Efficient Estimation Technique Research Articles

Covariate Information Matrix for Sufficient Dimension Reduction

ABSTRACT Building upon recent research on the applications of the density information matrix, we develop a tool for sufficient dimension reduction (SDR) in regression problems called covariate information matrix (CIM). CIM exhaustively identifies the central subspace (CS) and provides a rank ordering of the reduced covariates in terms of their regression information. Compared to other popular SDR methods, CIM does not require distributional assumptions on the covariates, or estimation of the mean regression function. CIM is implemented via eigen-decomposition of a matrix estimated with a previously developed efficient nonparametric density estimation technique. We also propose a bootstrap-based diagnostic plot for estimating the dimension of the CS. Results of simulations and real data applications demonstrate superior or competitive performance of CIM compared to that of some other SDR methods. Supplementary materials for this article are available online.

An improved and efficient biased estimation technique in logistic regression model

In this article, we propose a new improved and efficient biased estimation method which is a modified restricted Liu-type estimator satisfying some sub-space linear restrictions in the binary logistic regression model. We study the properties of the new estimator under the mean squared error matrix criterion and our results show that under certain conditions the new estimator is superior to some other estimators. Moreover, a Monte Carlo simulation study is conducted to show the performance of the new estimator in the simulated mean squared error and predictive median squared errors sense. Finally, a real application is considered.

Crowd density estimation in still images using multiple local features and boosting regression ensemble

Crowd density estimation is a challenging research problem in computer vision and has many applications in commercial and defense sectors. Various crowd density estimation methods have been proposed by researchers in the past, but there is an utmost need for accurate, robust and efficient crowd density estimation techniques for its practical implementation. In this paper, we propose a fine-tuned and computationally economical, ensemble regression-based machine learning model for crowd density estimation. The WorldExpo’10 dataset has been used for experimental analysis and model performance evaluation. We extract variety of features in texture-based features such as gray-level co-occurrence matrix, local binary pattern and histogram of oriented gradients, structure-based features such as perimeter pixel and the orientation of pixels, and segment-based handcrafted features from each patch of the image and use an optimum combination of these features as input to the regression model. To achieve optimized memory utilization and faster speed, principal component analysis is employed to reduce the dimensions of the lengthy feature vector. Extensive experiments on different fronts ranging from the model hyperparameter optimization, features optimization and features selection were conducted, and at each step, we selected the most favorable results as input to the optimized model. The performance of the model is evaluated based on two popular metrics, i.e., mean absolute error and mean squared error. The comparative analysis shows that the proposed system outperforms the former methods tested on the WorldExpo’10 dataset.

Efficient Multidimensional Wideband Parameter Estimation for OFDM Based Joint Radar and Communication Systems

In this paper, we propose a new pre-processing technique for efficient multidimensional wideband parameter estimation. One application is provided by an orthogonal frequency division multiplexing-(OFDM) based joint radar and communication system, which uses SIMO architecture. In this paper, the estimated parameters are given by the range (time delay), the relative velocity, and the direction of arrival (DoA) pairs of the dominant radar targets. Due to the wideband assumption, the received signals on different subcarriers are incoherent and, therefore, cannot fully exploit the frequency diversity of the OFDM waveform. To estimate the parameters jointly and coherently on different subcarriers, we propose an interpolation-based coherent multidimensional parameter estimation framework, where the wideband measurements are transformed into an equivalent narrowband system. Then, narrowband multidimensional parameter estimation algorithms can be applied. In particular, a wideband $R$ -D periodogram is introduced as a benchmark algorithm, and we develop the $R$ -D Wideband Unitary Tensor-ESPRIT algorithm. The simulations show that the proposed coherent parameter estimation method significantly outperforms the direct application of narrowband parameter estimation algorithms to the wideband measurements. If the fractional bandwidth is significant and the SNR is not too low, the estimates provided by the narrowband estimation algorithms can become inconsistent. Moreover, the interpolation order should be chosen according to the SNR regime. In the low SNR regime, interpolation with a lower-order (i.e., linear interpolation) is recommended. For higher SNRs, we propose an interpolation with higher-order polynomials, e.g., fourth-order (cubic splines) or even higher.

