Efficient Estimation Algorithm Research Articles

Efficient Impulsive Noise Mitigation for OFDM Systems Using the Alternating Direction Method of Multipliers

An efficient impulsive noise estimation algorithm based on alternating direction method of multipliers (ADMM) is proposed for OFDM systems using quadrature amplitude modulation (QAM). Firstly, we adopt the compressed sensing (CS) method based on the l1-norm optimization to estimate impulsive noise. Instead of the conventional methods that exploit only the received signal in null tones as constraint, we add the received signal of data tones and QAM constellations as constraints. Then a relaxation approach is introduced to convert the discrete constellations to the convex box constraints. After that a linear programming is used to solve the optimization problem. Finally, a framework of ADMM is developed to solve the problem in order to reduce the computation complexity. Simulation results for 4-QAM and 16-QAM demonstrate the practical advantages of the proposed algorithm over the other algorithms in bit error rate performance gains.

Intelligent Vehicle Counting and Classification Sensor for Real-Time Traffic Surveillance

Real-time traffic surveillance is essential in today's intelligent transportation systems and will surely play a vital role in tomorrow's smart cities. The work detailed in this paper reports on the development and implementation of a novel smart wireless sensor for traffic monitoring. Computationally efficient and reliable algorithms for vehicle detection, speed and length estimation, classification, and time-synchronization were fully developed, integrated, and evaluated. Comprehensive system evaluation and extensive data analysis were performed to tune and validate the system for a reliable and robust operation. Several field studies conducted on highway and urban roads for different scenarios and under various traffic conditions resulted in 99.98% detection accuracy, 97.11% speed estimation accuracy, and 97% length-based vehicle classification accuracy. The developed system is portable, reliable, and cost-effective. The system can also be used for short-term or long-term installment on surface of highway, roadway, and roadside. Implementation cost of a single node including enclosure is US $50.

Unique entity estimation with application to the Syrian conflict

Entity resolution identifies and removes duplicate entities in large, noisy databases and has grown in both usage and new developments as a result of increased data availability. Nevertheless, entity resolution has tradeoffs regarding assumptions of the data generation process, error rates, and computational scalability that make it a difficult task for real applications. In this paper, we focus on a related problem of unique entity estimation, which is the task of estimating the unique number of entities and associated standard errors in a data set with duplicate entities. Unique entity estimation shares many fundamental challenges of entity resolution, namely, that the computational cost of all-to-all entity comparisons is intractable for large databases. To circumvent this computational barrier, we propose an efficient (near-linear time) estimation algorithm based on locality sensitive hashing. Our estimator, under realistic assumptions, is unbiased and has provably low variance compared to existing random sampling based approaches. In addition, we empirically show its superiority over the state-of-the-art estimators on three real applications. The motivation for our work is to derive an accurate estimate of the documented, identifiable deaths in the ongoing Syrian conflict. Our methodology, when applied to the Syrian data set, provides an estimate of $191\text{,}874\pm 1\text{,}772$ documented, identifiable deaths, which is very close to the Human Rights Data Analysis Group (HRDAG) estimate of 191,369. Our work provides an example of challenges and efforts involved in solving a real, noisy challenging problem where modeling assumptions may not hold.

Resilience analytics: coverage and robustness in multi-modal transportation networks

A multi-modal transportation system of a city can be modeled as a multiplex network with different layers corresponding to different transportation modes. These layers include, but are not limited to, bus network, metro network, and road network. Formally, a multiplex network is a multilayer graph in which the same set of nodes are connected by different types of relationships. Intra-layer relationships denote the road segments connecting stations of the same transportation mode, whereas inter-layer relationships represent connections between different transportation modes within the same station. Given a multi-modal transportation system of a city, we are interested in assessing its quality or efficiency by estimating the coverage i.e., a portion of the city that can be covered by a random walker who navigates through it within a given time budget, or steps. We are also interested in the robustness of the whole transportation system which denotes the degree to which the system is able to withstand a random or targeted failure affecting one or more parts of it. Previous approaches proposed a mathematical framework to numerically compute the coverage in multiplex networks. However solutions are usually based on eigenvalue decomposition, known to be time consuming and hard to obtain in the case of large systems. In this work, we propose MUME, an efficient algorithm for Multi-modal Urban Mobility Estimation, that takes advantage of the special structure of the supra-Laplacian matrix of the transportation multiplex, to compute the coverage of the system. We conduct a comprehensive series of experiments to demonstrate the effectiveness and efficiency of MUME on both synthetic and real transportation networks of various cities such as Paris, London, New York and Chicago. A future goal is to use this experience to make projections for a fast growing city like Doha.

