Iterative Estimation Approach Research Articles

Improvement of triangle centroid localization algorithm based on PIT criterion (ITCL-PIT) for WSNs

One of the most significant study directions is node positioning in wireless sensor networks (WSNs). Because the existing RSSI-based triangle centroid localization technique is susceptible to the surrounding environment, this paper proposes an improved triangle centroid localization algorithm based on point-in-triangulation (PIT) criterion (ITCL-PIT) in terms of positioning accuracy and response speed. When combined with the actual placement situation in conventional triangle centroid localization, the suggested algorithm considers the estimated coordinates of the junction points as extra beacon nodes. As a result of the new beacon nodes, the size of the triangle in the junction region is decreased. Then, using the PIT criteria, keep calculating until the predicted position of the node is outside the triangle. Finally, the unknown node's coordinates are determined using the centroid approach. Based on the guaranteed response time, when the communication distance is 15–30 m, the ITCL-PIT method may enhance localization accuracy by up to five times when compared to the standard triangle centroid localization approach. Furthermore, the proposed technique has higher localization accuracy and faster response speed than the centroid iterative estimation approach, and the response time of the ITCL-PIT algorithm is reduced by around 17%. In addition, the experimental platform is built to ensure that the proposed strategy effectively lowers positioning error.

Past price ‘memory’ in the housing market: testing the performance of different spatio-temporal specifications

ABSTRACTRecent methodological developments provide a way to incorporate the temporal dimension when accounting for spatial effects in hedonic pricing. Weight matrices should decompose the spatial effects into two distinct components: bidirectional contemporaneous spatial connections; and unidirectional spatio-temporal effects from past transactions. Our iterative estimation approach explicitly analyses the role of time in price determination. The results show that both spatio-temporal components should be included in model specification; past transaction information stops contributing to price determination after eight months; and limited temporal friction is exhibited within this period. These findings highlight the decidedly non-linear temporal patterns of such information effects.

Scatter and crosstalk corrections for 99mTc/123I dual‐radionuclide imaging using a CZT SPECT system with pinhole collimators

The energy spectrum for a cadmium zinc telluride (CZT) detector has a low energy tail due to incomplete charge collection and intercrystal scattering. Due to these solid-state detector effects, scatter would be overestimated if the conventional triple-energy window (TEW) method is used for scatter and crosstalk corrections in CZT-based imaging systems. The objective of this work is to develop a scatter and crosstalk correction method for (99m)Tc/(123)I dual-radionuclide imaging for a CZT-based dedicated cardiac SPECT system with pinhole collimators (GE Discovery NM 530c/570c). A tailing model was developed to account for the low energy tail effects of the CZT detector. The parameters of the model were obtained using (99m)Tc and (123)I point source measurements. A scatter model was defined to characterize the relationship between down-scatter and self-scatter projections. The parameters for this model were obtained from Monte Carlo simulation using SIMIND. The tailing and scatter models were further incorporated into a projection count model, and the primary and self-scatter projections of each radionuclide were determined with a maximum likelihood expectation maximization (MLEM) iterative estimation approach. The extracted scatter and crosstalk projections were then incorporated into MLEM image reconstruction as an additive term in forward projection to obtain scatter- and crosstalk-corrected images. The proposed method was validated using Monte Carlo simulation, line source experiment, anthropomorphic torso phantom studies, and patient studies. The performance of the proposed method was also compared to that obtained with the conventional TEW method. Monte Carlo simulations and line source experiment demonstrated that the TEW method overestimated scatter while their proposed method provided more accurate scatter estimation by considering the low energy tail effect. In the phantom study, improved defect contrasts were observed with both correction methods compared to no correction, especially for the images of (99m)Tc in dual-radionuclide imaging where there is heavy contamination from (123)I. In this case, the nontransmural defect contrast was improved from 0.39 to 0.47 with the TEW method and to 0.51 with their proposed method and the transmural defect contrast was improved from 0.62 to 0.74 with the TEW method and to 0.73 with their proposed method. In the patient study, the proposed method provided higher myocardium-to-blood pool contrast than that of the TEW method. Similar to the phantom experiment, the improvement was the most substantial for the images of (99m)Tc in dual-radionuclide imaging. In this case, the myocardium-to-blood pool ratio was improved from 7.0 to 38.3 with the TEW method and to 63.6 with their proposed method. Compared to the TEW method, the proposed method also provided higher count levels in the reconstructed images in both phantom and patient studies, indicating reduced overestimation of scatter. Using the proposed method, consistent reconstruction results were obtained for both single-radionuclide data with scatter correction and dual-radionuclide data with scatter and crosstalk corrections, in both phantom and human studies. The authors demonstrate that the TEW method leads to overestimation in scatter and crosstalk for the CZT-based imaging system while the proposed scatter and crosstalk correction method can provide more accurate self-scatter and down-scatter estimations for quantitative single-radionuclide and dual-radionuclide imaging.