Novel Spectral Efficient Technique for MIMO-OFDM Channel Estimation with Reference to PAPR and BER Analysis

With emerge of increasing research in domain of future wireless communications, massive multiple input multiple output (MIMO) attracted most of researchers interests. Massive MIMO is nothing but high speed wireless communication standards. The performance of MIMO systems is based on techniques used for channel estimation. Efficient channel estimation leads to spectral efficient wireless communications. There are number of channel estimation techniques presented recently in literature with pros and cons. The recent method shows the spectral and bit error rate (BER) efficiency, however apart from this, there is need of improving the peak to average power ratio (PAPR). Recently we proposed, novel channel estimation method as the existing channel estimation techniques failed to effectively solve the inter symbol interference (ISI) problem. The presence of ISI in MIMO-OFDM may leads to worst performance. Our proposed blind channel estimation is combined with independent component analysis (ICA) hence this method is called as hybrid ICA (HICA) to minimize the ISI effect. The extensive simulation analysis of proposed HICA required to claiming the scalability as well as reliability. In this paper, proposed study on additional performance metrics such as PAPR and computational costs (energy) along with BER and spectral efficiency performances. The result claims that HICA is not improving the PAPR and energy performances significantly.

An Improved Non-Intrusive Induction Motor Efficiency Estimation Technique for Non-Ideal Supply Voltage Conditions

Methods of accurately determining the efficiency of motors in situ without costly plant downtime are required to implement online motor management strategies that can lead to energy and cost savings. An ideal method must perform in practical power system conditions where there is variation in the supply voltage. An improved non-intrusive compensated slip technique utilising vibration signature analysis has been proposed for accurately estimating induction motor efficiency. The performance of this technique is evaluated through experimental data under ideal and non-ideal supply voltage conditions. The proposed method of estimating motor efficiency is found to be accurate and easy to implement. The proposed technique is robust in under-voltage and over-voltage power supply conditions.

Corporate governance, risk and efficiency: evidence from GCC Islamic banks

PurposeThe purpose of this paper is to determine the simultaneous effect of corporate governance (CG) of Gulf Cooperation Council (GCC) Islamic banks (IBs) on efficiency and risk.Design/methodology/approachThe authors include Shariah supervisory board (SSB) size, Chief Executive Officer (CEO)-duality and ownership structure as CG variables. Efficiency and risk are measured using the data envelopment analysis (DEA)/stochastic frontier analysis (SFA) and Z-score, respectively. This paper also examines the risk-efficiency relationship. To test the hypotheses, the authors used seemingly unrelated regressions on a sample of 56 GCC IBs during the period 2004-2013.FindingsThe results indicate that implementing rigorous CG structures correlate with higher efficiency levels. Particularly, the authors show that the governance structure of IBs allows them to take higher risks to achieve a high efficiency level. In addition, results show that bank efficiency and risk are positively related.Practical implicationsThis paper gives some insights to policy makers. It points out detail attention toward the importance of CG in IB that influences the efficiency level and risk-taking behavior. Thus, IB should improve governance procedures that can lead to higher efficiency and survival in a competitive environment and sustain financial crisis. Moreover, the economic conditions of a country are the main determinant of an IB’s efficiency and risk relationships.Originality/valueThe simultaneous effect of the CG of the GCC IBs on efficiency and risk is examined, taking into consideration different CG proxies, i.e., SSB size, CEO-duality and ownership structure, and different efficiency estimation techniques, i.e., SFA and DEA.

Literature survey on motion estimation techniques

Motion Estimation (ME) is one of the most intensive computational operations in video compression techniques. Video compression algo-rithm utilizes numerous standards such as MPEG1, MPEG4 AND H.261, H.264. Compression performance can be increased drastically by efficient motion estimation techniques by which energy is reduced within the residual frames involved in motion compensation. In this paper literature survey of motion estimation especially considering block matching ME (Motion Estimation). In this paper, comparison is made between the already existing block matching algorithms and their limitations in motion estimation along with their applications. Many algorithms including Three Step Search (TSS), Improved Three Step Search (ITSS), New Three Step Search (NTSS), Four Step Search (4SS), Diamond Search (DS), Flexible Triangle Search(FTS), Full Search(FS), Modified Full Search(MDF) are compared and their per-formance measures are discussed in this paper.

