Academic literature on the topic 'Multiple mean estimation'
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Journal articles on the topic "Multiple mean estimation"
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Green, Edwin J., William E. Strawderman, Ralph L. Amateis, and Gregory A. Reams. "Improved Estimation for Multiple Means with Heterogeneous Variances." Forest Science 51, no. 1 (2005): 1–6. http://dx.doi.org/10.1093/forestscience/51.1.1.
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Abstract Two new estimators are presented for use in situations where simultaneous estimation of more than two sample means is required and alternative, possibly biased information is available. The new estimators are modifications to an older estimator by Green and Strawderman. The latter estimator assumed homogeneous variances, whereas the new ones are designed for the more usual case of heterogeneous variances among the sample means. In simulation experiments, the new estimators yielded superior performance to that of the ordinary sample mean vector (X). Surprisingly, the estimator designed to dominate X under precision-weighted loss was apparently the best estimator, even under nonprecision-weighted loss. Use of the new estimators should allow foresters to achieve considerable savings in estimation precision. FOR. SCI. 51(1):1–6.
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Liu, Huan Bin. "Additive-Accelerated Mean Regression Model for Multiple Type Recurrent Events." Advanced Engineering Forum 6-7 (September 2012): 93–96. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.93.
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Recurrent events data is often observed in applied research fields like biostatistics, clinical experiment, and so on. In this paper, an additive-accelerated mean regression model is established for multiple type recurrent events data, and the estimation methods of unknown parameter and non-parameter function based on the idea of estimating equation are given.
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Sedeeq, Bekhal Samad, Hogr Mohammed Qader, Azhy Akram Aziz, and Dlshad Mahmood Saleh. "Implementing a New Scale Technique in the M-Estimation Method to Estimate Parameters of Multiple Linear Regression: Simulation Study." Tikrit Journal of Administrative and Economic Sciences 19, no. 64, 1 (2023): 712–25. http://dx.doi.org/10.25130/tjaes.19.64.1.38.
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The goal of this study is to develop a new technique for estimating the parameters of a multiple linear regression by using M-estimation based on scale estimator to handle the influence of outlier values. In order to get new estimators, the root mean square error (RMSE) criterion is used to check the efficiency between the new technique and the classical method. The research showed that the new technique (M-estimation based on scale estimator) yields more accurate parameter estimates than the traditional approach (OLS) in all simulated cases.
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Pinette, M. G., Y. Pan, S. G. Pinette, J. Blackstone, J. Garrett, and A. Cartin. "Estimation of fetal weight: mean value from multiple formulas." Journal of Ultrasound in Medicine 18, no. 12 (1999): 813–17. http://dx.doi.org/10.7863/jum.1999.18.12.813.
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Kim, Jae-Hee, and Sooy-Oung Cheon. "Multiple Change-Point Estimation of Air Pollution Mean Vectors." Korean Journal of Applied Statistics 22, no. 4 (2009): 687–95. http://dx.doi.org/10.5351/kjas.2009.22.4.687.
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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 (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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Lee, Andrew Sanghyun, Yuandi Wu, Stephen Andrew Gadsden, and Mohammad AlShabi. "Interacting Multiple Model Estimators for Fault Detection in a Magnetorheological Damper." Sensors 24, no. 1 (2023): 251. http://dx.doi.org/10.3390/s24010251.
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This paper proposes a novel estimator for the purpose of fault detection and diagnosis. The interacting multiple model (IMM) strategy is effective for estimating the behaviour of systems with multiple operating modes. Each mode corresponds to a distinct mathematical model and is subject to a filtering process. This paper applies various model-based filters in combination with the IMM strategy. One such estimator employs the recently introduced extended sliding innovation filter (ESIF) known as the IMM-ESIF. The ESIF is an extension of the sliding innovation filter for nonlinear systems based on the sliding mode concept. In the presence of modeling uncertainties, the ESIF has been proven to be more robust compared to methods such as the extended Kalman filter (EKF). The novel IMM-ESIF strategy is also compared with the IMM strategy, which incorporates the unscented Kalman filter (UKF), referred to herein as IMM-UKF. While EKF uses Taylor series approximation to linearize the system model, the UKF uses sigma point to calculate the system’s mean and covariance. The methods were applied to an experimental magnetorheological (MR) damper setup, which was designed for testing control and estimation theory. Magnetorheological dampers exhibit a diverse array of applications in the automotive and aerospace sectors, with particular relevance to attenuating vibrations through adaptive suspension systems. Applied to a magnetorheological (MR) damper with distinct operating modes determined by the damper’s current, the results showcase the effectiveness of IMM-ESIF. In mixed operational conditions, IMM-ESIF demonstrates a notable 80% to 90% reduction in estimation error compared to its counterparts. Furthermore, it exhibits a 4% to 5% enhancement in correctly classifying operational modes, establishing IMM-ESIF as a promising and efficient alternative for adaptive estimation in electromechanical systems. The improved accuracy in estimating the system’s behaviour, even amidst uncertainties and mixed operational scenarios, signifies the potential of IMM-ESIF to significantly enhance the overall robustness and efficiency of estimations.
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Omar Gan, Sarimah, and Sabri Ahmad. "ESTIMATION OF TRADE BALANCE USING MULTIPLE LINEAR REGRESSION MODEL." Labuan Bulletin of International Business and Finance (LBIBF) 16 (November 30, 2018): 44–52. http://dx.doi.org/10.51200/lbibf.v16i.1642.
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This study aims to evaluate the performance of multiple linear regression in estimating trade balance, so that a regression model for estimating the trade balance can be developed based on the important variables that have been identified. The performance of four regression methods including enter, stepwise regression, backward deletion, and forward selection is measured by mean absolute error, standard deviation, and Pearson correlation at the validation stage. The study concludes that multiple linear regression model developed by stepwise method is the best model for the trade balance estimation. The model considers the following six significant variables: Exports of palm oil, imports of tubes, pipes, and fittings of iron or steel, exports of crude petroleum, imports of petroleum products, exports of plywood plain, and imports of rice. The regression model achieves a moderate value of model estimated accuracy (76.10%), mean absolute error (0.257), standard deviation (0.308), and linear correlation (0.851).
