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This paper focuses on impulsive noise filtering and outliers rejection in gray-scale images. The proposed method combines neural networks, lower-upper-middle (LUM) smoothers and adaptive switching operations to produce a high-quality enhanced image. Extensive experimentation reported in this paper indicates that the proposed method is sufficiently robust, achieves an excellent balance between noise suppression and signal-detail preservation, and outperforms some well-known filters both subjectively and objectively.

In order to further improve fractional-pel interpolation image quality of video sequence with different resolutions and reduce algorithm complexity, the fractional-pel interpolation algorithm based on adaptive filter (AF_FIA) is proposed. This algorithm adaptively selects the interpolation filters with different orders according to the three video sequence regions with different resolutions; in the three video sequence regions with different resolutions, the high-order interpolation filter is replaced by low-order interpolation filter according to the correlation between pixels to realize the adaptive selection of filter. The complexity analysis results show that compared with other algorithms, this algorithm reduces space complexity and computation complexity, thus reducing the storage access and coding time. The simulation results indicate that compared with other algorithms, this algorithm has good coding performance and robustness for video sequences with different resolutions.

Pipeline leakages have plagued pipeline transportation for long time. Therefore, accurately extracting the features of leak signal in the presence of noise, and prompt identification of leak states and leak sizes is essential when leakage occurs. A novel leakage detection method based on the improved adaptive filter, whose parameters were optimized by the particle swarm optimization (PSO), was formulated and applied. The PSO-adaptive filter proved to be an effective signal processing method in contrast with variational mode decomposition (VMD). Its efficiency stems from the fact that the adaptive filter employs the noise collected from the detection environment. Therefore, the filter can adjust its parameters according to the changing situation. What is more, the application of PSO is conducive to automatically set suitable parameters for adaptive filter. After signal denoising, principal component analysis (PCA) was used for feature dimension reduction and selecting optimal features. The features after PCA proved to be more helpful in pattern recognition than the features without PCA. Furthermore, the relationship between the recognition results of leakage sizes and the measurement distance of the sensor was studied. Experimental results show that the method used in this paper can identify the leakage states with the accuracy of 100%. The identification result of leakage size reaches an accuracy of 86.75% under the influence of the measurement distance.

Frequency-dependent image interference is an inevitable impairment in wideband quadrature receivers. To suppress this interference, this paper presents a non-data-aided adaptive compensation algorithm, optimal block adaptive filtering algorithm based on circularity (OBA-C). This technique exploits the concept that the image interference in wireless systems causes the received complex signal to lose its nature of circularity. Then the OBA-C algorithm restores the circularity of the signal to compensate for the image interference. To avoid manually selecting a step size, the presented algorithm employs the complex Taylor series expansion to optimally update the adaptive filter coefficients. This technique fully exploits the degrees of freedom of the system, and generates an individual update for each filter coefficient at each iteration. Computer simulations are carried out to test the performance of the OBA-C for practical levels of image interference. The simulation results illustrate that the OBA-C achieves fast convergence and excellent image rejection performance. Other advantages of OBA-C are also analyzed, including the robustness against radio frequency impairments and different levels of image interference.

Acoustic feedback often limits the maximum usable gain of acoustic systems and degrades the overall system response. It is well known to be detrimental that the system stability and performance must be taken into account in system design. Most of the conventional methods for acoustic feedback cancellation in an acoustic system are based primarily on an adaptive filter with the least-mean-square (LMS) error algorithm. Unfortunately, convergence speed is often limited when a sound source or a filtering plant is varied, because the learning process of the adaptive algorithm fails to respond fast enough to changing operational conditions. This report proposes a variable step-size affine-projection algorithm (VSS APA) for acoustic feedback cancellation in audio systems. The proposed adaptive filter is based on the filtering affine-projection algorithm with variable step-size for improving convergence speed in acoustic feedback cancellation. A performance evaluation and simulation comparison has been conducted to compare the proposed algorithm and various traditional adaptive filtering algorithms.

Signals transmitted over long distances through underwater acoustic channels are prone to corruption due to wind interference, ambient noises and various other sources of disturbance. Adaptive filters can be used to extenuate the effect of ambient noise in acoustic signals. A competent technique to denoise acoustic signals using adaptive filters has been proposed. Adaptive filtering techniques such as least mean square (LMS), normalized least mean square (NLMS) and Kalman least mean square (KLMS) have been analyzed based on their performance, with the help of characteristics like signal-to-noise ratio (SNR) and mean square error (MSE) for various wind speeds. An exhaustive set of data, collected using a custom made fixture containing two hydrophones, from shallow water regions in Bay of Bengal, have been used to verify the efficacy of this method. Based on the results obtained by simulation and Lab window simulator, hardware has been designed to denoise the useful signal. The defective source signal is passed through a Kalman filter based denoising hardware system. This system performs necessary operations to denoise the defective source signal and the final turnout is made free from ambient noise. The denoised signal is then stored in an external device for future use.

