This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cart...

Recursive credibility estimation is discussed from the viewpoint of linear filtering theory. A conjunction of geometric mterpretation and the innovation approach leads to general algorithms not developed before. Moreover, covariance characterizations considered by other researchers drop our elegantly as a result of geometric considerations. Examples are presented of Kalman type filters valid fo...

objective the aim of this research was determine the effectiveness of theory of mind training on the promotion of theory of mind levels in educable intellectual disability students.method: design of research was experimental with pre test- pos test along control . group: from all intellectual disability girl student exceptional school (mashhad city, andisheh school),30 students were selected af...

Automatic speech recognizers perform poorly when training and test data are systematically different in terms of noise and channel characteristics. One manifestation of such differences is variations in the probability density functions (pdfs) between training and test features. Consequently, both automatic speech recognition and automatic speaker identification may be severely degraded. Previo...

Variation-source identification in manufacturing processes is highly desired since it enables improvements in product quality. Recently, data-driven variation-source identification has received considerable attention. This paper presents a systematic variation-source identification method by assuming a linear model between the quality measurements and process faults. The noise term in the model...

In multiantenna systems the optimization of linear spatial precoding is severely hampered by the amount of feedback. An efficient solution consists in the opportunistic beamforming (OB), which randomly generates the precoding and schedules the users according to the corresponding signal-to-noise ratio. Performance of OB can be enhanced by matching the generation of the beamforming to the users ...

In this work a novel Multi-Sensor Data Fusion (MSDF) architecture is presented. First, each measurement-vector coming from each sensor is fed to a Fuzzy Logic-based Adaptive Kalman Filter (FL-AKF); thus there are N sensors and N FL-AKFs working in parallel. The adaptation in each FL-AKF is in the sense of dynamically tuning the measurement noise covariance matrix R employing a fuzzy inference s...

It is well known that the uncertainty of the covariance parameters of the process noise (Q) and the observation errors (R) has a significant impact on Kalman filtering performance. Q and R influence the weight that the filter applies between the existing process information and the latest measurements. Errors in any of them may result in the filter being suboptimal or even cause it to diverge. ...

Herein, a novel eigenstructure-based method for direction estimation is presented. The method assumes that the emitter signals are uncorrelated. Ideas from subspace and covariance matching methods are combined to yield a non-iterative estimation algorithm when a uniform linear array is employed. The large sample performance of the estimator is analyzed. It is shown that the asymptotic variance ...

This paper demonstrates how the EM algorithm can be used for learning and matching mixtures of point distribution models. We make two contributions. First, we show how shape-classes can be learned in an unsupervised manner. We present a fast procedure for training point distribution models using the EM algorithm. Rather than estimating the class means and covariance matrices needed to construct...