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Neubauer, Jirí, and Vítezslav Veselý. "Detection of multiple changes in mean by sparse parameter estimation." Nonlinear Analysis: Modelling and Control 18, no. 2 (2013): 177–90. http://dx.doi.org/10.15388/na.18.2.14021.
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The contribution is focused on detection of multiple changes in the mean in a onedimensional stochastic process by sparse parameter estimation from an overparametrized model. The authors’ approach to change point detection differs entirely from standard statistical techniques. A stochastic process residing in a bounded interval with changes in the mean is estimated using dictionary (a family of functions, the so-called atoms, which are overcomplete in the sense of being nearly linearly dependent) and consisting of Heaviside functions. Among all possible representations of the process we want to find a sparse one utilizing a significantly reduced number of atoms. This problem can be solved by ℓ1-minimization. The basis pursuit algorithm is used to get sparse parameter estimates. In this contribution the authors calculate empirical probability of successful change point detection as a function depending on the number of change points and the level of standard deviation of additive white noise of the stochastic process. The empirical probability was computed by simulations where locations of change points were chosen randomly from uniform distribution. The authors’ approach is compared with LASSO algorithm, ℓ1 trend filtering and selected statistical methods. Such probability decreases with increasing number of change points and/or standard deviation of white noise. The proposed method was applied on the time series of nuclear magnetic response during the drilling of a well.
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Najlaa Ali Dhumad and Abbas Lafta Kneehr. "Comparison Of Some Estimation Methods For The Estimators Of Marshall Olkin Distribution With Simulation." Bilangan : Jurnal Ilmiah Matematika, Kebumian dan Angkasa 2, no. 4 (2024): 234–47. http://dx.doi.org/10.62383/bilangan.v2i4.204.
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The research comprised multiple simulated tests to determine the relationship between (sample size, distribution parameter value, estimation method, and pollution indivuduales). The experimental findings indicate that the estimator is influenced by sample size, the value of distribution parameter, estimation method, and pollution indivuduales. The results of the mean square error analysis indicate that (robust estimation method) produces the best results with the lowest mean square error, and the best estimation method was (191) of (243) simulation experiments. Additional statistical distributions with additional factors can be performed to demonstrate additional results.
Dissertations / Theses on the topic "Multiple mean estimation"
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Potter, Chris. "Modeling Channel Estimation Error in Continuously Varying MIMO Channels." International Foundation for Telemetering, 2007. http://hdl.handle.net/10150/604490.
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ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada<br>The accuracy of channel estimation plays a crucial role in the demodulation of data symbols sent across an unknown wireless medium. In this work a new analytical expression for the channel estimation error of a multiple input multiple output (MIMO) system is obtained when the wireless medium is continuously changing in the temporal domain. Numerical examples are provided to illustrate our findings.
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Feng, Limin. "JAMES-STEIN TYPE COMPOUND ESTIMATION OF MULTIPLE MEAN RESPONSE FUNCTIONS AND THEIR DERIVATIVES." UKnowledge, 2013. http://uknowledge.uky.edu/statistics_etds/6.
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Charnigo and Srinivasan originally developed compound estimators to nonparametrically estimate mean response functions and their derivatives simultaneously when there is one response variable and one covariate. The compound estimator maintains self consistency and almost optimal convergence rate. This dissertation studies, in part, compound estimation with multiple responses and/or covariates. An empirical comparison of compound estimation, local regression and spline smoothing is included, and near optimal convergence rates are established in the presence of multiple covariates.
James and Stein proposed an estimator of the mean vector of a p dimensional multivariate normal distribution, which produces a smaller risk than the maximum likelihood estimator if p is at least 3. In this dissertation, we also extend their idea to a nonparametric regression setting. More specifically, we present Steinized local regression estimators of p mean response functions and their derivatives. We consider different covariance structures for the error terms, and whether or not a known upper bound for the estimation bias is assumed.
We also apply Steinization to compound estimation, considering the application of Steinization to both pointwise estimators (for example, as obtained through local regression) and weight functions.
Finally, the new methodology introduced in this dissertation will be demonstrated on numerical data illustrating the outcomes of a laboratory experiment in which radiation induces nanoparticles to scatter evanescent waves. The patterns of scattering, as represented by derivatives of multiple mean response functions, may be used to classify nanoparticles on their sizes and structures.
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Fermanian, Jean-Baptiste. "High dimensional multiple means estimation and testing with applications to machine learning." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM035.
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Nous étudions dans cette thèse l'influence de la grande dimension dans des problèmes de test et d'estimation. Notre analyse porte sur la dépendance en la dimension de la vitesse de séparation d'un test de proximité et du risque quadratique de l'estimation multiples de vecteurs. Nous complétons les résultats existants en étudiant ces dépendances dans le cas de distributions non isotropes. Pour de telles distributions, le rôle de la dimension est alors joué par des notions de dimension effective définies à partir de la covariance des distributions. Ce cadre permet d'englober des données de dimension infinie comme le kernel mean embedding, outil de machine learning que nous chercherons à estimer. A l'aide de cette analyse, nous construisons des méthodes d'estimation simultanée de vecteurs moyennes de différentes distributions à partir d'échantillons indépendants de chacune. Ces estimateurs ont de meilleures performances théorique et pratique relativement aux moyennes empiriques, en particulier dans des situations défavorables où la dimension (effective) est grande. Ces méthodes utilisent explicitement ou implicitement la relative facilité du test par rapport à l'estimation. Elles reposent sur la construction d'estimateurs de distances et de moments de la covariance pour lesquels nous fournissons des bornes de concentration non asymptotiques. Un intérêt particulier est porté à l'étude de données bornées pour lesquels une analyse spécifique est nécessaire. Nos méthodes sont accompagnées d'une analyse minimax justifiant leur optimalité. Dans une dernière partie, nous proposons une interprétation du mécanisme d'attention utilisé dans les réseaux de neurones Transformers comme un problème d'estimation multiple de vecteurs. Dans un cadre simplifié, ce mécanisme partage des idées similaires avec nos approches et nous mettons en évidence son effet de débruitage en grande dimension<br>In this thesis, we study the influence of high dimension in testing and estimation problems. We analyze the dimension dependence of the separation rate of a closeness test and of the quadratic risk of multiple vector estimation. We complement existing results by studying these dependencies in the case of non-isotropic distributions. For such distributions, the role of dimension is played by notions of effective dimension defined from the covariance of the distributions. This framework covers infinite-dimensional data such as kernel mean embedding, a machine learning tool we will be seeking to estimate. Using this analysis, we construct methods for simultaneously estimating mean vectors of different distributions from independent samples of each. These estimators perform better theoretically and practically than the empirical mean in unfavorable situations where the (effective) dimension is large. These methods make explicit or implicit use of the relative ease of testing compared with estimation. They are based on the construction of estimators of distances and moments of covariance, for which we provide non-asymptotic concentration bounds. Particular interest is given to the study of bounded data, for which a specific analysis is required. Our methods are accompanied by a minimax analysis justifying their optimality. In a final section, we propose an interpretation of the attention mechanism used in Transformer neural networks as a multiple vector estimation problem. In a simplified framework, this mechanism shares similar ideas with our approaches, and we highlight its denoising effect in high dimension
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Tran, Nguyen Duy. "Performance bounds in terms of estimation and resolution and applications in array processing." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00777503.
