A comprehensive uncertainty, baseline, and noise analysis in computing 3D points using a recent L1-based triangulation algorithm is presented. This method is shown to be not only faster and more accurate than its main competitor, linear triangulation, but also more stable under noise and baseline changes. A Monte Carlo analysis of covariance and a confidence ellipsoid analysis were performed ov...

A method is proposed for determining dynamical and observational noise parameters in state and parameter identification from time series using Kalman filters. The noise covariances are estimated in a secondary optimization by maximizing the predictive likelihood of the data. The approach is based on internal consistency; for the correct noise parameters, the uncertainty projected by the Kalman ...

Heisenberg’s uncertainty relation for measurement noise and disturbance states that any position measurement with noise ǫ brings the momentum disturbance not less than h̄/2ǫ. This relation holds only for restricted class of measuring apparatuses. Here, Heisenberg’s uncertainty relation is generalized to a relation that holds for all the possible quantum measurements, from which conditions are ob...

Brain decoding algorithms form an important part of the arsenal of analysis tools available to neuroscientists, allowing for a more detailed study of the kind of information represented in patterns of cortical activity. While most current decoding algorithms focus on estimating a single, most likely stimulus from the pattern of noisy fMRI responses, the presence of noise causes this estimate to...

Our movements are variable, but the origin of this variability is poorly understood. We examined the sources of variability in human saccadic eye movements. In two experiments, we measured the spatiotemporal variability in saccade trajectories as a function of movement direction and amplitude. One of our new observations is that the variability in movement direction is smaller for purely horizo...

Although deep neural network (DNN) based acoustic models have obtained remarkable results, the automatic speech recognition (ASR) performance still remains low in noise and reverberant conditions. To address this issue, a speech enhancement front-end is often used before recognition to reduce noise. However, the front-end cannot fully suppress noise and often introduces artifacts that are limit...

Additive fuzzy systems combined with supervised or unsupervised learning have been proposed to deal with impulsive noise in discrete-time signals, but suffer from the curse of dimensionality. Faster methods, possibly based on new paradigms, would be welcome. Order statistics filters and their fuzzy counterparts provide alternative and robust ways of dealing with discrete-time data, but the idea...

In this paper, we consider the problem of cooperative spectrum sensing in the presence of the noise power uncertainty. We propose a new spectrum sensing method based on the fuzzy hypothesis test (FHT) that utilizes membership functions as hypotheses for the modeling and analyzing such uncertainty. In particular, we apply the Neyman–Pearson lemma on the FHT and propose a threshold-based local de...