Narrow Results

Adaptive filters have wide range of applications in areas such as echo or interference cancellation, prediction and system identification. Due to high computational complexity of adaptive filters, their hardware implementation is not an easy task. However, it becomes essential in many cases where real-time execution is needed. This paper presents the design and hardware implementation of a variable step size 40 order adaptive filter for de-noising acoustic signals. To ensure an area efficient implementation, a novel structure is being proposed. The proposed structure eliminates the requirement of extra registers for storage of delayed inputs thereby reducing the silicon area. The structure is compared with direct-form and transposed-form structures by adapting the filter coefficients using four different variants of the least means square (LMS) algorithm. Subsequently, the filters are implemented on three different field programmable gate arrays (FPGAs) viz. Spartan 6, Virtex 6 and Virtex 7 to find out the best device family that can be used to implement an Adaptive noise canceller (ANC) by comparing speed, power and area utilization. The synthesis results clearly reveal that ANC designed using the proposed structure has resulted in a reduction in silicon area without incurring any significant overhead in terms of power or delay.

The electrocardiogram (ECG) signal is widely used for diagnosis of heart disorders. However, ECG signal is a kind of weak signal to be interfered with heavy background interferences. Moreover, there are some overlaps between the interference frequency sub-bands and the ECG frequency sub-bands, so it is difficult to inhibit noise in the ECG signal. In this paper, the ECG signal in-band noise de-noising method based on empirical mode decomposition (EMD) is proposed. This method uses random permutation to process intrinsic mode functions (IMFs). It abstracts QRS complexes to separate them from noise so that the clean ECG signal is obtained. The method is validated through simulations on the MIT-BIH Arrhythmia Database and experiments on the measured test data. The results indicate that the proposed method can restrain noise, improve signal noise ratio (SNR) and reduce mean squared error (MSE) effectively.

Speckle noise in ultrasound images is a major hindrance for the automation of segmentation, detection, classification and measurements of region of interest, to assist clinician for diagnosing pathologies. Speckle noise occurs due to constructive and destructive interference of the echo signals reflected from the target and has a granular appearance. Various techniques have been devised for speckle reduction. Most of these techniques are based on adaptive filters, wavelet transform and anisotropic diffusion filters. In this paper, a new speckle reduction technique based on the trilateral filter and local statistics of the image has been developed. The local speckle content of the image influences the trilateral filtering. The trilateral filter is a robust edge preserving filter which considers the similarity of neighboring regions in terms of adjacency, intensity and edge details. Hence, the new method preserves the finer details of the ultrasound images in the process of filtering speckle noise. The proposed technique is validated using synthetic, simulated and real-time clinical ultrasound images. Comparison of the proposed technique with the existing speckle removal algorithms in terms of quality metrics such as MSE, PSNR, UQI, SSI, FoM has been made and best results are obtained for the proposed technique.

A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method is based on fractal-entropy and clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle and pulse waves), enabling targeted de-noising and preventing the loss of useful information in filtering. To implement the technique, four characteristic parameters — fractal box dimension (FBD), higuchi fractal dimension (HFD), fuzzy entropy (FuEn) and approximate entropy (ApEn) — are extracted from MT time-series. The fuzzy c-means (FCM) clustering technique is used to analyze the characteristic parameters and automatically distinguish signals with strong interference from the rest. The wavelet threshold (WT) de-noising method is used only to suppress the identified strong interference in selected signal sections. The technique is validated through signal samples with known interference, before being applied to a set of field measured MT/Audio Magnetotelluric (AMT) data. Compared with the conventional de-noising strategy that blindly applies the filter to the overall dataset, the proposed method can automatically identify and purposefully suppress the intermittent interference in the MT/AMT signal. The resulted apparent resistivity-phase curve is more continuous and smooth, and the slow-change trend in the low-frequency range is more precisely reserved. Moreover, the characteristic of the target-filtered MT/AMT signal is close to the essential characteristic of the natural field, and the result more accurately reflects the inherent electrical structure information of the measured site.