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This manuscript concerns the performance analysis in signal processing and consists into two parts : First, we study the lower bounds in characterizing and predicting the estimation performance in terms of mean square error (MSE). The lower bounds on the MSE give the minimum variance that an estimator can expect to achieve and it can be divided into two categories depending on the parameter assumption: the so-called deterministic bounds dealing with the deterministic unknown parameters, and the so-called Bayesian bounds dealing with the random unknown parameter. Particularly, we derive the closed-form expressions of the lower bounds for two applications in two different fields: (i) The first one is the target localization using the multiple-input multiple-output (MIMO) radar in which we derive the lower bounds in the contexts with and without modeling errors, respectively. (ii) The other one is the pulse phase estimation of X-ray pulsars which is a potential solution for autonomous deep space navigation. In this application, we show the potential universality of lower bounds to tackle problems with parameterized probability density function (pdf) different from classical Gaussian pdf since in X-ray pulse phase estimation, observations are modeled with a Poisson distribution. Second, we study the statistical resolution limit (SRL) which is the minimal distance in terms of the parameter of interest between two signals allowing to correctly separate/estimate the parameters of interest. More precisely, we derive the SRL in two contexts: array processing and MIMO radar by using two approaches based on the estimation theory and information theory. We also present in this thesis the usefulness of SRL in optimizing the array system.
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Thomas, Robin Rajan. "Optimisation of adaptive localisation techniques for cognitive radio." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/27076.
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Spectrum, environment and location awareness are key characteristics of cognitive radio (CR). Knowledge of a user’s location as well as the surrounding environment type may enhance various CR tasks, such as spectrum sensing, dynamic channel allocation and interference management. This dissertation deals with the optimisation of adaptive localisation techniques for CR. The first part entails the development and evaluation of an efficient bandwidth determination (BD) model, which is a key component of the cognitive positioning system. This bandwidth efficiency is achieved using the Cramer-Rao lower bound derivations for a single-input-multiple-output (SIMO) antenna scheme. The performances of the single-input-single-output (SISO) and SIMO BD models are compared using three different generalised environmental models, viz. rural, urban and suburban areas. In the case of all three scenarios, the results reveal a marked improvement in the bandwidth efficiency for a SIMO antenna positioning scheme, especially for the 1×3 urban case, where a 62% root mean square error (RMSE) improvement over the SISO system is observed. The second part of the dissertation involves the presentation of a multiband time-of arrival (TOA) positioning technique for CR. The RMSE positional accuracy is evaluated using a fixed and dynamic bandwidth availability model. In the case of the fixed bandwidth availability model, the multiband TOA positioning model is initially evaluated using the two-step maximum-likelihood (TSML) location estimation algorithm for a scenario where line-of-sight represents the dominant signal path. Thereafter, a more realistic dynamic bandwidth availability model has been proposed, which is based on data obtained from an ultra-high frequency spectrum occupancy measurement campaign. The RMSE performance is then verified using the non-linear least squares, linear least squares and TSML location estimation techniques, using five different bandwidths. The proposed multiband positioning model performs well in poor signal-to-noise ratio conditions (-10 dB to 0 dB) when compared to a single band TOA system. These results indicate the advantage of opportunistic TOA location estimation in a CR environment.<br>Dissertation (MEng)--University of Pretoria, 2012.<br>Electrical, Electronic and Computer Engineering<br>unrestricted
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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Hunter, Brandon. "Channel Probing for an Indoor Wireless Communications Channel." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/64.
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The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter allows spatial diversity at the transmitter along with the receiver to be simulated. The process of going from raw measurement data to discrete arrivals and then to clustered arrivals is analyzed. Many possible errors associated with discrete arrival processing are discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated.
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"Distributed estimation in wireless sensor networks under a semi-orthogonal multiple access technique." Thesis, 2014. http://hdl.handle.net/10388/ETD-2014-09-1753.
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This thesis is concerned with distributed estimation in a wireless sensor network (WSN) with analog transmission. For a scenario in which a large number of sensors are deployed under a limited bandwidth constraint, a semi-orthogonal multiple-access channelization (MAC) approach is proposed to provide transmission of observations from K sensors to a fusion center (FC) via N orthogonal channels, where K≥N. The proposed semi-orthogonal MAC can be implemented with either fixed sensor grouping or adaptive sensor grouping.
The mean squared error (MSE) is adopted as the performance criterion and it is first studied under equal power allocation. The MSE can be expressed in terms of two indicators: the channel noise suppression capability and the observation noise suppression capability. The fixed version of the semi-orthogonal MAC is shown to have the same channel noise suppression capability and two times the observation noise suppression capability when compared to the orthogonal MAC under the same bandwidth resource. For the adaptive version, the performance improvement of the semi-orthogonal MAC over the orthogonal MAC is even more significant. In fact, the semi-orthogonal MAC with adaptive sensor grouping is shown to perform very close to that of the hybrid MAC, while requiring a much smaller amount of feedback.
