Relevant bibliographies by topics / Multiple systems estimation

Academic literature on the topic 'Multiple systems estimation'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Multiple systems estimation"

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Swapna, Sonti. "Channel Estimation for MIMO Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 201–4. http://dx.doi.org/10.22214/ijraset.2022.39776.

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Abstract: A combination of multiple-input multiple-output (MIMO) systems and orthogonal frequency division multiplexing (OFDM) technologies can be employed in modern wireless communication systems to achieve high data rates and improved spectrum efficiency. For multiple input multiple output (MIMO) systems, this paper provides a Rayleigh fading channel estimation technique based on pilot carriers. The channel is estimated using traditional Least Square (LS) and Minimum Mean Square (MMSE) estimation techniques. The MIMO-OFDM system's performance is measured using the Bit Error Rate (BER) and Mean Square Error (MSE) levels. Keywords: MIMO, MMSE, Channel estimation, BER, OFDM
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González-Coma, José P., Pedro Suárez-Casal, Paula M. Castro, and Luis Castedo. "FDD Channel Estimation Via Covariance Estimation in Wideband Massive MIMO Systems." Sensors 20, no. 3 (February 10, 2020): 930. http://dx.doi.org/10.3390/s20030930.

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A method for channel estimation in wideband massive Multiple-Input Multiple-Output systems using hybrid digital analog architectures is developed. The proposed method is useful for Frequency-Division Duplex at either sub-6 GHz or millimeter wave frequency bands and takes into account the beam squint effect caused by the large bandwidth of the signals. To circumvent the estimation of large channel vectors, the posed algorithm relies on the slow time variation of the channel spatial covariance matrix, thus allowing for the utilization of very short training sequences. This is possibledue to the exploitation of the channel structure. After identifying the channel covariance matrix, the channel is estimated on the basis of the recovered information. To that end, we propose a novel method that relies on estimating the tap delays and the gains as sociated with each path. As a consequence, the proposed channel estimator achieves low computational complexity and significantly reduces the training overhead. Moreover, our numerical simulations show better performance results compared to the minimum mean-squared error solution.
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Napolitano, A., and M. Tanda. "Blind parameter estimation in multiple-access systems." IEEE Transactions on Communications 49, no. 4 (April 2001): 688–98. http://dx.doi.org/10.1109/26.917775.

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Robinson, P. M. "Multiple local whittle estimation in stationary systems." Annals of Statistics 36, no. 5 (October 2008): 2508–30. http://dx.doi.org/10.1214/07-aos545.

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Billings, S. A., and A. K. Swain. "Reconstruction of multiple-input multiple-output non-linear differential equation models from the generalized frequency response function matrix." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 214, no. 1 (February 1, 2000): 35–52. http://dx.doi.org/10.1243/0959651001540500.

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A new algorithm is introduced to identify differential equation models for linear and non-linear multiple-input multiple-output systems from frequency response data using a weighted complex orthogonal estimator. The estimation procedure is progressive beginning with the estimation of the linear terms and then sequentially adding higher-order non-linear terms to build up the model. Simulated examples are included to demonstrate the performance of the new algorithm.
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Zhang, Zhe, Chunyu Wang, and Wenhu Qin. "Semantically Synchronizing Multiple-Camera Systems with Human Pose Estimation." Sensors 21, no. 7 (April 2, 2021): 2464. http://dx.doi.org/10.3390/s21072464.

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Multiple-camera systems can expand coverage and mitigate occlusion problems. However, temporal synchronization remains a problem for budget cameras and capture devices. We propose an out-of-the-box framework to temporally synchronize multiple cameras using semantic human pose estimation from the videos. Human pose predictions are obtained with an out-of-the-shelf pose estimator for each camera. Our method firstly calibrates each pair of cameras by minimizing an energy function related to epipolar distances. We also propose a simple yet effective multiple-person association algorithm across cameras and a score-regularized energy function for improved performance. Secondly, we integrate the synchronized camera pairs into a graph and derive the optimal temporal displacement configuration for the multiple-camera system. We evaluate our method on four public benchmark datasets and demonstrate robust sub-frame synchronization accuracy on all of them.
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Bird, Sheila M., and Ruth King. "Multiple Systems Estimation (or Capture-Recapture Estimation) to Inform Public Policy." Annual Review of Statistics and Its Application 5, no. 1 (March 7, 2018): 95–118. http://dx.doi.org/10.1146/annurev-statistics-031017-100641.