Forecast of stock price fluctuation based on the perspective of volume information in stock and exchange market

PurposeIn the process of discussing the relationship between volume and price in the stock market, the purpose of this paper is to consider how to take the flow of foreign capital into consideration, to determine whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price.Design/methodology/approachBy comparing the relative advantages and disadvantages of the two main non-parametric methods mainstream, and taking the characteristics of the time series of the volume into consideration, the stochastic volatility with Volume (SV-VOL) model based on the APF-LW simulation method is used in the end, to explore and implement a more efficient estimation algorithm. And the volume is incorporated into the model for submersible quantization, by which the problem of insufficient use of volume information in previous research has been solved, which means that the development of the SV model is realized.FindingsThrough the Sequential Monte Carlo (SMC) algorithm, the effective estimation of the SV-VOL model is realized by programming. It is found that the stock market volume information is helpful to the prediction of the volatility of the stock price. The exchange market volume information affects the stock returns and the price-volume relationship, which is achieved indirectly through the net capital into stock market. The current exchange devaluation and fluctuation are not conducive to the restoration and recovery of the stock market.Research limitations/implicationsIt is still in the exploratory stage that whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price, and how to incorporate the exchange market volume information. This paper tries to determine the information weight of the exchange market volume according to the direct and indirect channels from the perspective of causality. The relevant practices and conclusions need to be tested and perfected.Practical implicationsPrevious studies have neglected the influence of the information contained in the exchange market volume on the volatility of stock prices. To a certain extent, this research makes a useful supplement to the existing research, especially in the aspects of research problems, research paradigms, research methods and research conclusion.Originality/valueSV model with volume information can not only effectively solve the inefficiency of information use problem contained in volume in traditional practice, but also further improve the estimation accuracy of the model by introducing the exchange market volume information into the model through weighted processing, which is a useful supplement to the existing literature. The SMC algorithm realized by programming is helpful to the further advancement and development of non-parametric algorithms. And this paper has made a useful attempt to determine the weight of the exchange market volume information, and some useful conclusions are drawn.

Median bias reduction in random-effects meta-analysis and meta-regression.

The reduction of the mean or median bias of the maximum likelihood estimator in regular parametric models can be achieved through the additive adjustment of the score equations. In this paper, we derive the adjusted score equations for median bias reduction in random-effects meta-analysis and meta-regression models and derive efficient estimation algorithms. The median bias-reducing adjusted score functions are found to be the derivatives of a penalised likelihood. The penalised likelihood is used to form a penalised likelihood ratio statistic which has known limiting distribution and can be used for carrying out hypothesis tests or for constructing confidence intervals for either the fixed-effect parameters or the variance component. Simulation studies and real data applications are used to assess the performance of estimation and inference based on the median bias-reducing penalised likelihood and compare it to recently proposed alternatives. The results provide evidence on the effectiveness of median bias reduction in improving estimation and likelihood-based inference.

Parameter estimation of LFM signal intercepted by improved dual‐channel Nyquist folding receiver

A Nyquist folding receiver (NYFR) is a novel ultra-wideband (UWB) receiver architecture, which can realise a wideband receiving with fewer components. Intercepted by the NYFR with a sinusoidal frequency modulation (SFM) local oscillator, a linear frequency modulation (LFM) signal will be converted into a LFM/SFM hybrid modulated signal. To simplify the processing of this kind of complicated signal, the authors propose improved dual-channel NYFR architecture with an efficient parameter estimation algorithm. Compared with the existing parameter estimation methods for the NYFR, the proposed architecture offers better estimation performance, higher signal type adaptability, and simpler operation.