Another contribution of this thesis is an analysis of the behavior of the average MSE in terms of the number of sensors, namely the scaling law, under equal power allocation. It is shown that the proposed semi-orthogonal MAC with adaptive sensor grouping can achieve the optimal scaling law of the analog WSN studied in this thesis.
Finally, improved power allocations for the proposed semi-orthogonal MAC are investigated. First, the improved power allocations in each sensor group for different scenarios are provided. Then an optimal solution of power allocation among sensor groups is obtained by the convex optimization theory, and shown to outperform equal power allocation. The issue of balancing between the performance improvement and extra feedback required by the improved power allocation is also thoroughly discussed.
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Tenenbaum, Adam. "MSE-based Linear Transceiver Designs for Multiuser MIMO Wireless Communications." Thesis, 2011. http://hdl.handle.net/1807/31954.
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This dissertation designs linear transceivers for the multiuser downlink in multiple-input multiple-output (MIMO) systems. The designs rely on an uplink/downlink duality for the mean squared error (MSE) of each individual data stream.
We first consider the design of transceivers assuming channel state information (CSI) at the transmitter. We consider minimization of the sum-MSE over all users subject to a sum power constraint on each transmission. Using MSE duality, we solve a computationally simpler convex problem in a virtual uplink. The transformation back to the downlink is simplified by our demonstrating the equality of the optimal power allocations in the uplink and downlink.
Our second set of designs maximize the sum throughput for all users. We establish a series of relationships linking MSE to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. We show that minimizing the product of MSE matrix determinants is equivalent to sum-rate maximization, but we demonstrate that this problem does not admit a computationally efficient solution. We simplify the problem by minimizing the product of mean squared errors (PMSE) and propose an iterative algorithm based on alternating optimization with near-optimal performance.
The remainder of the thesis considers the more practical case of imperfections in CSI. First, we consider the impact of delay and limited-rate feedback. We propose a system which employs Kalman prediction to mitigate delay; feedback rate is limited by employing adaptive delta modulation. Next, we consider the robust design of the sum-MSE and PMSE minimizing precoders with delay-free but imperfect estimates of the CSI. We extend the MSE duality to the case of imperfect CSI, and consider a new optimization problem which jointly optimizes the energy allocations for training and data stages along with the sum-MSE/PMSE minimizing transceivers. We prove the separability of these two problems when all users have equal estimation error variances, and propose several techniques to address the more challenging case of unequal estimation errors.
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Dutta, Amit Kumar. "Transceiver Design Based on the Minimum-Error-Probability Framework for Wireless Communication Systems." Thesis, 2015. http://etd.iisc.ac.in/handle/2005/3732.
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Parameter estimation and signal detection are the two key components of a wireless communication system. They directly impact the bit-error-ratio (BER) performance of the system. Several criteria have been successfully applied for parameter estimation and signal detection. They include maximum likelihood (ML), maximum a-posteriori probability (MAP), least square (LS) and minimum mean square error (MMSE) etc. In the linear detection framework, linear MMSE (LMMSE) and LS are the most popular ones. Nevertheless, these criteria do not necessarily minimize the BER, which is one of the key aspect of any communication receiver design. Thus, minimization of BER is tantamount to an important design criterion for a wireless receiver, the minimum bit/symbol error ratio (MBER/MSER). We term this design criterion as the minimum-error-probability (MEP). In this thesis, parameter estimation and signal detection have been extensively studied based on the MEP framework for various unexplored scenar-ios of a wireless communication system. Thus, this thesis has two broad categories of explorations, first parameter estimation and then signal detection. Traditionally, the MEP criterion has been well studied in the context of the discrete signal detection in the last one decade, albeit we explore this framework for the continuous parameter es-timation. We first use this framework for channel estimation in a frequency flat fading single-input single-output (SISO) system and then extend this framework to the carrier frequency offset (CFO) estimation of multi-user MIMO OFDM system. We observe a reasonably good SNR improvement to the tune of 1 to 2.5 dB at a fixed BER (tentatively at 10−3). In this context, it is extended to the scenario of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM with pa-rameter estimation error statistics obtained from LMMSE only and checked its effect at the equalizer design using MEP and LMMSE criteria. In the second exploration of the MEP criterion, it is explored for signal detection in the context of MIMO-relay and MIMO systems. Various low complexity solutions are proposed to alleviate the effect of high computational complexity for the MIMO-relay. We also consider various configurations of relay like cognitive, parallel and multi-hop relaying. We also propose a data trans-mission scheme with a rate of 1/Ns (Ns is the number of antennas at the transmitter) with the help of the MEP criterion to design various components. In all these cases, we obtain considerable BER improvement compared to the existing solutions.
Books on the topic "Multiple mean estimation"
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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.
Book chapters on the topic "Multiple mean estimation"
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Cetintav, Bekir, Gozde Ulutagay, Selma Gurler, and Neslihan Demirel. "Mean Estimation Based on FWA Using Ranked Set Sampling with Single and Multiple Rankers." In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40581-0_64.
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Abdelhady, Aya Salama, Aboul Ella Hassanien, Yasser Mahmoud Awad, Moataz El-Gayar, and Aly Fahmy. "Automatic Sheep Weight Estimation Based on K-Means Clustering and Multiple Linear Regression." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99010-1_50.
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Carraro, Marco, Matteo Munaro, Alina Roitberg, and Emanuele Menegatti. "Improved Skeleton Estimation by Means of Depth Data Fusion from Multiple Depth Cameras." In Intelligent Autonomous Systems 14. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-48036-7_85.
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Brüningk, Sarah C., and Gibin G. Powathil. "Modelling Direct and Indirect Effects of Radiation: Experimental, Clinical and Environmental Implications." In NATO Science for Peace and Security Series A: Chemistry and Biology. Springer Netherlands, 2022. http://dx.doi.org/10.1007/978-94-024-2101-9_5.