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Khan, Imran, Mohammad Zafar, Majid Ashraf, and Sunghwan Kim. "Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems." Electronics 7, no. 12 (December 3, 2018): 382. http://dx.doi.org/10.3390/electronics7120382.

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Traditional channel estimation algorithms such as minimum mean square error (MMSE) are widely used in massive multiple-input multiple-output (MIMO) systems, but require a matrix inversion operation and an enormous amount of computations, which result in high computational complexity and make them impractical to implement. To overcome the matrix inversion problem, we propose a computationally efficient hybrid steepest descent Gauss–Seidel (SDGS) joint detection, which directly estimates the user’s transmitted symbol vector, and can quickly converge to obtain an ideal estimation value with a few simple iterations. Moreover, signal detection performance was further improved by utilizing the bit log-likelihood ratio (LLR) for soft channel decoding. Simulation results showed that the proposed algorithm had better channel estimation performance, which improved the signal detection by 31.68% while the complexity was reduced by 45.72%, compared with the existing algorithms.
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Rao, Zhushi, Qinzhong Shi, and Ichiro Hagiwara. "Optimal Estimation of Dynamic Loads for Multiple-Input System." Journal of Vibration and Acoustics 121, no. 3 (July 1, 1999): 397–401. http://dx.doi.org/10.1115/1.2893993.

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An optimal method is developed to estimate the dynamic loads for systems subjected to multiple inputs. The method focuses on minimizing the ensemble mean square error of the estimation. First, the inverse system analysis technique is employed to establish the error estimation equation. Then, by applying the noncausal Wiener filtering theory, the optimal estimator of dynamic loads is derived out. Numerical simulation work demonstrates that the method is of a good ability in suppressing the influence of measurement noises on estimation accuracy. Meanwhile, the simulating calculation of load estimation by a conventional method is also performed and the comparison of both results shows that the method proposed in this paper is rather effective and practicable for dynamic load estimation.
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Wang, Yan, and Joanne Thandrayen. "MULTIPLE-RECORD SYSTEMS ESTIMATION USING LATENT CLASS MODELS." Australian & New Zealand Journal of Statistics 51, no. 1 (March 2009): 101–11. http://dx.doi.org/10.1111/j.1467-842x.2008.00531.x.

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Dissertations / Theses on the topic "Multiple systems estimation"

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Miao, H. (Honglei). "Channel estimation and positioning for multiple antenna systems." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514284113.

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Abstract The multiple–input multiple–output (MIMO) technique, applying several transmit and receive antennas in wireless communications, has emerged as one of the most prominent technical breakthroughs of the last decade. Wideband MIMO parameter estimation and its applications to the MIMO orthogonal frequency division multiplexing (MIMO–OFDM) channel estimation and mobile positioning are studied in this thesis. Two practical MIMO channel models, i.e., correlated-receive independent-transmit channel and correlated-transmit-receive channel, and associated space-time parameter estimation algorithms are considered. Thanks to the specified structure of the proposed training signals for multiple transmit antennas, the iterative quadrature maximum likelihood (IQML) algorithm is applied to estimate the time delay and spatial signature for the correlated-receive independent-transmit MIMO channels. For the correlated-transmit-receive MIMO channels, the spatial signature matrix corresponding to a time delay can be further decomposed in such a way that the angle of arrival (AOA) and the angle of departure (AOD) can be estimated simultaneously by the 2-D unitary ESPRIT algorithm. Therefore, the combination of the IQML algorithm and the 2-D unitary ESPRIT algorithm provides a novel solution to jointly estimate the time delay, the AOA and the AOD for the correlated-transmit-receive MIMO channels. It is demonstrated from the numerical examples that the proposed algorithms can obtain good performance at a reasonable cost. Considering the correlated-receive independent-transmit MIMO channels, channel coefficient estimation for the MIMO–OFDM system is studied. Based on the parameters of the correlated-receive independent-transmit MIMO channels, the channel statistics in terms of the correlation matrix are developed. By virtue of the derived channel statistics, a joint spatial-temporal (JST) filtering based MMSE channel estimator is proposed which takes full advantage of the channel correlation properties. The mean square error (MSE) of the proposed channel estimator is analyzed, and its performance is also demonstrated by Monte Carlo computer simulations. It is shown that the proposed JST minimum mean square error (MMSE) channel estimator outperforms the more conventional temporal MMSE channel estimator in terms of the MSE when the signals in the receive antenna array elements are significantly correlated. The closed form bit error probability of the space-time block coded OFDM system with correlation at the receiver is also developed by taking the channel estimation errors and channel statistics, i.e., correlation at the receiver, into account. Mobile positioning in the non-line of sight (NLOS) scenarios is studied. With the knowledge of the time delay, the AOA and the AOD associated with each NLOS propagation path, a novel geometric approach is proposed to calculate the MS's position by only exploiting two NLOS paths. On top of this, the least squares and the maximum likelihood (ML) algorithms are developed to utilize multiple NLOS paths to improve the positioning accuracy. Moreover, the ML algorithm is able to estimate the scatterers' positions as well as those of the MSs. The Cramer-Rao lower bound related to the position estimation in the NLOS scenarios is derived. It is shown both analytically and through computer simulations that the proposed algorithms are able to estimate the mobile position only by employing the NLOS paths.
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Abd, El-Sallam Amar. "Low order channel estimation for CDMA systems." Thesis, Curtin University, 2005. http://hdl.handle.net/20.500.11937/2420.

