Wyniki wyszukiwania - Biblioteka Nauki

Ten serwis zostanie wyłączony 2025-02-11.

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

2 Advances in Science and Technology. Research Journal

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: Bayesian filtering

Sortuj według:

Ogranicz wyniki do:

Application of Particle Filter in Path-loss Modelling

100%

Wójcicki P. , Zientarski T. , Przyłucki S.

tom Vol. 17, no 5

337--349

The article presents the dynamic estimation method of the path loss exponent parameter in the function of the distance based on the conducted measurements. A specific feature of this solution is its suitability for distance estimation on devices which are characterised by a small amount of resources. The presented method allows to provide an acceptable precision of distance estimation while using a relatively small measurement set. For this purpose, real RSSI (Received Signal Strength Indicator) measurements were used and estimation of the path-loss exponent was performed with the use of a Bayesian particle filter. The article, apart from a detailed demonstration of the algorithms, presents the results of the sensitivity analysis of this method to change the number of inserted particles and of the repetitions of calculations needed to estimate the path loss exponent. Additionally, the results of the model stability study on the size change of the experimental dataset RSSI are presented. The properties and accuracy of the proposed method are verified based on a set of actual measurement data. All the obtained results indicate the utility of the Bayesian filtering method for effective estimation of the path loss exponent and confirm the possibility of using the described method in systems with a limited amount of computing resources.

Nonlinear Nonlocal Algorithm for Video Filtering

63%

El Ouafdi A. F. , El Houari H.

Advances in Science and Technology. Research Journal

2021

tom Vol. 15, no 4

243--252

Video sequences are frequently contaminated by noise throughout the acquisition process, resulting in considerable degradation of video display quality. In this paper, we present a novel method of video filtering. The proposed filter is developed from an optimization problem in which a Bayesian term and a noisy video sequence prior distribution are combined. The method begins by segmenting the video sequence into space-time blocks and then substituting each noisy block by a weighted average of non-local neighbor blocks. Gradient-based weights are used to dynamically adjust the edge preservation and smoothness of the reference block. The obtained formulation enables nonlinear filtering and, hence, preserving key features such as edges and corners while using the intrinsic Bayesian filtering framework. Experiments on different video sequences with varying degrees of noise show that the proposed method performs better than state-of-the-art video filtering approaches.