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AbstractRadiotherapy is a commonly used treatment for cancer and is usually given in varying doses. Mathematical modelling of radiation effects traditionally means the modelling or estimation of cell-kill due to its direct exposure to irradiation and sometimes ignoring other multiple direct/indirect effects. However, advances in molecular biology have expanded this classical view and it is now realized that in addition to cell-death, signals produced by irradiated cells can further influence the behavior of non-irradiated cells or organisms in several ways. Consequently, it has now wider implications in multiple areas making it relevant for further exploration, both experimentally and mathematically. Here, we provide a brief overview of a hybrid multiscale mathematical model to study the direct and indirect effects of radiation and its implications in clinical radiotherapy, experimental settings and radiation protection.
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Legaz-Aparicio, Álvar-Ginés, Rafael Verdú-Monedero, Juan Morales-Sánchez, Jorge Larrey-Ruiz, and Jesús Angulo. "Detection of Retinal Vessel Bifurcations by Means of Multiple Orientation Estimation Based on Regularized Morphological Openings." In IFMBE Proceedings. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_79.
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Rastogi, Krati, and Divya Lohani. "Edge Computing-Based Internet of Things Framework for Indoor Occupancy Estimation." In Research Anthology on Edge Computing Protocols, Applications, and Integration. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5700-9.ch031.
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Indoor occupancy estimation has become an important area of research in the recent past. Information about the number of people entering or leaving a building is useful in estimation of hourly sales, dynamic seat allocation, building climate control, etc. This work proposes a decentralized edge computing-based IoT framework in which the majority of the data analytics is performed on the edge, thus saving a lot of time and network bandwidth. For occupancy estimation, relative humidity and carbon dioxide concentration are used as inputs, and estimation models are developed using multiple linear regression, quantile regression, support vector regression, kernel ridge regression, and artificial neural networks. These estimations are compared using execution speed, power consumption, accuracy, root mean square error, and mean absolute percentage error.
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Brown, Philip J. "Regularized multiple regression." In Measurement, Regression, and Calibration. Oxford University PressOxford, 1994. http://dx.doi.org/10.1093/oso/9780198522454.003.0004.
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Abstract The Gauss-Markov theorem lends considerable weight to the use of least-squares as a method of fitting a linear parametric model. It provides ‘best’ estimators. It does, however, have a very restricted notion of what is best. Firstly it confines attention to estimators linear in the response Y. Secondly it looks only at unbiased estimators. Thirdly it minimizes mean-squared error, that is, it is concerned with expected squared error or quadratic loss: in the scalar case it uses is as an estimator of θ. Even retaining quadratic loss, there may be estimators which are either biased or non-linear in Y or both, which have an average performance far better than least-squares over much of the relevant parameter space. Such estimators may be introduced in a variety of ways. They are often thought to be particularly apt when the model matrix, X, becomes singular or rather near to singular. They are sometimes called regularized estimators and at other times shrinkage estimators, due to the reduced length of the estimated regression parameter vector compared to that of least-squares. The term regularized emanates from the method of regularization in approximation theory literature. Introducing a Lagrange multiplier, λ., this amounts to minimization of or its Hilbert space generalization. Their use as estimators or predictors comprises a large literature; a much smaller literature addresses wider inferential questions. In the model (3.7) assumed here, where columns of X have mean zero, generally, as in standard least-squares estimation.
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Kovačević, Branko, Zoran Banjac, and Tomislav Unkašević. "Perspective Chapter: Approximate Kalman Filter Using M-Robust Estimate Dynamic Stochastic Approximation with Parallel Adaptation of Unknown Noise Statistics by Huber’s M-Robust Parameter Estimator." In Kalman Filters -Theory, Applications, and Optimization [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1004294.
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The problem of designing a feasible adaptive M-robustified Kalman filter in a case of a thick-tailed Gaussian environment, characterized by impulsive noise-inducing observation and innovation outliers, and/or errors in mathematical model-inducing structural outliers, has been considered. Firstly, the time-varying criterion is used to generate a family of dynamic stochastic approximation algorithms. The M-robust estimate stochastic approximation is derived by minimizing the minimum variance criterion, the estimates of the latter being combined with the one-step minimum mean square error prediction to design M-robust estimate Kalman filter. Finally, the latter is combined with the Huber moving window M-robust parameter estimator of the unknown noises statistics, in parallel with the M-robust state estimation to design an adaptive M-robust estimate Kalman filter. Simulated maneuvering target tracking scenario revealed that the proposed adaptive M-robust estimate-based Kalman filter improves significantly the target estimation and tracking quality, being effective in suppressing multiple outliers with contamination degrees less than thirty percent. Moreover, the achieved improvement comes with additional computational efforts. However, these efforts are usually not significant enough to prevent real-time application.
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Eid, Abdulrahman M., Ali Bou Nassif, Chaouki Ghenai, Heba Y. Youssef, Latifa A. Alkhaja, and Hajar H. Almazrouei. "Machine Learning-Based SOC Prediction for Lithium-ion Batteries in Electric Vehicles." In Chronicle of Computing. Oklahoma International Publishing, 2024. http://dx.doi.org/10.55432/978-1-6692-0007-9_12.
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Developing an effective and accurate battery management system that can predict the state of charge of electric vehicles is essential to enhance the safety and efficiency of electric vehicles. The assessment of the state of charge of the battery is important not only for determining the amount of energy that is available from the battery but also for determining how long the battery will last. This paper provides a brief understanding of how the state of charge estimation was predicted before and after the era of Machine Learning (ML). In addition, it proposes an accurate and fast state of charge estimation for lithium-ion batteries in electric vehicle applications using machine learning. The proposed model is designed to be generalizable across various inputs, applicable to both new and old batteries, and robust under different charging and discharging scenarios. Model performance and accuracy were evaluated using predefined metrics such as Root Mean Square Error and Mean Absolute Error. Among the four machine learning algorithms that achieved an approximate error of 0.75, while Multiple Linear Regression (MLR) model was chosen for its lightness and speed in training and testing. The findings of this research contribute to the advancement of Battery Management System (BMS) design and implementation for enhancing the efficiency and safety of Lithium-Ion Batteries (LIBs) in real-world driving scenarios. In the future, further research might be conducted to study the implementation of a variety of deep learning algorithms, as well as the estimation of battery health and the remaining useful life.
