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.

The least mean square (LMS) ADAPTIVE algorithm is widely used in acoustic NOISE cancellation (ANC) scenario. In this scenario, speech signals usually have high amplitude and sudden variations that are modeled by impulsive disturbances and it is well known that the acoustic channels usually have been sparse impulse response. Impulsive NOISE and sparsity of the acoustic channel are two important challenges in the ANC scenario that have paid special attention, recently. This paper presents a novel ADAPTIVE NOISE cancellation algorithm, to address the poor performance of the LMS algorithm in presence of impulsive NOISE along with a sparse impulse response. In order to eliminate impulsive NOISE from speech signal, the information theoretic criterion is used in the proposed cost function and the zero norm is also employed to deal with the sparsity feature of the acoustic channel impulse response. Simulation results indicates the superiority of the proposed algorithm in presence of impulsive NOISE along with sparsity condition of acoustic channel.

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.

A novel method for linear dynamic data reconciliation problem is proposed. The method integrates recursive least square identifier and discrete Kalman filter state estimator. The model used for the method is a black box, linear, discrete, state space, and MIMO model. The structure of the model is fixed but its parameters are estimated with online noisy input-output data of plant using identifier. The identified model parameters along with plant input-output data are used for regeneration of free NOISE states and outputs. Simulink (toolbox of MATLAB) is used for implementation and simulation of this method. Plant data is entered from workspace of MATLAB. The data is artificially contaminated with white NOISE in Simulink.The method is tested using input-output data of Tennessee-Eastman (TE) simulator. After simulation and comparison of true process data with reconciled process data, it can be shown that the results of this simulation are satisfactory.

Digital image is often degraded by many kinds of NOISE during the process of acquisition and transmission. To make subsequent processing more convenient, it is necessary to decrease the effect of NOISE. There are many kinds of NOISEs in image, which mainly include salt and pepper NOISE and Gaussian NOISE. This paper focuses on median filters to remove the salt and pepper NOISE. After summarizing the main disadvantages of the conventional median filters, this paper proposes a new kind of median filter algorithm based on the detection of impulse NOISE points. The performance of the proposed algorithm is compared with the conventional standard median filter (SMF), extremum median filter (EMF), and ADAPTIVE median filter (AMF). Experimental results under various NOISE intensities show that the proposed method has better denoising performance and detail preservation compared with the other denoising methods.