness. Experiments show that topK-Rank algorithm can find k nodes much faster than the existing approaches.
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D.W. Kim
2013-08-01
Full Text Available Estimation of distribution algorithms (EDAs constitute a new branch of evolutionary optimization algorithms that were developed as a natural alternative to genetic algorithms (GAs. Several studies have demonstrated that the heuristic scheme of EDAs is effective and efficient for many optimization problems. Recently, it has been reported that the incorporation of mutation into EDAs increases the diversity of genetic information in the population, thereby avoiding premature convergence into a suboptimal solution. In this study, we propose a new mutation operator, a transpose mutation, designed for Bayesian structure learning. It enhances the diversity of the offspring and it increases the possibility of inferring the correct arc direction by considering the arc directions in candidate solutions as bi-directional, using the matrix transpose operator. As compared to the conventional EDAs, the transpose mutation-adopted EDAs are superior and effective algorithms for learning Bayesian networks.
Multi-User Identification-Based Eye-Tracking Algorithm Using Position Estimation
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Suk-Ju Kang
2016-12-01
Full Text Available This paper proposes a new multi-user eye-tracking algorithm using position estimation. Conventional eye-tracking algorithms are typically suitable only for a single user, and thereby cannot be used for a multi-user system. Even though they can be used to track the eyes of multiple users, their detection accuracy is low and they cannot identify multiple users individually. The proposed algorithm solves these problems and enhances the detection accuracy. Specifically, the proposed algorithm adopts a classifier to detect faces for the red, green, and blue (RGB and depth images. Then, it calculates features based on the histogram of the oriented gradient for the detected facial region to identify multiple users, and selects the template that best matches the users from a pre-determined face database. Finally, the proposed algorithm extracts the final eye positions based on anatomical proportions. Simulation results show that the proposed algorithm improved the average F1 score by up to 0.490, compared with benchmark algorithms.
Uni-Vector-Sensor Dimensionality Reduction MUSIC Algorithm for DOA and Polarization Estimation
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Lanmei Wang
2014-01-01
Full Text Available This paper addresses the problem of multiple signal classification- (MUSIC- based direction of arrival (DOA and polarization estimation and proposes a new dimensionality reduction MUSIC (DR-MUSIC algorithm. Uni-vector-sensor MUSIC algorithm provides estimation for DOA and polarization; accordingly, a four-dimensional peak search is required, which hence incurs vast amount of computation. In the proposed DR-MUSIC method, the signal steering vector is expressed in the product form of arrival angle function matrix and polarization function vector. The MUSIC joint spectrum is converted to the form of Rayleigh-Ritz ratio by using the feature where the 2-norm of polarization function vector is constant. A four-dimensional MUSIC search reduced the dimension to two two-dimensional searches and the amount of computation is greatly decreased. The theoretical analysis and simulation results have verified the effectiveness of the proposed algorithm.
Weng, Yang; Xiao, Wendong; Xie, Lihua
2011-01-01
Distributed estimation of Gaussian mixtures has many applications in wireless sensor network (WSN), and its energy-efficient solution is still challenging. This paper presents a novel diffusion-based EM algorithm for this problem. A diffusion strategy is introduced for acquiring the global statistics in EM algorithm in which each sensor node only needs to communicate its local statistics to its neighboring nodes at each iteration. This improves the existing consensus-based distributed EM algorithm which may need much more communication overhead for consensus, especially in large scale networks. The robustness and scalability of the proposed approach can be achieved by distributed processing in the networks. In addition, we show that the proposed approach can be considered as a stochastic approximation method to find the maximum likelihood estimation for Gaussian mixtures. Simulation results show the efficiency of this approach.
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Yu Huang
Full Text Available Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.
Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng
2015-01-01
Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.