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Aggarwal, Ritu, and Suneet Kumar. "Missing Value Imputation and Estimation Methods for Arrhythmia Feature Selection Classification Using Machine Learning Algorithms." In Machine Learning Methods for Engineering Application Development. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9879815079180122010013.
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&nbsp;Electrocardiogram signal analysis is very difficult to classify cardiac arrhythmia using machine learning methods. The ECG datasets normally come with multiple missing values. The reason for the missing values is the faults or distortion. When performing data mining, missing value imputation is the biggest task for data preprocessing. This problem could arise due to incomplete medical datasets if the incomplete missing values and cases were removed from the original database. To produce a good quality dataset for better analyzing the clinical trials, the suitable missing value imputation method is used. In this paper, we explore the different machine-learning techniques for the computed missing value in the electrocardiogram dataset. To estimate the missing imputation values, the collected data contains feature dimensions with their attributes. The experiments to compute the missing values in the dataset are carried out by using the four feature selection methods and imputation methods. The implemented results are shown by combined features using IG (information gain), GA (genetic algorithm) and the different machine learning classifiers such as NB (naïve bayes), KNN (K-nearest neighbor), MLP (Multilayer perception), and RF (Random forest). The GA (genetic algorithm) and IG (information gain) are the best suitable methods for obtaining the results on lower dimensional datasets with RMSE (Root mean square error. It efficiently calculates the best results for missing values. These four classifiers are used to analyze the impact of imputation methods. The best results for missing rate 10% to 40% are obtained by NB that is 0.657, 0.6541, 0.66, 0.657, and 0.657, as computed by RMSE (Root mean Square error). It means that error will efficiently reduced by naïve bayes classifier.
Conference papers on the topic "Multiple mean estimation"
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Guibert, Alexandre, Álvaro Díaz-Flores, Anirban Chaudhuri, and H. Alicia Kim. "Multifidelity Uncertainty Quantification in Battery Performance for eVTOL Flights Under Material and Loading Uncertainties." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1167.
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This study addresses safety concerns within the rapidly evolving Electric Vertical Takeoff and Landing (eVTOL) aircraft domain, focusing on efficient tools to quantify uncertainties in lithium-ion battery behavior - a critical aspect of eVTOL. One major issue with quantifying uncertainty is the prohibitive computational cost associated with many queries of an expensive-to-evaluate computational model. This work employs three physics-based battery models models of varying fidelity and cost to estimate the mean and the variance of the selected quantities of interest through a multifidelity method to reduce the computation cost. By combining information from multiple cheaper, lower-fidelity models through the Multifidelity Monte Carlo method, we significantly reduce the number of high-fidelity samples required for a prescribed mean-squared error, consequently reducing computational costs down to a tractable level. The proposed methodology is applied to estimate the mean and the variance of the battery temperature and voltage, accounting for uncertainties in flight conditions and materials. The first example focuses on a 580-second flight and is benchmarked against a standard Monte Carlo sampling technique. Results indicate a notable fourfold speed-up using the Multifidelity Monte Carlo method compared to the standard Monte Carlo method for the same mean-squared error for the voltage estimate. To showcase the method's generality, the multifidelity method is then applied to a longer flight of 3580 seconds for estimating the mean and the variance and utilizing these statistics to approximately estimate the probability of the flight completion. This demonstrates the adaptability of the methodology to various power profiles and considered uncertainties, with potential extensions to any battery chemistry. In conclusion, the presented multifidelity method offers a robust approach to enhance eVTOL safety by efficiently estimating uncertainties in battery behavior.
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Sekokotoana, Lehlohonolo Edwin, Fambirai Takawira, and Olutayo Oyeyemi Oyerinde. "Least Mean Squares Channel Estimation for Downlink Non-Orthogonal Multiple Access." In 2019 IEEE AFRICON. IEEE, 2019. http://dx.doi.org/10.1109/africon46755.2019.9133868.
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Wilson, D. Keith, Chris L. Pettit, and Vladimir Ostashev. "Bayesian estimation of mean transmission loss along multiple paths with randomly scattered signals." In 176th Meeting of Acoustical Society of America 2018 Acoustics Week in Canada. Acoustical Society of America, 2018. http://dx.doi.org/10.1121/2.0000968.
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Ben Hassen, Sonia, Faouzi Bellili, Abdelaziz Samet, and Sofiene Affes. "Estimation of Angular Spreads and Mean Angles of Arrival for Multiple Incoherently-Distributed Noncircular Sources." In 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB). IEEE, 2015. http://dx.doi.org/10.1109/icuwb.2015.7324449.
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Alecsa, Cristian Daniel. "G-KMM: A Flexible Kernel Mean Matching Optimization Method for Density Ratio Estimation Involving Multiple Train & Test Datasets." In 12th International Conference on Soft Computing, Artificial Intelligence and Applications. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.132422.
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In the present paper we introduce new optimization algorithms for the task of density ratio estimation. More precisely, we consider extending the well-known KMM (kernel mean matching) method using the construction of a suitable loss function, in order to encompass more general situations involving the estimation of density ratio with respect to subsets of the training data and test data, respectively. The codes associated to our Python implementation can be found at https://github.com/CDAlecsa/ Generalized-KMM.
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Young, Monica J., Mark N. Glauser, Hiroshi Higuchi, and Jeffrey Taylor. "Towards Sensing and Control of Separation in Subsonic Flows." In ASME/JSME 2003 4th Joint Fluids Summer Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/fedsm2003-45586.
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The purpose of this study is to validate the use of Proper Orthogonal Decomposition POD and Modified Linear Stochastic Estimation mLSE based low-dimensional methods to model an external flow over a NACA 4412 airfoil. By using a combination of Particle Image Velocimetry PIV and multiple airfoil surface pressure measurements, the full velocity field (mean plus fluctuating) is estimated through implementation of a modified complementary technique. We will identify a low-dimensional mean flow just from the wall pressure, specifically observing when the profiles are at the incipient condition. This gives a reasonable estimate of the low-dimensional velocity field. The importance of this work lies in that the flow is estimated from the wall pressure only, providing a practical means for estimating the flow state. This is particularly important for flow control applications.