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New approaches and algorithms are developed for the identification and estimation of low order models that represent multipath channel effects in Code Division Multiple Access (CDMA) communication systems. Based on these parsimonious channel models, low complexity receivers such as RAKE receivers are considered to exploit these propagation effects and enhance the system performance. We consider the scenario where multipath is frequency selective slowly fading and where the channel components including delays and attenuation coefficients are assumed to be constant over one or few signalling intervals. We model the channel as a long FIR-like filter (or a tapped delay line filter) with the number of taps related to the ratio between the channel delay-spread and the chip duration. Due to the high data rate of new CDMA systems, the channel length in terms of the chip duration will be very large. With classical channel estimation techniques this will result in poor estimates of many of the channel parameters where most of them are zero leading to a reduction in the system performance. Unlike classical techniques which estimate directly the channel response given the number of taps or given an estimate of the channel length, the proposed techniques in this work will firstly identify the significant multipath parameters using model selection techniques, then estimate these identified parameters. Statistical tests are proposed to determine whether or not each individual parameter is significant. A low complexity RAKE receiver is then considered based on estimates of these identified parameters only. The level of significance with which we will make this assertion will be controlled based on statistical tests such as multiple hypothesis tests. Frequency and time domain based approaches and model selection techniques are proposed to achieve the above proposed objectives.The frequency domain approach for parsimonious channel estimation results in an efficient implementation of RAKE receivers in DS-CDMA systems. In this approach, we consider a training based strategy and estimate the channel delays and attenuation using the averaged periodogram and modified time delay estimation techniques. We then use model selection techniques such as the sphericity test and multiple hypotheses tests based on F-Statistics to identify the model order and select the significant channel paths. Simulations show that for a pre-defined level of significance, the proposed technique correctly identifies the significant channel parameters and the parsimonious RAKE receiver shows improved statistical as well as computational performance over classical methods. The time domain approach is based on the Bootstrap which is appropriate for the case when the distribution of the test statistics required by the multiple hypothesis tests is unknown. In this approach we also use short training data and model the channel response as an FIR filter with unknown length. Model parameters are then estimated using low complexity algorithms in the time domain. Based on these estimates, bootstrap based multiple hypotheses tests are applied to identify the non-zero coefficients of the FIR filter. Simulation results demonstrate the power of this technique for RAKE receivers in unknown noise environments. Finally we propose adaptive blind channel estimation algorithms for CDMA systems. Using only the spreading code of the user of interest and the received data sequence, four different adaptive blind estimation algorithms are proposed to estimate the impulse response of frequency selective and frequency non-selective fading channels. Also the idea is based on minimum variance receiver techniques. Tracking of a frequency selective varying fading channel is also considered.A blind based hierarchical MDL model selection method is also proposed to select non-zero parameters of the channel response. Simulation results show that the proposed algorithms perform better than previously proposed algorithms. They have lower complexity and have a faster convergence rate. The proposed algorithms can also be applied to the design of adaptive blind channel estimation based RAKE receivers.
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Kane, Roma. "Multiuser TDMA channel estimation." Diss., Columbia, Mo. : University of Missouri-Columbia, 2004. http://hdl.handle.net/10355/5810.

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Thesis (M.S.)--University of Missouri-Columbia, 2004.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (June 30, 2006) Vita. Includes bibliographical references.
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Baştürk, İlhan Özbek Berna. "Iterative Channel Estimation Techniques For Multiple İnput Multiple Output Orthogonal Frequency Division Multiplexing Systems/." [s.l.]: [s.n.], 2007. http://library.iyte.edu.tr/tezler/master/elektrikveelektronikmuh/T000653.pdf.