A simple and efficient method for automatic strawberry shape and size estimation and classification

In strawberry production farms, shape and size classification of harvested strawberry fruits is very important phase before packing and sending to the market. However, it is not only very labour-intensive but also time-consuming task for farmers. Computer vision-based automatic strawberry grading systems are capable to overcome this labour-intensive and time-consuming process. In this work, a simple and efficient image processing algorithm for automatic strawberry shape and size estimation and classification is presented. Being different from other existing methods in literature, the current method is based on the geometrical properties of 'right kite' and 'simple kite' which resemble to strawberry fruit shape. The proposed method is used to estimate diameter, length and apex angle from two-dimensional images of strawberry fruits. Then, these parameters are used as input data to a 3-layer neural network for class-A, B, C and D classification. The performance of proposed method is tested for a total of 337 strawberry samples with and without calyx occlusion. The results show that the accuracies for diameter and length estimations are 94% and 93% respectively for strawberries without calyx occlusion and 94% and 89% for that with calyx occlusion. The classification accuracy is between 94 and 97% and the average processing time for one strawberry (one piece) is below 0.45–0.5 s.

LSMM: a statistical approach to integrating functional annotations with genome-wide association studies.

Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still two major challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in non-coding region and their biological interpretation is still unclear. Second, accumulating evidence from GWAS suggests the polygenicity of complex traits, i.e. a complex trait is often affected by many variants with small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown. The availability of functional annotation data enables us to address the above challenges. In this study, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase the statistical power of identifying risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWAS of complex phenotypes integrated with nine genic category annotations and 127 cell-type specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power than conventional methods, and can help researchers achieve deeper understanding of genetic architecture of these complex phenotypes. The LSMM software is available at https://github.com/mingjingsi/LSMM. Supplementary data are available at Bioinformatics online.

Affordable content creation for free-viewpoint video and VR/AR applications

We present a scalable pipeline for Free-Viewpoint Video (FVV) content creation, considering also visualisation in Augmented Reality (AR) and Virtual Reality (VR). We support a range of scenarios where there may be a limited number of handheld consumer cameras, but also demonstrate how our method can be applied in professional multi-camera setups. Our novel pipeline extends many state-of-the-art techniques (such as structure-from-motion, shape-from-silhouette and multi-view stereo) and incorporates bio-mechanical constraints through 3D skeletal information as well as efficient camera pose estimation algorithms. We introduce multi-source shape-from-silhouette (MS-SfS) combined with fusion of different geometry data as crucial components for accurate reconstruction in sparse camera settings. Our approach is highly flexible and our results indicate suitability either for affordable content creation for VR/AR or for interactive FVV visualisation where a user can choose an arbitrary viewpoint or sweep between known views using view synthesis.

Non parametric methods of disparity computation

Disparity is inversely proportional to depth. Informationabout depth is a key factor in many real time applicationslikecomputer vision applications, medical diagnosis, model precision etc. Disparity is measured first in order to calculate the depth that suitsthe real world applications. There are two approaches viz., active and passive methods. Due to its cost effectiveness, passive approach is the most popular approach. In spite of this, the measures arelimited by its occlusion, more number of objects and texture areas. So, effective and efficient stereo depth estimation algorithms have taken the toll on the researchers. Theimportant goal of stereo vision algorithms is the disparity map calculation between twoimages clicked the same time. These pictures are taken using two cameras. We have implemented the non-parametric algorithmsfor stereo vision viz., Rank and Census transform in both single processor and multicore processors are implemented andthe results showsits time efficient by 1500 times.