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Hamadate, Naoya, Keiichi Watanuki, and Kazunori Kaede. "Confidence Estimation in Multiple Choice Questions Using Eye Movements." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001801.
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In recent years, with the spread of electronic devices, e-learning has become a common form of learning, especially among students and other young people. One of the most common forms of e-learning is multiple-choice questions. While multiple-choice questions allow the learner to grasp the answer instantaneously and reliably, they also allow the learner to answer correctly by guesswork or chance, which may cause the learner to ignore content that should be reviewed. Therefore, it is important to estimate the confidence level from the learner's mental information. Eye movements are often used as a method of ascertaining learners' mental information. Previous studies have suggested that saccades are effective in discriminating comprehension of sentences, and pupil size is effective in evaluating English word acquisition. In addition, it has been confirmed that the fixation of multiple choice questions changes depending on the learner's confidence level. The purpose of this study was to evaluate the relationship between eye movements, such as saccades and pupil size, and the level of confidence in multiple-choice questions. As an experiment, we measured eye movements using four-choice questions on Japanese translation of English words. Thirty questions were designed to have varying degrees of difficulty. A tabletop eye movement measurement device was used for eye tracking. The flow of one trial is described below. The participant gazed at the English word on the monitor for 5 seconds. After that, four choices were displayed on the monitor, and the participants answered the questions. The participants were instructed to answer the question immediately after the choice was decided. Finally, a post-questionnaire was conducted. In the post-questionnaire, the participants were asked to answer one of the following questions: "I could answer the question without any choice," "I knew the answer by looking at the choices," "I did not know the answer but guessed from the choices," or "I do not know the answer at all. The experiment was conducted on seven Japanese male university students (23.3±1.6 years old) in a random order of 30 trials each. Saccade frequency and mean pupil size were used as evaluation indices. The percentage of fixation time in the answer choices was also evaluated based on previous studies. The confidence level was defined as "recall," "recognition," "guess," and "intuition," in descending order based on the responses to the post-questionnaire, and classified into four groups. The results of multiple comparison tests showed that the percentage of fixation time of the answer choices was significantly larger when the answer choice was "recall" than when the answer choice was "guess. On the other hand, there was no significant difference in saccade frequency and mean pupil size. The reason for the lack of differences may be that there were some trials in which the pupil size did not change because the time to answer the questions was too short. Therefore, as an experiment for improvement, we changed the contents of the multiple-choice questions and are studying the contents of questions that require more time. By increasing the answer time, we expect to see different characteristics of eye movements depending on the level of confidence from the previous experiment.
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Hardesty, R. M., and Barry J. Rye. "Simulations of discrete spectral peak estimators for single and multiple lidar pulse returns." In Coherent Laser Radar. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/clr.1991.tha2.
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Given an estimate of the spectrum of a time series sample drawn from a single pulse lidar return, there are several ways in which the signal frequency can be estimated. Texts on weather radar stress use of the first moment expression for estimation of the mean Doppler frequency shift, with or without threshold techniques to remove contributions from the spectrum of the noise.1,2 For lidar, there is increasing interest in discrete spectral peak estimation (DSPE) techniques,3 where the frequency at which the spectral maximum occurs is accepted as the estimate. Indeed, the weighting algorithm proposed for use with radar wind profilers4 uses a DSPE as a ’first guess’ of the mean frequency, prior to formation of a weighted average of spectral elements in the neighborhood of this value that have power levels above a selected threshold. It evidently has more in common with the DSPE method than earlier moment algorithms.
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Oba, Ryoya, Keiichi Watanuki, Kazunori Kaede, and Yusuke Osawa. "Feature Selection for Machine Learning-Based Core Body Temperature Estimation Using Hand-Measurable Biological Information." In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004362.
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Core body temperature (CBT) is an important health indicator that denotes the temperature of the body core, and maintains brain and organ function. Invasive methods of CBT measurement pose challenges in assessing and monitoring human health. To address this, estimation of tympanic membrane temperature using multiple biological parameters often referenced for CBT has been attempted in previous studies. Our research focused on machine learning-based CBT estimation using hand-measurable biological data. Furthermore, while various studies have investigated machine learning models and the impact of information acquisition environments, few have compared the estimation accuracy of different biological parameters or assessed optimal feature combinations. Our proposed method entails the evaluation of indices in both normal scenarios with all variables and patterned scenarios with varying combinations of reduced explanatory variables. The comparison results reveal that when estimating the CBT based on skin conductance and pulse wave intervals excluding skin temperature, the mean absolute error, coefficient of determination, and root mean square error were 0.17 °C, 0.71, and 0.24 °C, respectively. This suggests that our approach is a feasible CBT estimation method that does not rely on skin temperature, although accuracy concerns persist. Furthermore, the estimation of the difference between CBT and skin temperature suggests that the estimation method may have accounted for individual variations within the data. Implementing the proposed method in increasingly popular smart rings and watches could facilitate the acquisition of CBT in daily life.
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Pinardi, Daniel, Angelo Farina, Marco Binelli, and Andrea Toscani. "Estimation of Diagonal Volterra Kernels of an Audio System During Normal Operation with Multiple Least Mean Squares Adaptive Filters." In 2023 Immersive and 3D Audio: from Architecture to Automotive (I3DA). IEEE, 2023. http://dx.doi.org/10.1109/i3da57090.2023.10289311.
Full textReports on the topic "Multiple mean estimation"
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DiGrande, Laura, Sue Pedrazzani, Elizabeth Kinyara, et al. Field Interviewer– Administered Dietary Recalls in Participants’ Homes: A Feasibility Study Using the US Department of Agriculture’s Automated Multiple-Pass Method. RTI Press, 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0045.2105.
