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Random forest cannot give accurate and calibrated posterior class probability estimates for its predictions. In this paper, we propose novel probabilities estimators combining random forests with kernel density estimation. Kernel density estimator can manage to obtain smooth non-parametric estimations of class probabilities, but fail to scale up to the high dimensional data. In order to apply kernel...
High-speed I/O link plays an important role in modern computer systems. In order to accurately estimate a small BER value in the order of 10−12, a large number of bits need to be transmitted, which results in expensive testing cost. In this paper, we exploit the correlation between the performance of high-speed I/O link under different corners/configurations to improve the accuracy of the estimated...
With the aggressive scaling of integrated circuit technology, analog performance modeling is facing enormous challenges due to high-dimensional variation space and expensive transistor-level simulation. In this paper, we propose a kernel density based sparse regression algorithm (KDSR) to accurately fit analog performance models where the modeling error is not simply Gaussian due to strong nonlinearity...
Fingerprinting based localization is one of the most widely used indoor localization methods. This method is divided into two phases: during the off-line training phase, fingerprints within the area of interest are collected and stored in a fingerprint database; during the on-line mapping phase, the real-time location of a device is estimated by mapping itself to the most accurate fingerprint in the...
In this paper, we propose a novel pilot-aided channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems where the wireless channel is assumed to be both sparse and time-varying. In the proposed method, we firstly model the time-varying sparse channel as an autoregressive (AR) process. Then, utilizing the time-domain convergence property of Kalman filter, we formulate...
For non-sample-spaced multipath channels, multi-path energy leakage leads to an increase in the channel sparsity and detection difficulties. In this paper, we propose the sparsity adaptive matching pursuit (SAMP) algorithm for the estimation of non-sample-spaced multipath channels. Compared with other greedy algorithms, the most innovative feature of the SAMP algorithm is its capability of signal...
It is well known that the least squares estimation of ARMAX models is biased. In this paper, by combining the principle of bias compensation and hierarchical identification, a new identification is established for this equation error model with moving average noises. The proposed estimate of the system parameter is given by the least squares estimate modified by a correction term. A numerical example...
In this work, we present a framework to detect objects embedded in complex perspective geometry. Our goal is to accurately identify objects such as people standing in balconies or windows on building facades of surrounding buildings. Compared to traditional computer vision work focused on activity analysis from a horizontal view, our framework provides a solution for the application domain of mobile...
In wireless orthogonal frequency division multiplexing (OFDM) systems, the knowledge of signal-to-noise ratio (SNR) plays an important role for system optimization. Most of the exiting literatures have studied the SNR estimation for perfect synchronization in additive white Gaussian noise (AWGN) channels, or in frequency selective channels. However, the realistic channels are always doubly selective,...
Direct causality detection is an important and challenging problem in root cause and hazard propagation analysis. Several methods provide effective solutions to this problem for linear relationships. For nonlinear situations, currently only causality analysis can be conducted, but the direct causality cannot be identified based on process data. In this paper, we describe a direct causality detection...
In this paper, a joint frame detection and timing estimation scheme for burst Orthogonal Frequency Division Multiplexing (OFDM) systems is proposed in double selective channels. The proposed scheme based on the training sequence is not sensitive to carrier frequency offset (CFO) and the given threshold for frame detection is robust to different signal-to-noise ratio (SNR). Simulation results demonstrate...
In this paper, a novel rapid varying doubly-selective channel estimator for OFDM systems has been proposed, in which channel is modeled by basis expansion model (BEM). Unlike traditional BEM channel estimation algorithms, in which the comb-pilot is used and the large path delay is not considered, the proposed algorithm uses firstly block-type pilot to estimate the BEM coefficients of fast-varying...
In this paper, a new non-data aided estimator of the technical parameters of full responsible binary CPM signals has been proposed. The proposed estimator can be used in non-cooperative communication systems for estimating the carrier frequency offset. The simulation results show that the proposed estimator is very efficiency for full responsible binary CPM signals.
Flight delay is a nondeterministic problem. Modeling and estimating flight delay is very important in the flight delay research. It is also the precondition to calculate delay propagation. A new Bayesian network structure learning algorithm, named target-fixed stochastic-ordered K2(TSK2), has been proposed in this paper. After using this new algorithm to build the Bayesian network of flight delay,...
An adaptive diamond search algorithm is proposed for motion estimation algorithm of H.264 video compression standard, owing to the relationship between the quality of video and the running time of algorithm, using the motion vector of space-time correlation and center biased distribution in the neighboring images and in the same image. Based on the SAD of present point, forecast its motive type, reduce...
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