An efficient vision-based pose estimation algorithm using the assistant reference planes based on the perspective projection rays

The vision-based pose estimation using feature point markers is known as the PnP (perspective-n-point) problem. Usually the pose estimation algorithms use the pinhole imaging camera model. However, the traditional pinhole calibration may generate errors. It is a perspective projection algorithm using geometrical approximations to calibrate intrinsic and extrinsic parameters. It does not consider the lens system and only gives a single lumped result for the multiple optical elements. This leads to the accuracy reduction of pose estimation. To solve the problem, the pose estimation algorithm using a new camera model is presented in this paper. Two assistant reference planes are used to describe the camera model. The mapping relationship between the image plane and two assistant reference planes are established based on the perspective projection rays. Four coplanar feature points are used to solve the object pose. The expression of each perspective projection ray could be obtained through the mapping relationship between the image plane and two assistant reference planes. The geometrical constraints formed by the four points are then expressed with the perspective projection rays. The coordinates of feature points in the measurement system are calculated. The object pose is finally obtained. Both noise analysis and accuracy evaluation experiments were used to verify this pose estimation algorithm. Experiment results show that the algorithm in this paper can effectively reduce the influence of the pose estimation data noise and improve the pose estimation accuracy.

Mixed and componentwise condition numbers for a linear function of the solution of the total least squares problem

In this paper, we consider the mixed and componentwise condition numbers for a linear function Lx of the solution to the total least squares (TLS) problem. We derive the explicit expressions of the mixed and componentwise condition numbers through the dual techniques under both unstructured and structured componentwise perturbations. The sharp upper bounds for condition numbers are obtained. An efficient condition estimation algorithm is proposed, which can be integrated into the iterative method for solving large scale TLS problems. Moreover, the new derived condition number expressions can recover the previous results on the condition analysis for the TLS problem when L=In. Numerical experiments show the effectiveness of the introduced condition numbers and condition estimation algorithm.

Structural Variability from Noisy Tomographic Projections.

In cryo-electron microscopy, the three-dimensional (3D) electric potentials of an ensemble of molecules are projected along arbitrary viewing directions to yield noisy two-dimensional images. The volume maps representing these potentials typically exhibit a great deal of structural variability, which is described by their 3D covariance matrix. Typically, this covariance matrix is approximately low rank and can be used to cluster the volumes or estimate the intrinsic geometry of the conformation space. We formulate the estimation of this covariance matrix as a linear inverse problem, yielding a consistent least-squares estimator. For n images of size N-by-N pixels, we propose an algorithm for calculating this covariance estimator with computational complexity , where the condition number κ is empirically in the range 10-200. Its efficiency relies on the observation that the normal equations are equivalent to a deconvolution problem in six dimensions. This is then solved by the conjugate gradient method with an appropriate circulant preconditioner. The result is the first computationally efficient algorithm for consistent estimation of the 3D covariance from noisy projections. It also compares favorably in runtime with respect to previously proposed nonconsistent estimators. Motivated by the recent success of eigenvalue shrinkage procedures for high-dimensional covariance matrix estimation, we incorporate a shrinkage procedure that improves accuracy at lower signal-to-noise ratios. We evaluate our methods on simulated datasets and achieve classification results comparable to state-of-the-art methods in shorter running time. We also present results on clustering volumes in an experimental dataset, illustrating the power of the proposed algorithm for practical determination of structural variability.

State Estimation for Stochastic Time Varying Systems with Disturbance Rejection

State estimation in the presence of unknown disturbances is useful for the design of robust systems in different engineering fields. Most results available on this topic are restricted to linear time invariant (LTI) systems, whereas linear time varying (LTV) systems have been studied to a lesser extent. Existing results on LTV systems are mainly based on the minimization of the state estimation error covariance, ignoring the important issue of the stability of the state estimation error dynamics, which has been a main focus of the studies in the LTI case. The purpose of this paper is to propose a numerically efficient algorithm for state estimation with disturbance rejection, in the general framework of LTV stochastic systems, including linear parameter varying (LPV) systems, with easily checkable conditions guaranteeing the stability of the algorithm. The design method is conceptually simple: disturbance is first rejected from the state equation by appropriate output injection, then the Kalman filter is applied to the resulting state-space model after the output injection.