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Orguner, Umut. "Improved State Estimation For Jump Markov Linear Systems." Phd thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607895/index.pdf.

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This thesis presents a comprehensive example framework on how current multiple model state estimation algorithms for jump Markov linear systems can be improved. The possible improvements are categorized as: -Design of multiple model state estimation algorithms using new criteria. -Improvements obtained using existing multiple model state estimation algorithms. In the first category, risk-sensitive estimation is proposed for jump Markov linear systems. Two types of cost functions namely, the instantaneous and cumulative cost functions related with risk-sensitive estimation are examined and for each one, the corresponding multiple model estate estimation algorithm is derived. For the cumulative cost function, the derivation involves the reference probability method where one defines and uses a new probability measure under which the involved processes has independence properties. The performance of the proposed risk-sensitive filters are illustrated and compared with conventional algorithms using simulations. The thesis addresses the second category of improvements by proposing -Two new online transition probability estimation schemes for jump Markov linear systems. -A mixed multiple model state estimation scheme which combines desirable properties of two different multiple model state estimation methods. The two online transition probability estimators proposed use the recursive Kullback-Leibler (RKL) procedure and the maximum likelihood (ML) criteria to derive the corresponding identification schemes. When used in state estimation, these methods result in an average error decrease in the root mean square (RMS) state estimation errors, which is proved using simulation studies. The mixed multiple model estimation procedure which utilizes the analysis of the single Gaussian approximation of Gaussian mixtures in Bayesian filtering, combines IMM (Interacting Multiple Model) filter and GPB2 (2nd Order Generalized Pseudo Bayesian) filter efficiently. The resulting algorithm reaches the performance of GPB2 with less Kalman filters.
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Sadough, Seyed Mohammad Sajad. "Ultra wideband OFDM systems : channel estimation and improved detection accounting for estimation inaccuracies." Paris 11, 2008. http://www.theses.fr/2008PA112001.

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Les travaux présentés dans cette thèse se situent dans le cadre de la transmission OFDM appliqué au contexte Ultra Large Bande (UWB). L’objectif principal va être l’estimation du canal de propagation et la conception de récepteurs en prenant en compte la connaissance non idéale du canal. On propose d’abord une approche semi-aveugle d’estimation du canal qui bénéficie de la parcimonie du canal UWB dans le domaine des ondelettes pour réduire le nombre de paramètre à estimer. Ensuite, on propose des structures de réception itérative où la conception du détecteur prend en compte la présence des erreurs d’estimation de canal. La détection au sens maximum de vraisemblance (MV) est améliorée en modifiant sa métrique de détection pour le cas où le canal est estimé de manière imparfaite. La métrique MV améliorée ainsi obtenue nous permet de remettre en question le schéma de détection de type turbo MAP dans un contexte BICM et l’adapter à la connaissance imparfaite du canal. De plus, on dérive les débits de coupure atteignables (achievable outage rates) avec le détecteur MV améliorée ou un détecteur MV désadapté (utilisant les estimés de canal comme s’il s’agissait des vraies) qu’on comparera avec un détecteur théorique défini comme étant le meilleur récepteur possible en présence d’erreurs d’estimation de canal. Enfin, un récepteur itératif à complexité réduite basé sur un filtrage MMSE et l’annulation parallèle d’interférence (turbo-PIC) est introduit et ensuite adapté à la connaissance imparfaite du canal. Il est important de souligner que les améliorations proposées dans cette thèse n’impliquent pas une augmentation sensible de la complexité au niveau du récepteur
The aim of this thesis is to study the problem of iterative data detection in an ultra wideband (UWB) OFDM system, where the receiver disposes only of an imperfect (and possibly poor) estimate of the unknown channel parameters. First, we propose an efficient receiver jointly estimating the channel and the transmitted symbols in an iterative manner. This receiver is based on a wavelet representation of the unknown channel and exploits the sparseness property of UWB channels in the wavelet domain to reduce the receiver’s computational complexity. Second, we rely on the statistics characterizing the quality of the channel estimation as a mean to integrate the imperfect channel knowledge into the design of iterative receivers. In this way, we formulate an improved maximum likelihood (ML) detection metric taking into account the presence of channel estimation errors. A modified iterative MAP detector is derived by an appropriate use of this metric. The results are compared to those obtained by using the classical mismatched ML detector, which uses the channel estimate as if it was the perfect channel. Furthermore, we calculate the achieved throughputs associated to both improved and mismatched ML detectors, in terms of achievable outage rates. Finally, we propose an improved low-complexity iterative detector based on soft parallel interference cancellation and linear MMSE filtering where we takes into account the presence of channel estimation errors in the formulation of the detector. The important point is that the performance improvements reported in this thesis are obtained while imposing practically no additional complexity to the receiver
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Allen, Randal T. "Robust estimation and adaptive guidance for multiple UAVs' cooperation." Orlando, Fla. : University of Central Florida, 2009. http://purl.fcla.edu/fcla/etd/CFE0002535.

