Human life is always affected by various natural events such as earthquakes, volcanoes, subsidence, etc. One of the suitable tools for investigating and analyzing these hazards is synthetic aperture radar interferometry. This geodetic technique has the capability of resolving the displacement of the Earth's crust and analyzing the deformation through phase differences of radar images. The main advantage of the InSAR is the high temporal and spatial resolution. Analogous to other geodetic methods, the accuracy of the result depends on the modeling of observational disturbances and NOISEs. Despite progress in recent decades, these disorders have received little attention. The case study is northwest of Hawaii Island. In this study, FILTERING and reducing the turbulence in time series is based on the most appropriate functional model and stochastic model. This process is done using the MLE test. In this study, functional models include trend, cyclic, and offset. Statistical models also include white NOISE, flicker, and random walk, whose components are identified through univariate least squares NOISE analysis. Time series are reproduced through the best functional and statistical models. The results indicate that the best model is the linear trend with the presence of cyclic and offset, and white NOISE for all pixels. By implementing the univariate least squares NOISE analysis method, the accuracy of the results improved on average by 43%. In addition, applying both high-pass and low-pass filters resulted in an average improvement of 28%.

Seismic imaging is highly dependent on the quality of seismic data. Structural and stratigraphic interpretation of seismic sections that contain the least amount of NOISE is much easier. Reflection seismic data are often associated with NOISE. Coherent NOISE is a major category of NOISE that accompanies seismic data, and has the same trend in different seismic traces of the data. Ground roll is one of the main coherent NOISEs that has a low frequency, high amplitude and low velocity. Various methods, such as frequency filters and frequency-wavenumber filter, have been used for ground-roll attenuation. Different advantages and disadvantages are mentioned for each of the methods. In this paper, we have used time – frequency transform and variational mode decomposition to attenuate the ground-roll.

This paper describes the application of three straightforward image-based FILTERING methods to remove the geological NOISE effect which masks unexploded ordnances (UXOs) magnetic signals in geophysical surveys. Three image filters comprising of mean, median and Wiener are used to enhance the location of probable UXOs when they are embedded in a dominant background geological NOISE. The study area consists of three buried UXOs while a geological dyke structure covers the magnetic anomaly of the desired objects. To provide a better representation of the actual locations of UXOs in the observed magnetic anomaly over this area, all image-based filters could appropriately separate the geological dyke effect from the UXOs. These image filters can be good candidates to remove the geological NOISE effect in UXO detection when encountering a mixed response of multi-source magnetic anomaly in contaminated territories with UXOs. An analytic signal map of the separated magnetic anomaly of UXOs was provided to enhance locations of the UXOs in the studied field. Also, a combination of the analytic signal and the Euler deconvolution methods were used to estimate the depth of three buried UXO targets in the study area indicating a high sensitivity of the estimated parameter to the NOISE level.

Introduction: Electrocardiogram (ECG) is a method to measure the electrical activity of the heart which is performed by placing electrodes on the surface of the body. Physicians use observation tools to detect and diagnose heart diseases, the same is performed on ECG signals by cardiologists. In particular, heart diseases are recognized by examining the graphic representation of heart signals which is known as ECG. The ECG signals are accompanied by NOISE due to external sources or other physiological processes in the human body. Method: In this applied research, an adaptive filter based on wavelet transform and deep neural network was proposed to reduce the NOISE. The proposed method was a combination of wavelet transform, adaptive learning, and nonlinear mapping of deep neural networks. Deep neural network was used with an adaptive filter to reduce more NOISE in the ECG signal. Results: Signal-to-NOISE ratio (SNR) was used as a criterion to evaluate the quality of the proposed method to remove NOISE. In fact, the objective of this research was to increase this ratio which indicates higher efficiency of the method based on wavelet transform and deep learning. Conclusion: The results of the simulation showed that the proposed method improved the removal of NOISE from the ECG signal about 9. 56% compared to existing methods. The reason is that the coefficients extracted from adaptive filter were optimized using deep neural network so that it provided a low-NOISE waveform.

This work presents a Dual-mode VCO with NOISE FILTERING. By using dual-mode resonators, an oscillator is designed for two different frequency bands. Also, a properly designed NOISE filter in the tail current source reduces phase NOISE of LC-VCO. The dual-mode resonator circuit can be changed by adjusting the frequency range with capacitor bank and varactor in both frequency mode. Also, phase NOISE is improved in both frequency mode by current source filter. The VCO has been simulated in a standard 0. 18µ m CMOS process; it covers tuning range between 6. 27 GHz-8. 08 GHz and 10. 65 GHz-13. 02 GHz. The simulated VCO achieves a phase NOISE of-120 dBc/Hz from the 6. 83GHz carrier, and-113. 8 dBc/Hz from the 10. 61 GHz carrier at 1-MHz offset, while the power consumption is 14. 15 mW from a 1. 5V supply. The calculated figure of merit of this VCO is between-170 dBc/Hz and-184. 31 dBc/Hz.

Recently, many studies have examined filters for reducing or removing speckle NOISE, which is inherent to different images types such as Porous Silicon (PS) images, in order to ameliorate the metrological evaluation of their applications. In the case of digital images, NOISE can produce difficulties in the diagnosis of images details, such as edges and limits, should be preserved. Most algorithms can reduce or remove speckle NOISE, but they do not consider the conservation of these details. This paper describes in detail, the different techniques that focus mainly on the smoothing or elimination of speckle NOISE in images, as the aim of this study is to achieve the improvement of this smoothing and elimination, which is directly related to different processes (such as the detection of interest regions). Furthermore, the description of these techniques facilitates the operations of evaluations and research with a more specific scope. This study initially covers the definition and modeling of speckle NOISE. Then we elaborated in detail the different types of filters used in this study, finally, five statistical parameters such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Structural Similarity Index (SSIM), Peak Signal to NOISE Ratio (PSNR), Signal to NOISE Ratio (SNR) are calculated, compared and the results are tabulated, common in filter evaluation processes. Trough the calculation of the statistical parameters, we can classify the filters in terms of perceptual quality by providing greater certainty.