Accurate Quantile Estimation for Skewed Data Streams Using Nonlinear Interpolation

Quantile estimation is a fundamental method to generate the descriptions of the distribution of data for data management and analysis. Although the investigation and design of efficient quantile estimation algorithm has attracted much study, the problem of accurately finding quantiles in the case of skewed data streams, which are prevalent in many data sources like text data and IP traffic streams, is still not well addressed. In this paper, we specifically address the problem of estimating the quantiles of skewed data streams by designing and implementing an incremental quantile estimation with nonlinear-interpolation algorithm. The comprehensive experimental evaluation results demonstrate that the estimated quantiles of the proposed algorithm are more accurate than the existing methods in the literature on both synthetic and real-world datasets, especially on important extreme quantiles.

High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms

This paper proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility and exogenous predictors, as an equivalent high-dimensional static regression problem with thousands of covariates. Inference in this specification proceeds using Bayesian hierarchical priors that shrink the high-dimensional vector of coefficients either towards zero or time-invariance. Second, it introduces the frameworks of factor graphs and message passing as a means of designing efficient Bayesian estimation algorithms. In particular, a Generalized Approximate Message Passing (GAMP) algorithm is derived that has low algorithmic complexity and is trivially parallelizable. The result is a comprehensive methodology that can be used to estimate time-varying parameter regressions with arbitrarily large number of exogenous predictors. In a forecasting exercise for U.S. price inflation this methodology is shown to work very well.

Online wave estimation using vessel motion measurements

In this paper, a computationally efficient online sea state estimation algorithm is proposed for estimation of the onsite sea state. The algorithm finds the wave spectrum estimate from motion measurements in heave, roll and pitch by iteratively solving a set of linear equations. The main vessel parameters and motion transfer functions are required as input. Apart from this the method is signal-based, with no assumptions on the wave spectrum shape, and as a result it is computationally efficient. The algorithm is implemented in a dynamic positioning (DP) control system, and tested through simulations in different sea states, with heading changes. Discrete stability analysis is performed to find iteration gains in the algorithm.

Focus and Blurriness Measure Using Reorganized DCT Coefficients for an Autofocus Application

In this paper, two metrics for measuring image sharpness are presented and used for an autofocus (AF) application. Both measures exploit reorganized discrete cosine transform (DCT) representation. The first metric is a focus measure, which involves optimal high- and middle-frequency coefficients to evaluate relative sharpness. It is robust to noise while remaining sensitive to the best focus position. A psychometric function-based metric is introduced to quantify the focus measure. The second metric is a no-reference blurriness metric, which is used to measure absolute blurriness. It first constructs multiscale DCT edge maps using directional energy information and then determines image blurriness by combining change information in edge structures with image contrast. This metric gives predictions that are closely correlated with subjective perceived scores and shows performance comparable with that of state-of-the-art methods, especially for noisy images. For noisy situations, the two metrics are adjusted adaptively according to the estimated noise level. To prevent the introduction of extra computational load, an efficient noise-level estimation algorithm based on median absolute deviation is presented. This algorithm exploits only the available reorganized DCT coefficients. With the focus and blurriness measures, an AF method for which the two metrics play an important role was developed. Because of their high-quality performance, the realized AF function is able to locate the best focus position swiftly and reliably.

Adaptive Optimal Control of Wave Energy Converters

This paper proposes a hierarchical adaptive optimal control framework for wave energy converters (WECs) to improve their energy conversion efficiency and reduce the required modeling effort to facilitate the control design. Since the WEC dynamics vary significantly at various operation scenarios with different sea states, an efficient adaptive parameter estimation (APE) algorithm is employed to online update several critical WEC model parameters (e.g. the radiation force and excitation force generation coefficients). Based on the updated model, the predesigned non-causal optimal controller can maintain its efficacy. Thus the proposed method combines the strength of optimal control for generating maximum energy output and APE in coping with model parameter variations. Simulation results demonstrate that the proposed optimal control with APE can cope with the model mismatch and variations effectively.