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Objective: The purpose of this study was to assess the feasibility of administering the Automated Multiple-Pass Method (AMPM), a widely used tool for collecting 24-hour dietary recalls, in participants’ homes by field interviewers. Design: The design included computer-assisted personal interviews led by either a nutritionist (standard) or field interviewer. Portion estimators tested were a set of three-dimensional food models (standard), a two-dimensional food model booklet, or a tablet with digital images rendered via augmented reality. Setting: Residences in central North Carolina. Participants: English-speaking adults. Pregnant women and individuals who were fasting were excluded. Results: Among 133 interviews, most took place in living rooms (52%) or kitchens (22%). Mean interview time was 40 minutes (range 13–90), with no difference by interviewer type or portion estimator, although timing for nutritionist-led interviews declined significantly over the study period. Forty-five percent of participants referenced items from their homes to facilitate recall and portion estimation. Data entry and post-interview coding was evaluated and determined to be consistent with requirements for the National Health and Nutrition Examination Survey. Values for the number of food items consumed, food groups, energy intake (average of 3,011 kcal for men and 2,105 kcal for women), and key nutrients were determined to be plausible and within reasonably expected ranges regardless of interviewer type or portion estimator used. Conclusions: AMPM dietary recall interviews conducted in the home are feasible and may be preferable to clinical administration because of comfort and the opportunity for participants to access home items for recall. AMPMs administered by field interviewers using the food model booklet produced credible nutrition data that was comparable to AMPMs administered by nutritionists. Training field interviewers in dietary recall and conducting home interviews may be sensible choices for nutrition studies when response rates and cost are concerns.
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Tarko, Andrew P., Mario Romero, Cristhian Lizarazo, and Paul Pineda. Statistical Analysis of Safety Improvements and Integration into Project Design Process. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317121.
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RoadHAT is a tool developed by the Center for Road Safety and implemented for the INDOT safety management practice to help identify both safety needs and relevant road improvements. This study has modified the tool to facilitate a quick and convenient comparison of various design alternatives in the preliminary design stage for scoping small and medium safety-improvement projects. The modified RoadHAT 4D incorporates a statistical estimation of the Crash Reduction Factors based on a before-and-after analysis of multiple treated and control sites with EB correction for the regression-to-mean effect. The new version also includes the updated Safety Performance Functions, revised average costs of crashes, and the comprehensive table of Crash Modification Factors—all updated to reflect current Indiana conditions. The documentation includes updated Guidelines for Roadway Safety Improvements. The improved tool will be implemented at a sequence of workshops for the final end users and preceded with a beta-testing phase involving a small group of INDOT engineers.
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Clarke, Paul S., Tom M. Palmer, and Frank Windmeijer. Estimating structural mean models with multiple instrumental variables using the generalised method of moments. Institute for Fiscal Studies, 2011. http://dx.doi.org/10.1920/wp.cem.2011.2811.
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Hibbert, Angela, and Begona Pérez Gómez. Operational monitoring systems available at the three sites. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d5.9.
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Tide gauges can capture sea level variability on multiple timescales, from high frequency events like waves, tides and tsunamis, to seasonal and interannual changes and the longer-term trends associated with Climate Change. However, financial constraints dictate that they are often maintained to lower standards than the stringent accuracy requirements demanded by the IOC-UNESCO’s Global Sea Level Observing System (GLOSS) for monitoring sea level rise. In addition, a sparsity of Global Navigation Satellite System (GNSS) receivers at the coast means that there are large uncertainties in rates of land motion at tide gauges, which also hampers the estimation of long-term sea level trends. Task 5.1.1 has devised prototype low maintenance tide gauge systems, powered by renewable energy and which monitor both land motion and sea level using novel techniques such as ground-based GNSS Interferometric Reflectometry (GNSS-IR). These systems eliminate the need for costly ongoing levelling exercises and also incorporate customisations to local monitoring needs, such as sensors for lightning detection and wave height. Despite a number of unforeseen setbacks, these prototype systems are now operating at 2 locations in the Mediterranean Sea and a third system is in transit to Colombia for installation by local stakeholders who have been trained in the installation methodology by the WP5 team. It is hoped that there is potential to advance these technological solutions as a global standard, via the GLOSS community. (EuroSea Deliverable, D5.9)
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Mizrach, Amos, Michal Mazor, Amots Hetzroni, et al. Male Song as a Tool for Trapping Female Medflies. United States Department of Agriculture, 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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Grundy, Helen H., Lucy C. Brown, Mark Sykes, M. Rosario Romero, and Dominic Anderson. Review of allergen analytical testing methodologies. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.noe660.
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The Food Information Regulation (FIR) states that accurate and understandable allergen information needs to be supplied to consumers for the 14 priority allergens. Food allergies affects between 1-2% of the UK population, with some allergens responsible for hospital admissions with anaphylaxis. Food businesses have a legal responsibility to provide food that is safe, which means declaring allergens present as ingredients and warning consumers about their potential unintended presence due to cross-contact. A system needs to be implemented for testing allergens in foods, responding to incidents, and manage risks to protect consumers. This review was prepared to inform FSA on the current state of the art of allergen testing methodologies and the remaining challenges. This project combined a critical literature review of testing methods with assessments of allergen proficiency testing data, consultation with stakeholders from the food industry, and consultation with industry experts regarding multiplex methodologies and the harmonisation of methods in an unbiased review of the current status of testing capabilities for the 14 EU-retained regulated food allergens. Gaps in testing capabilities were highlighted in order to inform future direction, including a lack of transparent public data for the performance and applicability of commercial test kits. Cross-reactivities of kits were also highlighted along with the need for development of fast and accurate point-of-use tests to support food production. A review of allergen proficiency testing data revealed gaps in testing capabilities and variations between the outputs of different test kits when testing for the same allergen. This review critically compares current testing methods to progress towards a suitable harmonised testing protocol that facilitates allergen risk management, and to mitigate limitations and evidence gaps. Suitable workflows outlining recommended testing protocols are presented for priority allergens to provide a resource for compliant testing and incident management. Estimations of the cost of setting up new testing laboratories to support allergen workflows are also included in addition to detailing the cost of testing by established laboratories.
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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, 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.