Comparative Analysis of ML-PSO DOA Estimation with Conventional Techniques in Varied Multipath Channel Environment

In the field of array signal processing, direction of arrival (DOA) estimation is a prime area of research. DOA estimation and adaptive beamforming (ABF) are the main issues in smart antennas, which dynamically find the direction of desired and interfering users and finds the optimum weights for beamforming. There are numerous spectral and eigen structure algorithms for estimating the direction of narrow band sources. However, in a complex multipath channel environment, received signals from different directions are fully or partially correlated, which prevents the applications of high resolution techniques to estimate the direction of incoming signals. Maximum likelihood (ML) is an efficient technique for DOA estimation in a low signal to noise ratio (SNR) and coherent channel environment. In this paper, we use particle swarm optimization (PSO) for estimating ML solution by optimizing complex non linear multimodal function over a high dimensional space in linear arrays. PSO-ML estimator has been compared with conventional DOA estimation techniques in uncorrelated, partially correlated and coherent channel environment. The performance of PSO-ML estimator and conventional algorithms are analyzed in varying partially correlated channel environment. The simulation results demonstrate that PSO based estimator gives superior statistical performance.

Efficient Channel Estimation and Detection Techniques for MIMO-OFDM Receivers Using ESPAR Antenna with Low-Complexity

Efficient Channel Estimation and Detection Techniques for MIMO-OFDM Receivers Using ESPAR Antenna with Low-Complexity

Development of Efficient Estimation Technique for Population Mean in Two Phase Sampling Using Fuzzy Tools

Abstract The present investigation deals with the problem of estimation of population mean in two-phase (double) sampling. Utilizing information on two auxiliary variables, one chain exponential ratio and regression type estimator has been proposed and its properties are studied under two different structures of twophase sampling. To make the estimator practicable, unbiased version of the proposed strategy has also been developed. The dominance of the suggested estimator over some contemporary estimators of population mean has been established through numerical illustrations carried over the data set of some natural population and artificially generated population. Categorization of the dominance ranges of the proposed estimation strategies are deployed through defuzzification tools, which are followed by suitable recommendations.

Real‐time harmonic estimation using a novel hybrid technique for embedded system implementation

Summary In this paper, an analytical technique for efficient estimation of harmonics generated by power systems is presented. A robust linear estimator known as H∞ filter is applied to extract the amplitudes of harmonics. As well, phases are estimated using a nonlinear algorithm, namely, cubature Kalman filter. Both methods are based upon space modeling of the studied signals leading to improved numerical stability. The proposed H∞-cubature Kalman filter algorithm has the capability of estimating power system time varying waveforms even in highly noisy measurements. Synthetic and real signals are used for testing the proposed algorithm, and accuracy of the estimation is reported by statistical indices. Simulations in MATLAB are compared with unscented Kalman filter to represent the quality of estimation. Real-time implementation of the proposed algorithm is conducted using C++ programming code in hardware-in-the-loop laboratory. The setup consists of an embedded computer set and dedicated hardware. Results show satisfactory performance of the proposed method for practical application in power quality and protection cases.

A Technique for Efficient Query Estimation over Distributed Data Streams

Distributed data stream mining in a sliding window has emerged recently, due to its applications in many domains including large Telecoms and Internet Service Providers, financial tickers, ATM and credit card operations in banks and transactions in retail chains. Many of these large-scale applications prohibit monitoring data centrally at a single location due to their massive volume of the data; therefore, data acquisition, processing, and mining tasks are often distributed to a number of processing nodes, which monitor their local streams and exchange only the summary of data either periodically or on demand. While this offer many advantages, distributed stream applications possess significant challenges including problems related to an online analysis of the recent data, communication efficiency and various estimation of various complex queries. There are few existing techniques which solve problems related to distributed sliding window data stream; however, those techniques are focused on solving only simple problems and require high space, query, and communication cost, which can be a bottleneck for many of these large scale applications. In this paper, we propose an efficient query estimation technique by constructing a small sketch of the data stream. The constructed sketch uses a deterministic sliding window model and can estimate various complex queries, for both centralized and distributed applications; including point queries (i.e., range queries and heavy hitter queries), quantiles, inner product, and self-join size queries, with deterministic guarantees on the precision. The proposed approach improves upon recent existing work for these problems, in terms of the memory and query cost in a centralized setting and in terms of communication cost and merge complexity in a distributed setting. It requires $O(\frac{1}{\epsilon ^2}\log {(\epsilon N)})$ memory (where $0 is a user defined parameter), can provide estimates in $O(1)$ time, and processes each incoming record in $O(1)$ amortized time. Detailed experimental analysis, both in centralized and distributed settings demonstrates that in practice the proposed approach uses about six times less memory, and has about eight times less query time when compared to ECM sketches. In a distributed application, the proposed technique also significantly improves (around seven times) on the communication cost between distributed sites.

