Gaussian frequency shift keying is the modulation scheme specified for Bluetooth. Signal adversities typical in Bluetooth networks include AWGN, multipath propagation, carrier frequency, and modulation index offsets. In our effort to realise a robust but efficient Bluetooth receiver, we adopt a high-performance matched-filter-based detector, which is near optimal in AWGN, but requires a prohibi...

A matched filter maximizes the signal-to-noise ratio of a signal. In the recent work of Corron et al. [Chaos 20, 023123 (2010)], a matched filter is derived for the chaotic waveforms produced by a piecewise-linear system. This system produces a readily available binary symbolic dynamics that can be used to perform correlations in the presence of large amounts of noise using the matched filter. ...

This article presents a new image denoising algorithm that uses Gaussian Symmetric Markov random fields based on maximum posteriori estimation. First, an model is built, and the problem was converted to estimation problem. The prior probability of can be estimated using Gibbs distribution, which equivalent fields. Second, calculated expectation-maximization conjugate gradient method, where used...

The Probability Hypothesis Density (PHD) filter is a multipletarget filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operate in environments with false alarms and missed detections. Two distinct algorithmic implementations of this technique have been developed. The first of which, called the Particle PHD filter, req...

The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian Lévy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian Lévy noise may have infinite variance. A modified Kalman filter for linear systems with non-Gaussian Lévy noise is devised. It works effectively with reasonable c...

In this work we present an incremental Bayesian model to learn the corresponding points from natural unannotated images. The training set is recursively expanded and the model parameters updated after maching each image. The semirandom set of nodes in the first image is matched in the second image, by sampling with particle filters the unnormalized posterior distribution, being the product of t...

Aggregate edge detection is the basis of creating concrete mesoscale model, which is applied to analyze concrete mesoscale characteristics. A concrete digital image edge detection method using DIS operator is presented in this paper. Mean filter, multi-scale filter, and Gaussian filter are compared on the effect of concrete image noise reduction. Based on the result, Gaussian filter is the most...

Gaussian pulse shaping filters plays an important role in digital communications due to its intersymbol interference free property. The pulse shaping filter is a useful means to shape the signal spectrum and avoid Interferences. In this paper a Gaussian filter has been presented for pulse shaping in wireless communication systems. The proposed filter has been designed and simulated using Matlab...

State estimation in high dimensional systems remains a challenging part of real time analysis. The ensemble Kalman filter addresses this challenge by using Gaussian approximations constructed from a number of samples. This method has been a large success in many applications. Unfortunately, for some cases, Gaussian approximations are no longer valid and the filter does not work so well. In this...