Periodic noises are unwished and spurious signals that create repetitive pattern on images and decreased the visual quality. Firstly, this paper investigates various methods for reducing the effects of the periodic noise in digital images. Then an ADAPTIVE optimum NOTCH FILTER is proposed. In the proposed method, the regions of noise frequencies are determined by analyzing the spectral of noisy image. Then, the repetitive pattern of the periodic noise is produced by applying the corresponding NOTCH pass FILTER. Finally, an output image with reduced periodic noise is restored by an optimum NOTCH FILTER method. The results of the proposed ADAPTIVE optimum NOTCH FILTER are compared by the mean and the median FILTERing techniques in frequency domain. The results show that the proposed FILTER has higher performances, visually and statistically, and has lower computational cost. In spite of the other compared methods, the proposed FILTER does not need to tune any parameters.

The conventional feedback controller cannot perform well especially in presence of elastic behavior of flexible systems and variation in the character of disturbances, resulting in the reduction on the stability of the control system. This paper deals with designing a control strategy based on ‘ model reference ADAPTIVE approach ’ applied to appraise a single vibration mode of the system. This approach makes of a model reference ADAPTIVE lattice NOTCH FILTER which has been implemented on the system in the case of recursive form to the elimination of the unsatisfied vibrating frequency. The performances of the proposed control algorithms are evaluated by means of simulation on MATLAB and Simulink.

In this paper, different cases of the stability of Multiple ADAPTIVE NOTCH FILTER (MANF) are studied. The structure of MANF is composed of N parallel subFILTERs. Each subFILTER estimates the parameters of one the components of quasi periodic signals including the sum of N periodic signals. For this structure, there are three different cases of stability for N=K, N>K and N<K, which include the exponential stability in the isolated equilibrium point, the semistability and the ultimate boundedness in the presence of disturbance. Among these cases, the second and the third cases are analyzed more specifically in this paper and therefore in this paper, in addition to the presentation of MANF, a new approach is proposed to prove the semistability based on the Lyapunov function definition. Also, according to the fact that the estimated frequency of subFILTERsincludes a bias, a general form is obtained to determine the estimated frequency of subFILTERs in the case of the ultimate boundedness under disturbance. Additionally, in order to cancel this bias, a method is proposed based on the use of rectangular window functions in MANF. Simulations are carried out to demonstrate that using the rectangular window enhances the ANF performance.

Periodic noise damages the visual quality of images by imposingrepetitive patterns to them. In this research work, we introduce anew method which is based on fuzzy systems for de-noising periodic noise. The position of a frequency coefficient in the origin shifted Fouriertransformed image and the current coefficient's amplitude to a localmedian ratio are used as inputs of the fuzzy system. The output of the fuzzy system will be a restorationmask. Weimplemented the proposed method and evaluated its performance againstsome images corrupted with periodic noise. The experimental results showan acceptable level of performance. Overall, this research implies thatthe procedure conducted by experts in the NOTCH FILTER can be automatedby using a fuzzy system.

As nowadays the GPS navigation system has more usage in different areas, increasing its efficiency and accuracy has gained more importance. The transmitted signal travels a long distance from the satellites to reach the receivers on the ground, so its power fades. This faded signal can easily be affected by intentional noises, the so-called jamming, or unintentional noises. One of the most destructive kinds of jamming is the continuous wave (CW) jamming. The most favored method for countering this jamming is the NOTCH FILTER. Therefore, in this paper, an ADAPTIVE NOTCH FILTER (ANF) with a narrow response in proposed to reduce the effects of CW jamming. A kind of PSO evolutionary algorithm called the improved particle swarm optimization algorithm (IPSO) is used to adapt the FILTER’ s coefficients according to the power and frequency of the jamming signal. Evolutionary algorithms are used in problems without any straight forward answer, and that is why we chose this method for designing the FILTER. It also reduces the complexity of solving such mathematical problems. Finally, the efficiency of the proposed method is compared to other similar solutions, showing a significant improvement in the similarity of recovered signal to the original signal (up to 99%), as well as an increase in the number of observed satellites up to 6, and error reduction in determining the user coordinates which is the primary goal of the GPS system.

Particle FILTER is an effective tool for the object tracking problem. However, obtaining an accurate model for the system state and the observations is an essential requirement. Therefore, one of the areas of interest for the researchers is estimating the observation function according to the learning data. The observation function can be considered linear or nonlinear. The existing methods for estimating the observation function are faced some problems such as: 1) dependency to the initial value of parameters in expectation-maximization based methods and 2) requiring a set of predefined models for the multiple models based methods. In this paper, a new unsupervised method based on the kernel ADAPTIVE FILTERs is presented to overcome the above mentioned problems. To do so, least mean squares/ recursive least squares ADAPTIVE FILTERs are used to estimate the nonlinear observation function. Here, given the known process function and a sequence of observations, the unknown observation function is estimated. Moreover, to accelerate the algorithm and reduce the computational costs, a sparsification method based on approximate linear dependency is used. The proposed method is evaluated in two applications: time series forecasting and tracking objects in video. Results demonstrate the superiority of the proposed method compared with the existing algorithms.

A critical protection requirement for grid connected distributed generators (DG) is antiislanding protection. In this paper, a new islanding detection method for any possible network loading is proposed based on utilizing and combining various system parameter indices. In order to secure the detection of islanding, eight intentional disturbances are imposed to the system under study in which two sets of them simulate the islanding condition. The proposed technique uses the ADAPTIVE NOTCH FILTERs for extracting the frequency of oscillation of generator’s output waveform as one of the output parameter indices. An advantage of this technique is that it does not necessitate varying the islanding detection boundaries under various system loading conditions.

Two-dimensional (2D) ADAPTIVE FILTERing is a technique that can be applied to many image and signal processing applications. This paper extends the one-dimensional ADAPTIVE FILTER algorithms to 2D structure and the novel 2D ADAPTIVE FILTERs are established. Based on this extension, the 2D variable step-size normalized least mean squares (2D-VSSNLMS), the 2D-VSS affine projection algorithms (2D-VSS-APA), the 2D set-membership NLMS (2D-SM-NLMS), the 2D-SM-APA, the 2D selective partial update NLMS (2DSPU- NLMS), and the 2D-SPU-APA are presented. In 2D-VSS ADAPTIVE FILTERs, the stepsize changes during the adaptation which leads to improve the performance of the algorithms. In 2D-SM ADAPTIVE FILTER algorithms, the FILTER coefficients are not updated at each iteration. Therefore, the computational complexity is reduced. In 2D-SPU ADAPTIVE algorithms, the FILTER coefficients are partially updated which reduce the computational complexity. We demonstrate the good performance of the proposed algorithms thorough several simulation results in 2D ADAPTIVE noise cancellation (2D-ANC) for image denoising. The results are compared with the classical 2D ADAPTIVE FILTERs such as 2D-LMS, 2D-NLMS, and 2D-APA.

The probability hypothesis density (PHD) FILTER sequentially computes the first-order multi-target moment for the full multi-target probability density function and dramatically reduces the computational expense of tracking problem. In this paper، we propose an improved implementation of the PHD using the notion of auxiliary particle FILTER to enhance the effectiveness of the Sequential Monte Carlo (SMC) implementation of the PHD FILTER. The proposed method differs from traditional SMC implementations because it demonstrates an ability to simultaneously search in an effective way for persistent and newborn targets where the birth intensity is uniform and noninformative. Simulation results indicate that our novel method dramatically improves the accuracy of PHD approximation when compared to traditional SMC implementation methods for the same number of particles.