In this paper a new variation of the well known MAE (Mean Absolute Error) matching criterion for video coding applications, is introduced. The MAE criterion is simpler than the MSE criterion because of the lack of multiplication operations, but it achieves a lower performance in terms of PSNR and MSE. In this work we impose an additional statistical constraint over the MAE thus filling the perf...

In this paper, we model complex signals by approximating the phase and the logarithm of the time-varying amplitude of the signal as a finite-order polynomial. We refer to a signal that has this form as an exponential polynomial signal (EPS). We derive an iterative maximum-likelihood (ML) estimation algorithm to estimate the unknown parameters of the EPS model. The initialization of the ML algor...

where Cij = 1 1+λ ( uiujβ lilj √ α ), C ′ ij = 1 (1+λ)α(lilj) , and the model converges to the optimal model, then the MSE of λG(wt) is smaller than the MSE of G(wt) in approximating Hessian H(wt). Proof: For simplicity, we abbreviate E(Y |x,w∗) as E, Gt as G(wt) and Ht as H(wt). First, we calculate the MSE of Gt, λGt to approximate Ht for each element of Gt. We denote the element in the i-th r...

The threshold region mean squared error (MSE) performance of the Capon-MVDR algorithm is predicted via an adaptation of an interval error based method referred to herein as the method of interval errors (MIE). MIE requires good approximations of two quantities: (i) interval error probabilities, and (ii) the algorithm asymptotic (SNR→ ∞) MSE performance. Exact pairwise error probabilities for th...

Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (SE) on different scales...

Abstract. We consider the problem of estimating a vector x in the linear model Ax ≈ y, where A is a block circulant (BC) matrix with N blocks and x is assumed to have a weighted norm bound. In the case where both A and y are subjected to noise, we propose a minimax mean-squared error (MSE) approach in which we seek the linear estimator that minimizes the worst-case MSE over a BC structured unce...

To quantify and characterize seasonal variation in monosymptomatic optic neuritis (MON) onsets, multiple sclerosis (MS) onsets and MS exacerbations (MSE), a meta-analysis was performed, using established methods and pooling weighted information obtained from nine reports on MON, six reports on MS onsets and nine reports on MSE, which fulfilled specific criteria for report quality and data homog...

The Signal-to-Interference-plus-Noise Ratio (SINR) is an important metric of wireless communication link quality. SINR estimates have several important applications. These include optimizing the transmit power level for a target quality of service, assisting with handoff decisions and dynamically adapting the data rate for wireless Internet applications. Accurate SINR estimation provides for bo...

We propose a novel method for reducing the number of variables in quadratic unconstrained binary optimization problems, using a quantum annealer to fix the value of a large portion of the variables to values that have a high probability of being optimal. This method significantly increases the success rate and number of observations of the best known energy value in the sample obtained from the...

One approach to solving planning problems is to compile them to another problem for which powerful off-the-shelf solvers are available; common targets include SAT, CSP, and MILP. Recently, a novel optimization technique has become available: quantum annealing (QA). QA takes as input problem instances encoded as Quadratic Unconstrained Binary Optimization (QUBO). Early quantum annealers are now ...