Digital video stabilization (DVS) allows acquiring video sequences without disturbing jerkiness, removing unwanted camera movements. A good DVS should remove the unwanted camera movements while maintains the intentional camera movements. In this article, we propose a novel DVS algorithm that compensates the camera jitters applying an Adaptive fuzzy filter on the global motion of video frames. The Adaptive fuzzy filter is a Kalman filter which is tuned by a fuzzy system Adaptively to the camera motion characteristics. The fuzzy system is also tuned during operation according to the amount of camera jitters. The fuzzy system uses two inputs which are quantitative representations of the unwanted and the intentional camera movements. Since motion estimation is a computation intensive operation, the global motion of video frames is estimated based on the block motion vectors which resulted by video encoder during motion estimation operation. Furthermore, the proposed method also utilizes an Adaptive criterion for Filtering and validation of motion vectors. Experimental results indicate a good performance for the proposed algorithm.

Magnetic resonance imaging (MRI) is one of the most powerful techniques to study the internal structure of the body. MRI image quality is affected by various noises. Noises in MRI are usually thermal and mainly due to the motion of charged particles in the coil. Noise in MRI images also cause a limitation in the study of visual images as well as computer analysis of the images. In this paper, first, it is proved that probability density function (PDF) of MRI images is rician because of the process of image capturing and MRI hardware. Based on the review of later works in this area, it is determined that rician denoising in wavelet domain is better. Then, it is concluded that the remaining noise in the final output of the conventional methods in wavelet domain, is Gaussian and can be greatly reduced with a Gaussian Adaptive filter. Based on this estimation, a Gaussian filter designed and the output image was filtered again. The results showed that the final image quality will improve considerably. As a conclusion, in similar situations, our proposed algorithm is always better than the others.

In this paper we present a general formalism for the establishment of the family of selective partial update affine projection algorithms (SPU-APA). The SPU-APA, the SPU regularized APA (SPU-R-APA), the SPU partial rank algorithm (SPU-PRA), the SPU binormalized data reusing least mean squares (SPU-BNDR-LMS), and the SPU normalized LMS with orthogonal correction factors (SPU-NLMS-OCF) algorithms are established by this general formalism. In these algorithms, the filter coefficients are partially updated rather than the entire filter coefficients at every iteration which is computationally efficient. Following this, the transient and steady-state performance analysis of this family of Adaptive filter algorithms are studied. This analysis is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. We demonstrate the performance of the presented algorithms through simulations in system identification and acoustic echo cancellation scenarios. The good agreement between theoretically predicted and actually observed performances is also demonstrated.

The civilian GPS signals are unencrypted, predictable and low power ones. Therefore, they are vulnerable to destroyer interfaces such as spoofing. In this paper, in order to reduce spoofing effect and achieve time series without interface, an Adaptive filter with finite impulse response based on Least Mean Square (LMS) algorithm has been used for the first time. Contrary to the previous methods, proposed approach needs no extra hardware and structural change in GPS receiver. Input signal of utilized Adaptive filter is pseudo-range data in navigation section. The principle of using Adaptive filter to eliminate interference is obtaining an estimate of interfering signal and subtracting that from the corrupted signal. Therefore, what remains at final output is the authentic signal. In this paper, root mean square (RMS) criteria is used to validate the proposed method. Simulation results show that the proposed approach can neutralize laboratory interface effects average up to 95 percent and measurement spoofing effect in average of 81 percent.

Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by Adaptively updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

In this paper we show how the classical and modern Adaptive filter algorithms can be introduced in a unified way. The Max normalized least mean squares (MAX-NLMS), N-Max NLMS, the family of SPU-NLMS, SPU transform domain Adaptive filter (SPU-TDAF), and SPU subband Adaptive filter (SPU-SAF) are particular algorithms are established in a unified way. Following this, the concept of set-membership (SM) Adaptive Filtering is extended to this framework, and a unified approach to derivation of SM and SM-SPU Adaptive filters is presented. The SM-NLMS, SM-TDAF, SM-SAF, SM-SPU-NLMS, and SM-SPUSAF are presented based on this approach. Also, this concept is extended to the SPU affine projection (SPU-AP) and SPUTDAF algorithms and two new algorithms which are called SM-SPU-AP and SM-SPU-TDAF algorithms, are established. These novel algorithms are computationally efficient. The good performance of the presented algorithms is demonstrated in system identification application.