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Pichette, Alexandre. "Multiple model estimation and detection for adaptive guidance of hybrid systems." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80134.

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The interception of a tactical ballistic missile (TBM) represents a great challenge to the guidance and control community as the maneuver potential of the TBM is almost in the same order as the interceptor missile. The maneuver advantage of the interceptor over the TBM being reduced, a new approach for estimation and guidance for the interception of a maneuvering TBM is presented in this research. The target is assumed to use its maneuvering potential by performing a single evasive bang-bang maneuver. A simple detection algorithm for an evasive maneuver of the target combined with the use of a multiple model estimators like the interactive multiple models (IMM) led to a new adaptive guidance law, the DGL/Adapt law. Simulations results using this approach gave a reduction in the miss distance in comparison with a static guidance law.
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Canolla, Adriano. "Interactive Multiple Model Estimation for Unmanned Aircraft Systems Detect and Avoid." Thesis, Illinois Institute of Technology, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13419136.

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This research presents new methods to apply safety standards to Detect and Avoid (DAA) functions for Unmanned Aircraft Systems (UAS), using maneuvering target tracking and encounter models.

Previous DAA research methods focused on predefined, linear encounter generation. The new estimation and prediction methods in this research are based on the target tracking of maneuvering intruders using Multiple Model Adaptive Estimation and a realistic random encounter generation based on an established encounter model.

When tracking maneuvering intruders there is limited knowledge of changes in intruder behavior beyond the current measurement. The standard Kalman filter (KF) with a single motion model is limited in performance for such problems due to ineffective responses as the target maneuvers. In these cases, state estimation can be improved using MMAE. It is assumed that the current active dynamic model is one of a discrete set of models, each of which is the basis for a separate filter. These filters run in parallel to estimate the states of targets with changing dynamics.

In practical applications of multiple model systems, one of the most popular algorithms for the MMAE is the Interacting Multiple Model (IMM) estimator. In the IMM, the regime switching is modeled by a finite state homogeneous Markov Chain. This is represented by a transition probability matrix characterizing the mode transitions. A Markov Chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the previous event.

This research uses the hazard states estimates (which are derived from DAA standards) to analyze the IMM performance, and then presents a new method to predict the hazard states. To reduce the prediction error, this new method accounts for maneuvering intruders. The new prediction method uses the prediction phase in the IMM algorithm to predict the future intruder aircraft states based on the current and past sensor measurements.

The estimation and prediction methods described in this thesis can help ensure safe encounters between UAS and manned aircraft in the National Airspace System through improvement of the trajectory estimation used to inform the DAA sensor certification process.

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Khayyer, Pardis. "Multiple Model Based Estimation and Control in Large-Scale Interconnected Systems." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1385085547.

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Books on the topic "Multiple systems estimation"

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Babeshko, Lyudmila, Mihail Bich, and Irina Orlova. Econometrics and econometric modeling. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1141216.

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The textbook covers a wide range of issues related to econometric modeling. Regression models are the core of econometric modeling, so the issues of their evaluation, testing of assumptions, adjustment and verification are given a significant place. Various aspects of multiple regression models are included: multicollinearity, dummy variables, and lag structure of variables. Methods of linearization and estimation of nonlinear models are considered. An apparatus for evaluating systems of simultaneous and apparently unrelated equations is presented. Attention is paid to time series models. Detailed solutions of the examples in Excel and the R software environment are included. Meets the requirements of the federal state educational standards of higher education of the latest generation. For undergraduate and graduate students studying in the field of "Economics", the curriculum of which includes the disciplines "Econometrics"," Econometric Modeling","Econometric research".
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Park, Beomjin. Channel estimation in multiple-input multiple-output systems. 2004.

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Ström, Erik G. Direct-sequence code-division multiple access systems: Near-far resistant parameter estimation and detection. 1994.

