Search results for "Signal recovery"
showing 3 items of 3 documents
Shifting of wrapped phase maps in the frequency domain using a rational number
2016
The number of phase wraps in an image can be either reduced, or completely eliminated, by transforming the image into the frequency domain using a Fourier transform, and then shifting the spectrum towards the origin. After this, the spectrum is transformed back to the spatial domain using the inverse Fourier transform and finally the phase is extracted using the arctangent function. However, it is a common concern that the spectrum can be shifted only by an integer number, meaning that the phase wrap reduction is often not optimal. In this paper we propose an algorithm than enables the spectrum to be frequency shifted by a rational number. The principle of the proposed method is confirmed b…
Effectiveness of early administration of a single dose of steroids and escin after loss of signal on electromyographic signal recovery during neuromo…
2022
Background: The aim of this study was to evaluate the effect of a single early administration of dexamethasone and escin after loss of signal (LOS) during a neuromonitored total thyroidectomy. Methods: A retrospective analysis of results concerning consecutive patients undergoing total thyroidectomy was performed. Patients included in the study were divided into two groups: Group 1 for which a “wait and see” strategy was used; Group 2, receiving dexamethasone and escin immediately after LOS detection. Results: Overall 37 patients were included in Group 1 and 35 in Group 2. LOS recovery occurring in 29.7% of cases (n. 11) versus 65.7% (n. 23) respectively (p < 0.001). Postoperative fibrol…
JOINT TOPOLOGY LEARNING AND GRAPH SIGNAL RECOVERY VIA KALMAN FILTER IN CAUSAL DATA PROCESSES
2018
In this paper, a joint graph-signal recovery approach is investigated when we have a set of noisy graph signals generated based on a causal graph process. By leveraging the Kalman filter framework, a three steps iterative algorithm is utilized to predict and update signal estimation as well as graph topology learning, called Topological Kalman Filter or TKF. Similar to the regular Kalman filter, we first predict the a posterior signal state based on the prior available data and then this prediction is updated and corrected based on the recently arrived measurement. But contrary to the conventional Kalman filter algorithm, we have no information of the transition matrix and hence we relate t…