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In this paper, a generalization of the Misspecified Cramér-Rao Bound (MCRB) and of the Constrained MCRB (CMCRB) to complex parameter vectors is presented. Our derivation aims at providing lower bounds on the Mean Square Error (MSE) for both circular and non-circular, MS-unbiased, mismatched estimators. A simple toy example is also presented to clarify the theoretical findings.
In this paper, we address a wiener-process degradation model to estimate the remaining useful lifetime of aero-engines. The measurement error and unit-to-unit variability are taken into account in the proposed degradation model and the RUL estimation is derived by the concept of first hitting time. Typically, the unknown parameters of the degradation model were estimated based on historical degradation...
The last decades have witnessed the development of degradation modeling based remaining useful life (RUL) estimation, especially for Wiener process based degradation models. However, most researchers paid their attention to the drift coefficient representing the degradation rate, while much fewer eyes focus on the diffusion coefficient. The under consideration of diffusion process may cause bias in...
Inference and Estimation in Missing Information (MI) scenarios are important topics in Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been made to enhance prediction through precise feature selection methods. In sparse linear models, LASSO is well-known in extracting the desired support of the signal and resisting against noisy systems. When sparse models are...
Conflict resolution is the problem of finding true value among different and controversial facts about a single entity provided by different data sources such as web sites. All of the previous studies have relied on estimating two basic parameters, accuracy of data and reliability of sources. These methods are dealing with some challenges such as knowing distribution of data a priori or assumption...
Traditional matrix factorization methods approximate high dimensional data with a low dimensional subspace. This imposes constraints on the matrix elements which allow for estimation of missing entries. A lower rank provides stronger constraints and makes estimation of the missing entries less ambiguous at the cost of measurement fit. In this paper we propose a new factorization model that further...
Attribute-based recognition models, due to their impressive performance and their ability to generalize well on novel categories, have been widely adopted for many computer vision applications. However, usually both the attribute vocabulary and the class-attribute associations have to be provided manually by domain experts or large number of annotators. This is very costly and not necessarily optimal...
Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on the alignment of nearby frames. However, aligning images is a computationally expensive and fragile procedure, and methods...
Classification methods typically make use only of labeled data, in what is known as supervised learning. In some applications, however, labeled data is either scarce or costly to obtain. For these applications, unsupervised or semisupervised learning are adequate, since they will be able to use unlabeled data. This work proposes a new method for unsupervised and semisupervised learning of non-Gaussian...
One of the key technologies to take full advantage of wind power is to establish a wind turbine (WT) generator output estimation system with high accuracy. The static feed forward artificial neural network is widely used in previous WT generator output estimation technology. However, this method has many problems such as local minimization, a lack of dynamics, edge effect, and multi-correlation. To...
We consider the task of automated estimation of facial expression intensity. This involves estimation of multiple output variables (facial action units — AUs) that are structurally dependent. Their structure arises from statistically induced co-occurrence patterns of AU intensity levels. Modeling this structure is critical for improving the estimation performance, however, this performance...
Vehicle dynamics have very complex characteristic and nonlinear behaviour. Vehicle dynamics are decomposed of many internal and external components which influence vehicle stability. External components come from environment such as wind forces, surface coarse of road, lane bend or sudden maneuver, which will change the value of vehicle stability parameters, i.e. yaw rate and sideslip. Both are influenced...
While Golomb-Rice codes are optimal for geometrically distributed source, the practically achievable coding efficiency depends on the accuracy of the coding parameter estimated from the input data. Most existing methods are based on the assumption of geometric distribution and thus would suffer from a loss in coding efficiency if the underlying distribution deviates from the geometric distribution,...
Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest aboveground biomass (AGB), many difficulties remain in data resources and prediction approaches [1, 2]. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper...
In this paper, three approaches to forest change modeling with the two-level model (TLM) are compared by fitting the TLM to 12 VV-polarized TanDEM-X acquisitions over a hemi-boreal test site in southern Sweden. It is observed that the best inversion results are obtained when rapid forest change (e.g., harvesting) is modeled as change in canopy density, while growth is modeled as change in forest height.
Air pollution poses disproportionate health hazard in developing countries, due to juxtaposition of industrial units and residences. While excessive emission is routinely detected, enforcement of emission norms remains rare due to present technological limitations in pinpointing sources. To fill this gap, we propose a method of source localization and emission rate estimation via AERMOD-based simulation...
The traffic monitoring system is an imperative tool for traffic analysis and transportation planning. In this paper, we present WiTraffic: the first WiFi-based traffic monitoring system. Compared with existing solutions, it is non-intrusive, cost- effective, and easy-to-deploy. Unique WiFi Channel State Information (CSI) patterns of passing vehicles are captured and analyzed to effectively perform...
We propose PathML, an available bandwidth (i.e., unused capacity of an end-to-end path) estimation method based on a data-driven paradigm that uses machine learning with a large amount of data. An experiment over an operational LTE network was performed to compare our method with prior work.
Allowing for a priori optimization of the robot manipulation to improve the performance in the unmanned environment, it is critical for the augmented reality system to estimate the attitude of point clouds in model reconstruction. The estimation of planar parameter is not always faithful for point cloud fitting, because the gross errors and outliers are not considered in by the traditional plane fitting...
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