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Graham, Ian, Therese Cooney, and Dirk De Bacquer. Risk stratification and risk assessment. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199656653.003.0005.

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Cardiovascular disease (CVD) is the biggest cause of death worldwide. The underlying atherosclerosis starts in childhood and is often advanced when it becomes clinically apparent many years later. CVD is manageable: in countries where it has reduced this is due to changes in lifestyle and risk factors and to therapy. Risk factor management reduces mortality and morbidity. In apparently healthy people CVD risk is most frequently the result of multiple interacting risk factors and a risk estimation system such as SCORE can assist in making logical management decisions. In younger people a low absolute risk may conceal a very high relative risk, and use of the relative risk chart or calculation of their ‘risk age’ may help in advising them of the need for intensive life style efforts. All risk estimation systems are relatively crude and require attention to qualifying statements.
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Bhananker, Sanjay, and Paul Bhalla. Burns. Edited by Kirk Lalwani, Ira Todd Cohen, Ellen Y. Choi, and Vidya T. Raman. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190685157.003.0062.

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Burns injuries are a leading cause of morbidity and mortality in children. A significant burn injury affects every major organ system of the body. Initial management focuses on resuscitation along the ABCDE approach, with particular vigilance for the possibility of airway burn causing progressive edema and narrowing. Smoke inhalation is associated with carbon monoxide poisoning and possible cyanide toxicity. Fluid management depends on accurate estimation on the extent of the burn and use of a formula to calculate infusion rate, with careful monitoring of end-organ perfusion. Significant burn injuries are associated with hypermetabolic changes and a catabolic state, and burn victims are at high risk of developing sepsis. Treatment of a burn injury with debridement and skin grafting can involve multiple visits to the operating room with frequent need for sedation and anesthesia. Providers need a thorough understanding of potential anesthetic issues in burn-injured pediatric patients.
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Butz, Martin V., and Esther F. Kutter. Top-Down Predictions Determine Perceptions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0009.

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While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.
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Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.
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Book chapters on the topic "Multiple systems estimation"

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Rao, Nageswara S. V. "A Generic Sensor Fusion Problem: Classification and Function Estimation." In Multiple Classifier Systems, 16–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25966-4_2.

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Ghaderi, Reza, and Terry Windeatt. "Least Squares and Estimation Measures via Error Correcting Output Code." In Multiple Classifier Systems, 148–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_15.

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Kryszczuk, Krzysztof, and Paul Hurley. "Estimation of the Number of Clusters Using Multiple Clustering Validity Indices." In Multiple Classifier Systems, 114–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12127-2_12.

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Krasotkina, Olga, Oleg Seredin, and Vadim Mottl. "Supervised Selective Combination of Diverse Object-Representation Modalities for Regression Estimation." In Multiple Classifier Systems, 89–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20248-8_8.

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Monticelli, A. "Multiple Bad Data Processing Techniques." In State Estimation in Electric Power Systems, 227–66. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4999-4_9.

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Monticelli, A. "Estimation Based on Multiple Scans of Measurements." In State Estimation in Electric Power Systems, 283–312. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4999-4_11.

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Morimoto, Takashi, and Ikuhisa Mitsugami. "3D Pose Estimation Using Multiple Asynchronous Cameras." In Smart Innovation, Systems and Technologies, 39–50. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8944-7_3.

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Rychlicki-Kicior, Krzysztof, and Bartłomiej Stasiak. "Metaheuristic Optimization of Multiple Fundamental Frequency Estimation." In Advances in Intelligent Systems and Computing, 307–14. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02309-0_33.

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Gomes-Neto, Severino P., and Bruno M. de Carvalho. "MUGEN RANSAC - MUltiple GENerator Applied to Motion Estimation." In Advanced Information Systems Engineering, 876–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_106.

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D’Orazio, Leandro, Claudio Sacchi, and Massimo Donelli. "Adaptive Channel Estimation for STBC-OFDM Systems Based on Nature-Inspired Optimization Strategies." In Multiple Access Communications, 188–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15428-7_19.

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Conference papers on the topic "Multiple systems estimation"

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Wen-Yan Chang and Chu-Song Chen. "Pose estimation for multiple camera systems." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334517.

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Rao, Chinmay, Kushal Mukherjee, Soumik Sarkar, and Asok Ray. "Estimation of multiple parameters in dynamical systems." In 2008 American Control Conference (ACC '08). IEEE, 2008. http://dx.doi.org/10.1109/acc.2008.4586671.