How Many Iterations are Sufficient for Efficient Semiparametric Estimation?

Abstract. A common practice in obtaining an efficient semiparametric estimate is through iteratively maximizing the (penalized) full log‐likelihood w.r.t. its Euclidean parameter and functional nuisance parameter. A rigorous theoretical study of this semiparametric iterative estimation approach is the main purpose of this study. We first show that the grid search algorithm produces an initial estimate with the proper convergence rate. Our second contribution is to provide a formula in calculating the minimal number of iterationsk*needed to produce an efficient estimate. We discover that (i)k*depends on the convergence rates of the initial estimate and the nuisance functional estimate, and (ii)k*iterations are also sufficient for recovering the estimation sparsity in high dimensional data. The last contribution is the novel construction ofwhich does not require knowing the explicit expression of the efficient score function. The above general conclusions apply to semiparametric models estimated under various regularizations, for example, kernel or penalized estimation. As far as we are aware, this study provides a first general theoretical justification for the ‘one‐/two‐step iteration’ phenomena observed in the semiparametric literature.

The Effect of Phase-Correlated Returns and Spatial Smoothing on the Accuracy of Radar Refractivity Retrievals

Abstract Radar refractivity retrievals have the potential to accurately capture near-surface humidity fields from the phase change of ground clutter returns. In practice, phase changes are very noisy and the required smoothing will diminish large radial phase change gradients, leading to severe underestimates of large refractivity changes (ΔN). To mitigate this, the mean refractivity change over the field (〈ΔN〉field) must be subtracted prior to smoothing. However, both observations and simulations indicate that highly correlated returns (e.g., when single targets straddle neighboring gates) result in underestimates of 〈ΔN〉field when pulse-pair processing is used. This may contribute to reported differences of up to 30 N units between surface observations and retrievals. This effect can be avoided if 〈ΔN〉field is estimated using a linear least squares fit to azimuthally averaged phase changes. Nevertheless, subsequent smoothing of the phase changes will still tend to diminish the all-important spatial perturbations in retrieved refractivity relative to 〈ΔN〉field; an iterative estimation approach may be required. The uncertainty in the target location within the range gate leads to additional phase noise proportional to ΔN, pulse length, and radar frequency. The use of short pulse lengths is recommended, not only to reduce this noise but to increase both the maximum detectable refractivity change and the number of suitable targets. Retrievals of refractivity fields must allow for large ΔN relative to an earlier reference field. This should be achievable for short pulses at S band, but phase noise due to target motion may prevent this at C band, while at X band even the retrieval of ΔN over shorter periods may at times be impossible.

An iterative approach for estimation of student performances based on linguistic evaluations

In group decision analysis, numerous approaches have been suggested in an attempt to solve the problem of aggregation of individual fuzzy opinions to form a group consensus as the basis of a group decision. In this study, an optimization model, which reflects different points of view of many decision makers by weighting fuzzy opinions, is proposed for the evaluation of student performances in student-centered learning. An iterative algorithm is provided for the solution of this model, and the consequent theorem is proved. Experimental results show that the proposed iterative algorithm yields more efficient results than do the classical optimization methods. Moreover, the WABL (weighted averaging based on the levels) method produces more accurate results than do the other frequently used defuzzification methods, such as COA (center of area) and MOM (mean of maxima).

An efficient joint channel estimation and decoding algorithm for turbo-coded space–time orthogonal frequency division multiplexing receivers

The challenging problem in the design of digital receivers of today's and future high-speed, high data-rate wireless communication systems is to implement the optimal decoding and channel estimation processes jointly in a computationally feasible way. Without realising such a critical function perfectly at receiver, the whole system will not work properly within the desired performance limits. Unfortunately, direct implementation of such optimal algorithms is not possible mainly due to their mathematically intractable and computationally prohibitive nature. A novel algorithm that reaches the performance of the optimal maximum a posteriori (MAP) algorithm with a feasible computational complexity is proposed. The algorithm makes use of a powerful statistical signal processing tool called the expectation–maximisation (EM) technique. It iteratively executes the MAP joint channel estimation and decoding for space–time block-coded orthogonal frequency division multiplexing systems with turbo channel coding in the presence of unknown wireless dispersive channels. The main novelty of the work comes from the facts that the proposed algorithm estimates the channel in a non-data-aided fashion and therefore except a small number of pilot symbols required for initialisation, no training sequence is necessary. Also the approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen–Loeve (KL) orthogonal expansion and finds MAP estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion, no matrix inversion is required in the proposed MAP estimator. Moreover, optimal rank reduction is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the iterative estimation approach.