Parameters estimation of three-phase induction motors using differential evolution

Three-phase induction motors are extensively used in the industry due to their robustness characteristics, low cost and easy maintenance. Usually, it is necessary to implement drive and control systems for such motors, which requires the knowledge of their mechanical and electrical parameters. However, in some cases, these data are not immediately available, or the values of the parameters may change due to the wear of motor components. Such problems can be circumvented if an efficient parameter estimation technique is available. In order to automatically estimate the parameters efficiently, the present work proposes a method, based on the differential evolution algorithm, aimed at the estimation of the electrical and mechanical parameters of three-phase induction motors. Such algorithm is capable of estimating the parameters of the equivalent electrical circuit, such as stator and rotor resistances and leakage inductances, the magnetizing inductance, and also mechanical parameters, such as moment of inertia and the friction coefficient. The performance of the proposed parameter estimation technique is evaluated for three different input signals: (i) current signal of a phase associated with the speed measured from a tachogenerator, (ii) current signal of a phase associated with the speed acquired from a torquemeter, and (iii) only the current signal of one phase. Finally, a series of simulated and experimental results are presented to validate the proposed technique, and the results show the good performance of the proposed strategies.

Iterative MMSE‐based soft MIMO detection with parallel interference cancellation

Minimum mean square error (MMSE)-based techniques are often used for the joint iterative detection and decoding that is for coded multi-input-multi-output (MIMO) system due to a sound complexity and the performance trade-off. This study proposes an enhanced MMSE-based soft MIMO-detection scheme by using three main ideas. The first idea is an efficient complexity-reduced soft-bit estimation technique, the second one is a performance improvement method utilised inside the MMSE detection process, and the third one is a complexity-reduced soft-symbol estimation method for quadrature amplitude modulation. The proposed ideas enable the interference-cancellation processes to be activated in parallel on each symbol layer, thereby reducing the processing time. The simulation results show that the proposed method efficiently contributes to the improvement of the performance in addition to its reduction of the linear-order complexity.

Performance Analysis of Efficient Power Allocation Techniques with Different Modulation for 4X4 V-BLAST MIMO-OFDM System

Objectives: To propose an optimal power allocation method for high-speed V-BLAST MIMO-OFDM system for better performance in terms of SNR and BER than the traditional methods. Materials and Methods: To study and evaluate the performance of two linear channel estimators, the Least Square Error (LS) and Linear Minimum Mean Square Error (LMMSE) are used for 4 × 4 VBLAST Multiple input, multiple output-orthogonal frequency division multiplexing (MIMOOFDM) system with different modulation-4 QAM,16QAM and 64 QAM. For power allocation, optimal power is determined by the Greedy Power Allocation and the Cuckoo Search algorithm depending on the condition of the carriers measured from the channel estimation. Findings: Based on the detection method, Vertical Bell Labs Layered Space-Time (V-BLAST) MIMO-OFDM is found to be the high-speed system. Here, the proposed optimal power allocation method for high-speed V-BLAST MIMO-OFDM system showed a better performance in terms of Signal to Noise Ratio (SNR) and BER than the traditional methods. This thesis work has been evaluated based on different channel estimation with different modulation techniques for V-BLAST MMSE MIMO-OFDM System. The test program has been implemented in the latest technology system in Matlab R2013a. The experimental results show that our proposed method achieves better performance in terms of Bit Error Rate (BER) and SNR compared with the existing methods used for improving the performance of the MIMOOFDM. Linear MMSE proved efficient channel estimation technique over LS. MMSE -OSIC achieves very good performance with respect to other detectors in VBLAST MIMO-OFDM environment for 4× 4 MIMO-OFDM systems. Application: This result would be useful to make efficient Adaptive MIMO-OFDM system. It would be also useful in adaptive modulation and power allocation concept for making better wireless communication system. This performance analysis can perform for different channel models.

A Numerical Approach with Residual Error Estimation for Eolution of High-order Linear Differential-difference Equations by Using Gegenbauer Polynomials

The main aim of this study is to apply the Gegenbauer polynomials for the solution of high-order linear differential difference equations with functional arguments under initial-boundary conditions.The technique we have used is essentially based on the truncated Gegenbauer series and its matrix representations along with collocation points. Also, by using the Mean-Value Theorem and residual function, an efficient error estimation technique is proposed and some illustrative examples are presented to demonstrate the validity and applicability of the method.

