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We consider the problem of estimating the parameters of a vector autoregressive (VAR) process from low-dimensional random projections of the observations. This setting covers the cases where we take compressive measurements of the observations or have limits in the data acquisition process associated with the measurement system and are only able to subsample. We first present fundamental bounds on...
Estimating mutual information from i.i.d. samples drawn from an unknown joint density function is a basic statistical problem of broad interest with multitudinous applications. The most popular estimator is one proposed by Kraskov and Stogbauer and Grassberger (KSG) in 2004, and is nonparametric and based on the distances of each sample to its kth nearest neighboring sample, where k is a fixed small...
We prove convergence of the projected gradient algorithm with inexact projections when applied to linear inverse problems with constraint sets that are unions of subspaces. Such an algorithm is useful for joint angle and delay estimation in MIMO radar, where classical estimators for angle estimation can be integrated into compressive sensing methods for range estimation.
The estimation of Origin — Destination (OD) matrix is a methodologically and computationally challenging, yet essential step in setting up a transportation planning model. In this process, demand (and supply) parameters need to be calibrated to match the simulation output with real observations. In this paper, we investigate how information extracted from a Jacobian matrix can be applied to improve...
Convergence of Gibbs fields modeling procedures is evaluated. Evaluation of convergence time is based on analysis of a dynamics of random field realizations space-correlation characteristics during the process of Gibbs fields modeling. Gibbs fields modeling procedure stop time is estimated.
A good initial guess is critical for the convergence of the steepest descent method. This paper presents a new method to estimate the initial guess of the near field phase. Based on image theory, tangential near fields on two scanning planes are linked by a radiation matrix. The radiation matrix is then scaled by the near field amplitudes. It is shown that the phase of an eigenvector of the scaled...
In this paper, a Model Free Control based Nonlinear Integral Backstepping Control (MFC-NIB) strategy is developed and applied to blood glucose regulation systems, which is a typical biological system with parameter variations, uncertainties and external disturbances. Firstly, an Intelligent Proportional controller (iP), which is based on model-free theory and whose algebraic estimation technique is...
This paper proposes an improved version of sub-band adaptive notch (SAN) filters for detecting and eliminating multiple unknown sinusoids embedded in the broadband (in fact, white) signals. The proposed SAN filters enhance both the convergence speed and estimation accuracy especially when sinusoids have close angular frequencies, under which circumstances the original SAN filters suffer from convergence...
This paper presents a technique to estimate the time skew in time-interleaved ADCs. The proposed method estimates all of the time skew parameters jointly based on observations from a bank of correlators. The proposed method works for an arbitrary number of sub-ADCs. For implementation of the correlator bank, we propose the use of Mitchell's logarithmic multiplier and a hardware reuse mechanism, thereby...
The adoption of the digital/time converter (DTC) circuit has improved the performance of ΔΣ fractional-N phase-locked loops (PLLs). Accurate cancellation of ΔΣ quantization error via the DTC requires an automatic calibration made by an LMS loop. A high-order ΔΣ speeds up calibration convergence and improves PLL spectral purity, though at the price of larger quantization error and wider DTC range....
We present a novel solution for Channel Assignment Problem (CAP) in Device-to-Device (D2D) wireless networks that takes into account the throughput estimation noise. CAP is known to be NP-hard in the literature and there is no practical optimal learning algorithm that takes into account the estimation noise. In this paper, we first formulate the CAP as a Stochastic Optimization Problem (SOP) to maximize...
As more than 2.5 quintillion bytes of data are generated every day, the era of big data is undoubtedly upon us. Running analysis on extensive datasets is a challenge. Fortunately, a significant percentage of the data accrued can be omitted while maintaining a certain quality of statistical inference in many cases. Censoring provides us a natural option for data reduction. However, the data chosen...
Multiphase induction motor drives have gained popularity due to their fault tolerance and better power/current distribution per phase which are very attractive for industrial applications. Current control strategies with the estimation of unmeasurable variables (typically rotor variables) has been recently proposed for multiphase induction drive. This paper proposes a novel current control based on...
Indirect measurements of physical parameters of interest often require a mathematical model in which these parameters are estimated accordingly to the gathered measurements. Within the Least Squares estimation, the parameters are estimated through a regression problem. The presence of dynamics, multiple sensors and high sampling rates lead to high dimensional regression matrices. This paper deals...
Stochastic gradient algorithms are the main focus of large-scale optimization problems and led to important successes in the recent advancement of the deep learning algorithms. The convergence of SGD depends on the careful choice of learning rate and the amount of the noise in stochastic estimates of the gradients. In this paper, we propose an adaptive learning rate algorithm, which utilizes stochastic...
The aim of this paper is to introduce a controller that stabilizes a class of arbitrary order systems in predefined-time. The proposed controller is designed with basis on the block-control principle yielding in a nested structure similar to high order sliding mode algorithms and terminal sliding mode algorithms. For this case, it is assumed the availability of the state and the absence of perturbations...
This paper introduces a novel framework for the study of adaptive or online estimation problems for a common class of nonlinear systems governed by ordinary differential equations (ODEs) on ℝd. In contrast to most conventional strategies for ODEs, the approach here embeds the estimate of the unknown nonlinear function appearing in the plant in a reproducing kernel Hilbert space (RKHS), H. The nonlinear...
We consider the estimation of the state transition matrix in vector autoregressive models when the time sequence data is limited but nonsequence steady-state data is abundant. To leverage both sources of data, we formulate the problem as the least-squares minimization regularized by a Lyapunov penalty. Explicit cardinality or rank constraints are imposed to reduce the complexity of the model. The...
We develop an abstract approximation and convergence framework for the estimation of random parameters in infinite dimensional dynamical systems governed by regularly dissipative operators in a Gelfand triple setting. Our results are motivated by a problem involving the development of a data analysis system for a transdermal alcohol biosensor. Our approach combines some recent results for random abstract...
Nonline-of-sight (NLOS) error, which is the key factor of effect positioning accuracy, coupled with the presence of noise deviation of the environment itself, results in great location deviation. A joint algorithm of GA and iterative LS algorithm is proposed in this paper. The GA is used to get a relatively accurate initial value. And then the iterative LS algorithm is used to update the position...
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