An iterative noise cross-PSD estimation for two-microphone speech enhancement

Among various speech enhancement methods, dual-microphone methods are of a great importance for their low cost implementation and for exploiting spatial-filtering benefits of the microphone arrays. Coherence based methods are well-known as efficient two-microphone noise reduction techniques. These techniques do not work well, when received noise signals are correlated. These can be improved when the cross power spectral density (CPSD) of noise is available. In this paper, we propose an iterative approach for estimation of the noise CPSD to be employed in coherence based methods. We compare the proposed iterative noise CPSD estimation with a noise CPSD estimation technique based on voice activity detector (VAD), both of which are employed in a two-microphone speech enhancement, separately. Evaluation results show that the two-microphone speech enhancement scheme utilizing the proposed noise CPSD estimation technique performs superior than the enhancement system using the VAD-based noise CPSD estimation.

Iterative Channel Estimation and Decoding of Turbo Coded SFBC-OFDM Systems

We consider the design of turbo receiver structures for space-frequency block coded orthogonal frequency division multiplexing (SFBC-OFDM) systems in the presence of unknown frequency and time selective fading channels. The turbo receiver structures for SFBC-OFDM systems under consideration consists of an iterative MAP expectation/maximization (EM) channel estimation algorithm, soft MMSE-SFBC decoder and a soft MAP outer-channel-code decoder. MAP-EM employs iterative channel estimation and it improves receiver performance by re-estimating the channel after each decoder iteration. Moreover, the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The optimal truncation property of the KL expansion can reduce computational load on the iterative estimation approach. The performance of the proposed approaches are studied in terms of mean square error and bit-error rate. Through computer simulations, the effect of a pilot spacing on the channel estimator performance and sensitivity of turbo receiver structures on channel estimation error are studied. Simulation results illustrate that receivers with turbo coding are very sensitive to channel estimation errors compared to receivers with convolutional codes. Moreover, superiority of the turbo coded SFBC-OFDM systems over the turbo coded STBC-OFDM systems is observed especially for high Doppler frequencies.

Estimating the Held-Karp lower bound for the geometric TSP

The Held-Karp lower bound (HK) provides a very good problem-specific estimate of optimal tour length for the travelling salesman problem (TSP). This measure, which is relatively quick and easy to compute, has enormous practical value when evaluating the quality of near optimal solutions for large problems where the true optima are not known. Although HK can be evaluated exactly by Linear Programming techniques, code for doing this efficiently for problems larger than a few hundred cities is not readily available or easy to produce. In addition Linear Programming implementations (even efficient ones) do not scale well and rapidly become impractical for problems with many thousands of cities. In this study we concentrate on the iterative estimation approach proposed by Held and Karp in their original papers. Our paper provides implementation details for two iterative schemes which both use the subgraph speed-up technique. We begin by surveying the important theoretical issues which underpin the original iterative approach of Held and Karp (now known as subgradient optimisation). We then present some detailed practical guidelines for the evaluation of HK for large geometric TSP problems, and also provide some empirical evidence demonstrating the robustness of the iterative schemes we have used. Finally our estimation of the Goemans and Bertsimas constant provides an independent confirmation of the value published recently by Johnson, McGeoch and Rothberg and simultaneously supports the claim that our approach does indeed produce reliable results.

A three-stage: iterative estimation approach to the permanent income model

In this paper, the minimum-variance (MV) method, originally proposed by Singh and Drost (1971) and Willassen (1975) to estimate the permanent income model (PIM), is revised in accordance with Friedman's (1957) assumptions. It is shown that the revised MV estimator of the marginal propensity to consume permanent income (mpc) of the PIM is just the maximum-likelihood (ML) estimator of Kendall and Stuart (1901). Treating the PIM as a restricted factor-analysis model (RFAM) of two indicators and one single common factor and incorporating with Tuekey's (1951) grouping approach, the three-stage iterative estimation method developed by Lin (1978) is suggested as an alternative approach to estimate the PIM. For illustration, estimates of mpc are computed for the data used by Haavelmo (1953). The sensitivity of estimation of mpc to the prior specification of the ratio of the variance of transitory income (error in observable income) to that of transitory consumption (error in observable consumption) ia conducted. ...