The vibrational acoustic signals collected on pipelines contain substantial information concerning the working status of pipelines. They can be used for pinpointing leaks in buried pipelines. However, because of the complexity of signal composition and the heavy corruption of signals by ambient noises, it is essential to set up an appropriate signal model and scheme of analysis and synthesis in order to extract leak signatures and specify leak locations. In addition, the features of vibrational acoustic signals vary with materials, sizes and buried conditions of tubular pipes. It is difficult to pre-determine the knowledge of signals, such as the spectral knowledge. Here, an adaptive detection and estimation strategy is proposed based on LMS adaptive filtering and modified Wavelet denoising. The feasibility leak detection and location estimation is first automatically analysed by the signal processing procedure without any prerequisite on signals and then the procedure adaptively finds better estimate. With the proposed schemes, even where collected signals are heavily blurred by bursting interferences and present non-stationary, the instrument may also carry on detection and achieve effective results.

Global Positioning System (GPS) satellites signal processing to obtain all in view satellite measurements and to use them to find a solution and to do integrity monitoring forms a major component of the load on the receiver's processing element. If processing capability is limited there is restriction on the number of measurements which can be obtained and processed. Alternatively, the number of measurements can be restricted and the resulting saving in load on the processor can be used to offer more spare processing time which can be used for other user specific requirements. Thus if m visible satellites can provide measurements only n measurements can be used (n < m). The arrangement and the number of GPS satellites influence measurement accuracy. Dilution of Precision (DOP) is an index evaluating the arrangement of satellites. Geometric DOP (GDOP) is, in effect, the amplification factor of pseudo-range measurement errors into user errors due to the effect of satellite geometry. The GDOP approximation is an essential feature in determining the performance of a positioning system. In this paper, knowledge-based methods such as neural networks and evolutionary adaptive filters are presented for optimum approximation of GDOP. Without matrix inversion required, the knowledge-based approaches are capable of evaluating all subsets of satellites and hence reduce the computational burden. This would enable the use of a high-integrity navigation solution without the delay required for many matrix inversions. Models validity is verified with test data. The results are highly effective techniques for GDOP approximation.

Nowadays, digital images play an increasingly important role in helping to explain phenomena and to attract people’s attention through various types of media rather than the use of text. However, the quality of digital images may be degraded due to noise that has occurred either during their recording or their transmission via a network. Therefore, removal of image noise, which is known as “image denoising”, is one of the primary required tasks in digital image processing. Various methods in earlier studies have been developed and proposed to remove the noise found in images. For example, the use of metric filters to eliminate noise has received much attention from researchers in recent literature. However, the convergence speed when searching for the optimal filter coefficient of these proposed algorithms is quite low. Previous research in the past few years has found that biologically inspired approaches are among the more promising metaheuristic methods used to find optimal solutions. In this work, an image denoising approach based on the best-so-far (BSF) ABC algorithm combined with an adaptive filter is proposed to enhance the performance of searching for the optimal filter coefficient in the denoising process. Experimental results indicate that the denoising of images employing the proposed BSF ABC technique yields good quality and the ability to remove noise while preventing the features of the image from being lost in the denoising process. The denoised image quality obtained by the proposed method achieves a 20% increase compared with other recently developed techniques in the field of biologically inspired approaches.

The electrocardiogram (ECG) is generally used for the diagnosis of cardiovascular diseases. In many of the biomedical applications, it is necessary to remove the noise from ECG recordings. Several adaptive filter structures have been proposed for noise cancellation. Compared to the least mean square (LMS) method, the unbiased and normalized adaptive noise reduction (UNANR) algorithm has better performance, as mentioned in previous investigations. In this paper, we review various kinds of ECG noise reduction algorithms. To provide a detailed and fair comparison, all normalized LMS (NLMS), Block LMS (BLMS), recursive least squares (RLS) and UNANR algorithms are implemented and their performance have been assessed using the same dataset and compared to different state-of-the-art approaches. Then, the performance analysis of all five algorithms is presented and compared in term of mean squared error (MSE), computational complexity and stability. The obtained results revealed that RLS method is much more effective and powerful than other methods in ECG noise cancellation, and even better than UNANR. Then, in order to reach the best performance of the mentioned filter and also, to minimize the output signal error, the optimized parameters of the algorithm were defined and results were investigated. The obtained outcomes show that the best Lambda (λ) occurs between 0.05 and 0.9, so that the convergence rate of the optimized RLS filter is faster than others. It not only decreases the noise, but also the ECG waveform is better conserved. Furthermore, the introduced optimized method with adaptive threshold value would have great potential in biomedical application of signal processing and other fields.