Traditional time-frequency methods for partial discharge (PD) de-noising have some limitations such as low time-frequency resolution, single de-noising type and poor readability. In this paper, a novel de-noising algorithm based on synchro-squeezed continuous wavelet transform (CWT) is adopted to filter out narrowband noise and white noise. The synchro-squeezed CWT algorithm is designed to redistribute the time-frequency domain and to distinguish the signal from the noise carefully as a high-rate time-frequency analysis. High-order statistics is employed to pre-process the polluted PD signal. The generalized cross-validation (GCV) threshold is combined with the adaptive trimmed threshold of synchro-squeezing (SS) domain to deal with the subsequent signals. The proposed algorithm can effectively suppress two kinds of noise, and the signal distortion is lower.

Wavelet Shrinkage using DWT has been widely used in de-noising although DWT has a translation variance problem. In this study, we solve this problem by using the translation invariant DWT. For this purpose, we propose a new complex wavelet, the Real-Imaginary Spline Wavelet (RI-Spline wavelet). We also propose the Coherent Dual-Tree algorithm for the RI-Spline wavelet and extend it to the 2-Dimensional. Then we apply this translation invariant RI-Spline wavelet for translation invariant de-noising. Experimental results show that our method, when applied to ECG data, the medical image and the textile surface inspection can obtain better de-noising results than that of conventional Wavelet Shrinkage.

This work gives an algorithm that makes up for the many iterations and the loss of some main features encountered in the usual methods of wavelet transform filtering. The wavelet transform technique is merged with the window low-pass method. The results are acceptable.

In this paper, an off-line double density discrete wavelet transform based de-noising and baseline wandering removal methods are proposed. Different levels decomposition is used depending upon the noise level, so as to give a better result. When the noise level is low, three levels decomposition is used. When the noise level is medium, four levels decomposition is used. When the noise level is high, five levels decomposition is used. Soft threshold technique is applied to each set of wavelet detail coefficients with different noise level. Donoho's estimator is used as a threshold for each set of wavelet detail coefficients. The results are compared with other classical filters and improvement of signal to noise ratio is discussed. Using the proposed method the output signal to noise ratio is 19.7628 dB for an input signal to noise ratio of -7.11 dB. This is much higher than other methods available in the literature. Baseline wandering removal is done by using double density discrete wavelet approximation coefficients of the whole signal. This is an unsupervised method allowing the process to be used in off-line automatic analysis of electrocardiogram. The results are more accurate than other methods with less effort.

An aporia of signal de-noising is that the local signal reconstruction at the singular points. Based on the analysis for the signal singular points, combining signal scaling and orthogonal transform, This paper present a novel method called Scale-Invariant V-Transform (SIVT) for signal de-noising based on V-System, which is polynomial multi-wavelets in invariant set. SIVT employs multiple redundant basis of various scale to suppress the artifacts appearing in the singular points of denoised signal. The test results reveal the SIVT reconstructions exhibit higher visual quality and numerical measurement of SNR than wavelet-based reconstructions. Existing theory of SIVT suggests that these new approaches can perform significantly better than wavelet methods in certain signal reconstruction problems.

This paper presents a new method for the segmentation of Magnetic Resonance Imaging (MRI) of brain tumor. First, discrete wavelet transform (DWT)-based soft-thresholding technique is used for removing noise in the MRI. Second, intensity inhomogeneity (IIH) independent of noise is removed from the MRI image. Third, again DWT is used to sharpen the de-noised and IIH corrected image. In this method, the image is decomposed into first level using wavelet decomposition and approximate values are assigned to zero and reconstruct the image results in detailed image. The detailed image is added with the pre-processed image to produce sharpened image. Entropy maximization using Grammatical Swarm (GS) algorithm is used to obtain a set of threshold values and a threshold value is selected with the expert knowledge to separate the lesion part from the other non-diseased cells in the image.

This study aims to investigate the dynamic correlations (co-movement) in between energy commodities such as WTI Crude Oil (WOIL), Brent Crude Oil (BOIL), Heating Oil and Electricity prices. To achieve this goal, we employed partial wavelet coherence (PWC) and multiple wavelet coherence (MWC). Wavelet analysis constitutes the core of these methodologies and MWC is essential to determine the dynamic correlation (co-movement) of time intervals and scales between the time series. We have developed a software program to compute PWC and MWC for quadruple data set. Coherent time intervals of the time series are determined. Vector ARMA models are shown to give a good fit due to having low mean squared errors compared to the univariate case. This allowed us to have better forecast performance.