Efficient Bayesian compressed sensing-based channel estimation techniques for massive MIMO-OFDM systems

Efficient and highly accurate channel state information (CSI) at the base station (BS) is essential to achieve the potential benefits of massive multiple input multiple output (MIMO) systems. However, the achievable accuracy that is attainable is limited in practice due to the problem of pilot contamination. It has recently been shown that compressed sensing (CS) techniques can address the pilot contamination problem. However, CS-based channel estimation requires prior knowledge of channel sparsity to achieve optimum performance, also the conventional CS techniques show poor recovery performance for low signal to noise ratio (SNR). To overcome these shortages, in this paper, an efficient channel estimation approach is proposed for massive MIMO systems using Bayesian compressed sensing (BCS) based on prior knowledge of statistical information regarding channel sparsity. Furthermore, by utilizing the common sparsity feature inherent in the massive MIMO system channel, we extend the proposed Bayesian algorithm to a multi-task (MT) version, so the developed MT-BCS can obtain better performance results than the single task version. Several computer simulation based experiments are performed to confirm that the proposed methods can reconstruct the original channel coefficient more effectively when compared to the conventional channel estimator in terms of estimation accuracy.

Allometry data and equations for coastal marsh plants

AbstractCoastal marshes are highly valued for ecosystem services such as protecting inland habitats from storms, sequestering carbon, removing nutrients and other pollutants from surface water, and providing habitat for fish, shellfish, and birds. Because plants largely determine the structure and function of coastal marshes, quantifying plant biomass is essential for evaluating these ecosystem services, understanding the biogeochemical processes that regulate ecosystem function, and forecasting tidal wetland responses to accelerated sea level rise. Allometry is a convenient and efficient technique for nondestructive estimation of plant biomass, and it is commonly used in studies of carbon and nitrogen cycles, energy flows, and marsh surface elevation change. We present plant allometry data and models developed for three long‐term experiments at the Smithsonian Global Change Research Wetland, a brackish marsh in the Rhode River subestuary of the Chesapeake Bay. The dataset contains 9,771 measurements of stem height, dry mass, and (in 9638 cases) stem width across 11 plant species. The vast majority of observations are for Schoenoplectus americanus (8430) and Phragmites australis (311), with fewer observations for other common species: Amaranthus cannabinus, Atriplex patula, Iva frutescens, Kosteletzkya virginica, Polygonum hydropiper, Solidago sempervirens, Spartina alterniflora, Spartina cynosuroides, and Typha angustifolia. Allometric relationships take the form of linear regressions of biomass (transformed using the Box‐Cox procedure) on either stem height and width, or on stem height alone. Allometric relationships for Schoenoplectus americanus were not meaningfully altered by elevated CO2, N enrichment, the community context, interannual variation in climate, or year, showing that a single equation can be used across a broad range of conditions for this species. Archived files include: (1) raw data used to derive allometric equations for each species, (2) reports and evaluations of the allometric equations we derived from the data, and (3) R code with which our derivations can be replicated. Methodological details of our experiments, data collection efforts, and statistical modeling are described in the metadata. The allometric equations can be used for biomass estimation in empirical and modeling studies of North American coastal wetlands, and the data can be used in ecological studies of terrestrial plant allometry.

Dynamic Estimation of the Auditory Temporal Response Function From MEG in Competing-Speaker Environments.

A central problem in computational neuroscience is to characterize brain function using neural activity recorded from the brain in response to sensory inputs with statistical confidence. Most of existing estimation techniques, such as those based on reverse correlation, exhibit two main limitations: first, they are unable to produce dynamic estimates of the neural activity at a resolution comparable with that of the recorded data, and second, they often require heavy averaging across time as well as multiple trials in order to construct statistical confidence intervals for a precise interpretation of data. In this paper, we address the above-mentioned issues for estimating auditory temporal response function (TRF) as a parametric computational model for selective auditory attention in competing-speaker environments. The TRF is a sparse kernel which regresses auditory MEG data with respect to the envelopes of the speech streams. We develop an efficient estimation technique by exploiting the sparsity of the TRF and adopting an ℓ1-regularized least squares estimator which is capable of producing dynamic TRF estimates as well as confidence intervals at sampling resolution from single-trial MEG data. We evaluate the performance of our proposed estimator using evoked MEG responses from the human brain in an auditory attention experiment with two competing speakers. The TRFs are estimated dynamically over time using the proposed technique with multisecond resolution, which is a significant improvement over previous results with a temporal resolution of the order of a minute. Application of our method to MEG data reveals a precise characterization of the modulation of M50 and M100 evoked responses with respect to the attentional state of the subject at multisecond resolution. Our proposed estimation technique provides a high resolution real-time attention decoding framework in multispeaker environments with potential application in smart hearing aid technology.