The paper proposes a novel methodology of de-noising raw electroencephalogram (EEG) data from ocular artifacts (OAs) and alpha waves extraction from motor imagery-based signals that could be further utilized for brain–computer interface (BCI)-based applications. An algorithm based on discrete wavelet transform (DWT) and nonlinear adaptive filtering for the removal of OA is advocated, with an aim of making the process computationally intelligent. This algorithm has been tested on pre-recorded EEG dataset for BCI (Dataset IIIa; obtained from the website of the BCI Competition III). To further validate the competence of the proposed method, synthetic EEG signals were created, which were fused with white Gaussian noise. A total of 20 EEG signals were generated, half of which had added noise with a signal-to-noise ratio (SNR) of 10dB and other half had added noise of 5 dBSNR. Each signal contained 1000 samples with a sampling frequency of 250Hz. An optimum bandpass filter (FIR and IIR) for extraction of alpha waves has been suggested. FIR Equiripple filter is found most appropriate for the task as it has highest SNR and computes the response faster when compared with other filters. Among different mother wavelets, Daubechies 4 wavelet obtained using statistical thresholding denoises the EEG data most successfully. Correlation and root mean square error (RMSE) parameters show that the performance of nonlinear adaptive filter developed using nonlinear Volterra series has an edge over conventional adaptive filters for the intended purpose.

Image Processing are still being challenged by noises. Noise causes the intensity manipulation of the image. So, removing or reducing the noises from the image is a must before working with it. It is an active area of research because none of the established or proposed noise reducing methods can return back the original image. And also, there are different types of noises. Different proposed algorithms work fine with different types of noises and also up to a certain level of noises. In this paper, an adaptive noise removal algorithm is proposed which works fine with impulse noises and does not blur the edges of the inputted image. While removing the noises, the algorithm uses an adaptive mask which is n × n square or cross musk, n is usually an odd number. Our proposed algorithm has achieved 15.38 dB (Peak Signal to Noise Ratio) outperforming the existing filters.

Fingerprint enhancement determines the performance of automatic fingerprint recognition system, and it is necessary to repeatedly enhance the ridges due to the quality variety in a fingerprint image. This paper presents a novel adaptive enhancement algorithm by estimation of quality in local ridges. The algorithm can automatically adjust the parameters of filters and the time of filtering according to the quality factors in different regions. In order to improve filtering efficiency, a template bank of 4-dimenstion array is also designed to quantize the filter. Experimental results in eight low-quality images from FVC2004 data sets show that the proposed algorithm is higher 0.11 (or 23.7%) in Good Index (GI), and less 0.36625 second (or 54.06% time savings) in time consumptions than traditional Gabor-based methods. Since the eight images are extremely bad, a little improvement will be very meaningful. Furthermore, the variance of time consumption shows that the traditional method heavily relies on the quality of input fingerprint image and exposes the disadvantage of indistinctively repeated filtering. So the proposed method can avoid the defect of repeated filtering, protect minutiae, and improve clarity of ridges.

This paper mainly studies the interference suppression of Direct Sequence Spread Spectrum (DSSS) systems. The visual simulation tool Simulink provided by Matlab is used to build the transmitter and receiver modules of a DSSS communication system, and narrow-band interferences in different carrier frequencies and amplitudes are added to the AWNG channel. The relationship between the bit error rate (BER), signal-to-noise ratio (SNR) and spreading gain of the DSSS system is researched using every waveform and spectrum transformation diagram in the transmission process. When the narrow-band interference oversteps the tolerance of the DSSS system, we can use the adaptive filters such as LMS (Least Mean Square) filter and RLS (Recursive Least Square) filter to improve suppression of narrow-band interference. The simulation confirmed that the adaptive filter has a good effect on narrow-band interference suppression. The RLS Filter's algorithm is complex, so its simulation time is long. The LMS filter's convergence speed is slow.