When our proposed neurosurgical robot is applied, the neurosurgeon usually wants to sense the force on the remote site to operate on patients. The force signal analysis is of critical importance for neurosurgeons to perform stable, reliable, and safe operations. In this paper, based on the stationary wavelet transform (SWT), force information analysis and process is designed. Since force sampled by the JR3 sensor contains noise from the sensor and mechanical vibration when drilling, to smooth the force signal sent to the operator, SWT-based force information de-noising is proposed to reduce the noise significantly, especially for the force along the x and y axes. Simulations and experiments further verified the proposed research.

The aim of this paper is to show that the estimates made with vector autoregressive–moving-average (ARMA) models based on the coherent time intervals of the multiple time series give more precise results than the univariate case. The previous literature on dynamic correlations (co-movement) in between food and energy prices has mixed results and mainly based on parametric approaches. Therefore, partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) methods are used, respectively, to uncover the coherency simultaneously for time and frequency domains. In our study; world oil, corn, soybeans, wheat and sugar prices are examined instead of the return and volatility relationship between oil and agricultural commodities due to model-free approach of wavelet analysis.

In this paper, an improved method for de-noising bearing vibration signals to detect the bearing's faults is proposed. The method is based on discrete wavelet transforms, coefficients shrinkage methods and fast Fourier transforms. The frequency sub-bands for bearing fault conditions and the de-noised signal power spectrums are obtained to indicate better detecting results compared with conventional methods.

In this paper, emphasis is placed on de-noising via thresholding, which is based on wavelet transform. This paper also analyses the characteristic of signal and noise by wavelet transform, de-noising method by Donoho and other's improving scheme. According to the feature of noise and the continuity of signal, the improvement schemes are put forward on dealing with the preprocessing procedure to signal observation. These schemes are uncomplicated and practical.

Several 1-D windows could be constructed on the direction character of sub-image in the filter window after wavelet transform. Then the adaptive filters could be obtained from the 1-D windows. With the effective weighted combination of filters in different 1-D windows, the sub-image local adaptive filters are generated. And the simulation of the adaptive filter shows it could remove the white noise of image and preserve the edge of images effectively.

It is dissatisfactory to implement wavelet threshold to de-noise only with wavelet coefficient magnitudes. In this paper two novel methods of wavelet threshold de-noising based on locale variances are presented. They use the wavelet coefficient magnitudes as well as the local variances, which denote the clustering properties of wavelet coefficients preferably. Some simulation results, obtained from recovering a signal embedded in the additive white noise in different signal-to-noise (SNR) settings, show the potential and effectiveness of the proposed methods.

Skin color varies with human heart beat which can be recorded by camera even consumer-level webcam though it's uneasy to be observed by naked eyes because of human physical limitations. This phenomenon is applied to detection of human vital signal like heart and respiration rate recently. However, the video signals will inevitably be contaminated by noise and environmental interference. For subsequent process, denoising is very necessary. In this paper, a denoising method based on wavelet transform is proposed and suitable parameters are selected by research and experiments. The result shows it's effective and helpful to the next procedure.

In this paper, we propose an active hearing protection system for workers in an extreme noise industrial environment. For the active hearing protection, we utilize ANC (Active and speech enhancement technology. Active hearing protection provides face to face communication that voice is passed through and the noise can be blocked. For this object we propose a method of estimating a secondary path of ANC accordance with user's hearing protection wearing characteristics and a speech enhancement algorithm to improve speech intelligibility with the ANC and the De-Noised speech signal. An adaptive RMC (Residual Music Canceler) is proposed for enhancing the accuracy of the reference signal of the feedback ANC. We obtained results that the secondary path can be accurately estimated and high quality of music sound can be consistently obtained in a time-varying secondary path situation. Most of the ANC system use the LMS algorithm to design a secondary path filter. However the convergence time of the filter is prolonged in the conventional method because the initial coefficient values of the secondary path filter is set to 0. To solve this problem, in this paper we propose a method of shortening the convergence time of the filter by setting the coefficient values determined in advance as an initial value of the secondary pass filter. With the proposed method the error is decreased from the initial stage of the secondary path filter, whereby the convergence of the ANC system is increased. In the experiments by using a commercially available headset it was confirmed that the proposed method is faster than the conventional method for convergence of ANC system. By applying the ANC and the De-Noising system in real-time DSP (Digital Signal Processor), we have developed an active hearing protection.