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Srang, Sarot, and Masaki Yamakita. "Estimation of systems with multiple sliding surfaces." In 2013 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2013. http://dx.doi.org/10.1109/sii.2013.6776611.

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Shen, Truman, and Faroog Ibrahim. "Interacting multiple model road curvature estimation." In 2012 15th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/itsc.2012.6338884.

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Kocherry, Donna L., Shanglin Ye, and Elias Aboutanios. "Estimating Parameters of Multiple Damped Complex Sinusoids with Model Order Estimation." In 2016 IEEE International Workshop on Signal Processing Systems (SiPS). IEEE, 2016. http://dx.doi.org/10.1109/sips.2016.23.

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Gad, Ahmed F., Ahmed M. Hamad, and Khalid M. Amin. "Crowd density estimation using multiple features categories and multiple regression models." In 2017 12th International Conference on Computer Engineering and Systems (ICCES). IEEE, 2017. http://dx.doi.org/10.1109/icces.2017.8275346.

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Yoo, Sungjoo, Kyoungseok Rha, Youngchul Cho, Jinyong Jung, and Kiyoung Choi. "Performance estimation of multiple-cache IP-based systems." In the eighth international workshop. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/334012.334027.

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Liu, Yanjun, and Rui Ding. "Partially coupled estimation algorithm for discrete-time multiple-input multiple-output systems." In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6244335.

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Hideaki Takahashi, Teruo Yamaguchi, and Hiroshi Harada. "Velocity estimation of multiple objects using oculomotor system." In 2008 International Conference on Control, Automation and Systems (ICCAS). IEEE, 2008. http://dx.doi.org/10.1109/iccas.2008.4694283.

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Sa'id, Waladin K., Omar F. Jasim, and Safanah M. Raafat. "Estimation of Induction Furnace Charge Temperature Using Multiple Model Adaptive Estimator (MMAE)." In 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2019. http://dx.doi.org/10.1109/ssd.2019.8893282.

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Reports on the topic "Multiple systems estimation"

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Bonfil, David J., Daniel S. Long, and Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7696531.bard.

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To reduce financial risk and N losses to the environment, fertilization methods are needed that improve NUE and increase the quality of wheat. In the literature, ample attention is given to grid-based and zone-based soil testing to determine the soil N available early in the growing season. Plus, information is available on in-season N topdressing applications as a means of improving GPC. However, the vast majority of research has focused on wheat that is grown under N limiting conditions in sub-humid regions and irrigated fields. Less attention has been given to wheat in dryland that is water limited. The objectives of this study were to: (1) determine accuracy in determining GPC of HRSW in Israel and SWWW in Oregon using on-combine optical sensors under field conditions; (2) develop a quantitative relationship between image spectral reflectance and effective crop physiological parameters; (3) develop an operational precision N management procedure that combines variable-rate N recommendations at planting as derived from maps of grain yield, GPC, and test weight; and at mid-season as derived from quantitative relationships, remote sensing, and the DSS; and (4) address the economic and technology-transfer aspects of producers’ needs. Results from the research suggest that optical sensing and the DSS can be used for estimating the N status of dryland wheat and deciding whether additional N is needed to improve GPC. Significant findings include: 1. In-line NIR reflectance spectroscopy can be used to rapidly and accurately (SEP <5.0 mg g⁻¹) measure GPC of a grain stream conveyed by an auger. 2. On-combine NIR spectroscopy can be used to accurately estimate (R² < 0.88) grain test weight across fields. 3. Precision N management based on N removal increases GPC, grain yield, and profitability in rainfed wheat. 4. Hyperspectral SI and partial least squares (PLS) models have excellent potential for estimation of biomass, and water and N contents of wheat. 5. A novel heading index can be used to monitor spike emergence of wheat with classification accuracy between 53 and 83%. 6. Index MCARI/MTVI2 promises to improve remote sensing of wheat N status where water- not soil N fertility, is the main driver of plant growth. Important features include: (a) computable from commercial aerospace imagery that include the red edge waveband, (b) sensitive to Chl and resistant to variation in crop biomass, and (c) accommodates variation in soil reflectance. Findings #1 and #2 above enable growers to further implement an efficient, low cost PNM approach using commercially available on-combine optical sensors. Finding #3 suggests that profit opportunities may exist from PNM based on information from on-combine sensing and aerospace remote sensing. Finding #4, with its emphasis on data retrieval and accuracy, enhances the potential usefulness of a DSS as a tool for field crop management. Finding #5 enables land managers to use a DSS to ascertain at mid-season whether a wheat crop should be harvested for grain or forage. Finding #6a expands potential commercial opportunities of MS imagery and thus has special importance to a majority of aerospace imaging firms specializing in the acquisition and utilization of these data. Finding #6b on index MCARI/MVTI2 has great potential to expand use of ground-based sensing and in-season N management to millions of hectares of land in semiarid environments where water- not N, is the main determinant of grain yield. Finding #6c demonstrates that MCARI/MTVI2 may alleviate the requirement of multiple N-rich reference strips to account for soil differences within farm fields. This simplicity will be less demanding of grower resources, promising substantially greater acceptance of sensing technologies for in-season N management.
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Fox, Emily B. Bayesian Tracking within a Feedback Sensing Environment: Estimating Interacting, Spatially Constrained Complex Dynamical Systems from Multiple Sources of Controllable Devices. Fort Belvoir, VA: Defense Technical Information Center, July 2014. http://dx.doi.org/10.21236/ada609275.

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Tosi, R., R. Codina, J. Principe, R. Rossi, and C. Soriano. D3.3 Report of ensemble based parallelism for turbulent flows and release of solvers. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.06.

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In this work we focus on reducing the wall clock time required to compute statistical estimators of highly chaotic incompressible flows on high performance computing systems. Our approach consists of replacing a single long-term simulation by an ensemble of multiple independent realizations, which are run in parallel with different initial conditions. A failure probability convergence criteria must be satisfied by the statistical estimator of interest to assess convergence. Its error analysis leads to the identification of two error contributions: the initialization bias and the statistical error. We propose an approach to systematically detect the burn-in time in order to minimize the initialization bias, accompanied by strategies to reduce simulation cost. The framework is validated on two very high Reynolds number obstacle problems of wind engineering interest in a high performance computing environment.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Mizrach, Amos, Michal Mazor, Amots Hetzroni, Joseph Grinshpun, Richard Mankin, Dennis Shuman, Nancy Epsky, and Robert Heath. Male Song as a Tool for Trapping Female Medflies. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7586535.bard.

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This interdisciplinaray work combines expertise in engineering and entomology in Israel and the US, to develop an acoustic trap for mate-seeking female medflies. Medflies are among the world's most economically harmful pests, and monitoring and control efforts cost about $800 million each year in Israel and the US. Efficient traps are vitally important tools for medfly quarantine and pest management activities; they are needed for early detection, for predicting dispersal patterns and for estimating medfly abundance within infested regions. Early detection facilitates rapid response to invasions, in order to contain them. Prediction of dispersal patterns facilitates preemptive action, and estimates of the pests' abundance lead to quantification of medfly infestations and control efforts. Although olfactory attractants and traps exist for capturing male and mated female medflies, there are still no satisfactorily efficient means to attract and trap virgin and remating females (a significant and dangerous segment of the population). We proposed to explore the largely ignored mechanism of female attraction to male song that the flies use in courtship. The potential of such an approach is indicated by studies under this project. Our research involved the identification, isolation, and augmentation of the most attractive components of male medfly songs and the use of these components in the design and testing of traps incorporating acoustic lures. The project combined expertise in acoustic engineering and instrumentation, fruit fly behavior, and integrated pest management. The BARD support was provided for 1 year to enable proof-of-concept studies, aimed to determine: 1) whether mate-seeking female medflies are attracted to male songs; and 2) over what distance such attraction works. Male medfly calling song was recorded during courtship. Multiple acoustic components of male song were examined and tested for synergism with substrate vibrations produced by various surfaces, plates and loudspeakers, with natural and artificial sound playbacks. A speaker-funnel system was developed that focused the playback signal to reproduce as closely as possible the near-field spatial characteristics of the sounds produced by individual males. In initial studies, the system was tasted by observing the behavior of females while the speaker system played songs at various intensities. Through morning and early afternoon periods of peak sexual activity, virgin female medflies landed on a sheet of filter paper at the funnel outlet and stayed longer during broadcasting than during the silent part of the cycle. In later studies, females were captured on sticky paper at the funnel outlet. The mean capture rates were 67 and 44%, respectively, during sound emission and silent control periods. The findings confirmed that female trapping was improved if a male calling song was played. The second stage of the research focused on estimating the trapping range. Initial results indicated that the range possibly extended to 70 cm, but additional, verification tests remain to be conducted. Further studies are planned also to consider effects of combining acoustic and pheromonal cues.
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