Showing papers on "Root-raised-cosine filter published in 2016"

Fast Kalman-Like Optimal Unbiased FIR Filtering With Applications

Abstract: In this paper, an optimal unbiased finite impulse response (OUFIR) filter is proposed as a linking solution between the unbiased FIR (UFIR) filter and the Kalman filter (KF). We first derive the batch OUFIR estimator to minimize the mean square error (MSE) subject to the unbiasedness constraint and then find its fast iterative form. It is shown that the OUFIR filter is full horizon (FH) and that its estimate converges to the KF estimate by increasing the horizon length $N$ . As a special feature, we note that the FH OUFIR filter operates almost as fast as the KF. Several other critical properties of the OUFIR filter are illustrated based on simulations and practical applications. Similar to the UFIR filter, and contrary to the KF, the OUFIR filter is highly insensitive to the initial conditions. It has much better robustness than KF against temporary model uncertainties. Finally, the OUFIR filter allows for ignoring system noise, which is typically not well known to the engineer.

Application of adaptive Savitzky–Golay filter for EEG signal processing☆

Abstract: Summary A Savitzky–Golay filter typically requires pre-determined values of order and frame size for its fabrication. Generally, a random hit-and-trial method or prior experience is required to determine the suitable values of design parameters. However, the proposed adaptive Savitzky–Golay filter aims to provide a generic framework for optimal design of filter vis-a-vis the order and frame size of the filter. The algorithm uses all the possible combinations of these parameters in a certain range and the correlation coefficient is evaluated in each case to measure the filter efficiency. The parameters which provide the highest correlation coefficient are considered for filter design. In this paper the relative advantages of adaptive Savitzky–Golay filter over the standard models are also discussed. The proposed adaptive model of Savitzky–Golay filter is successfully tested for EEG signal processing.

Adaptive Kuwahara filter

Abstract: A new filter was created by improving the standard Kuwahara filter. It allows more efficient noise reduction without blurring the edges and image preparation for segmentation and further analyses operations. One of the biggest and most common restrictions encountered in filter algorithms is the need for a declarative definition of the filter window size or the number of iterations that an operation should be repeated. In the case of the proposed solution, we are dealing with automatic adaptation of the algorithm to the local environment of each pixel in the processed image.

Performance Evaluation and Implementation of FPGA Based SGSF in Smart Diagnostic Applications

Abstract: The main objective of the paper is to implement Savitzky Golay Smoothing Filter (SGSF) so as to apply in pre-processing of real time smart medical diagnostic systems. As very important information of EEG and ECG waveforms lies in the peak of the signal, hence it becomes absolutely necessary to filter noise and artifacts from the signal. The implemented filter should be able to reject the noise efficiently along with the least distortion from the original signal. The shape preserving characteristics of the filter are determined by introducing different noise levels in the signal. The designed filter is tested on synthetic signals of EEG and ECG by adding different types of noise and the performance is analysed on various parameters, i.e., SNR, SSNR, SNRI, MSE, COR and signal distortion of the final output. The smoothing performance comparison of SGSF with the most commonly used Moving Average Filter (MAF) proves that SGSF is more efficient. Hence it is suggested that MAF can be replaced by SGSF. For real time issues, it is further implemented on reconfigurable architectures so as to achieve high speed, low cost, low power consumption and less area. Therefore SGSF is realized on FPGA platform to combine the advantages of both. Real time EEG and ECG signals are also considered for experimentation. The experimental results show that the proposed methodology (FPGA-SGSF) significantly reduces the processing time and preserves the actual features of the signal.

A Filtered OFDM Using FIR Filter Based on Window Function Method

Abstract: Orthogonal Frequency Division Multiplexing (OFDM) is designed to combat the effect of multipath reception, by dividing the wide band frequency selective fading channel into many narrow flat sub- channels, which improves the spectral efficiency and significantly mitigates the intersymbol interference (ISI). However, OFDM can not meet the demand for 5G heterogeneous service scenarios since it has a high out-of-band emission and a large peak-to-average power ratio (PAPR), and it only supports one kind of waveform parameter in the whole bandwidth. The Filtered-OFDM (F-OFDM) is proposed as a candidate technique for 5G high-data rate wireless communication system. This paper proposes a finite impulse response (FIR) digital filter based on window function method to achieve the F-OFDM, and discusses the performance of different window functions implemented in the F-OFDM. Simulation results show that the proposed F-OFDM is easy to be implemented and has a very low out-of-band emission with the same bit error rate (BER) performance compared to the conventional OFDM.

Robust Design of Radar Doppler Filters

Abstract: This paper considers the design of robust filters for radar pulse-Doppler processing when the interference is a wide sense stationary random process. The figure of merit which is optimized is the signal-to-interference-plus-noise ratio (SINR) at the filter output under a multitude of constraints accounting for Doppler filter sidelobes as well as uncertainties both in the received useful signal component and interference covariance matrix. The design is analytically formulated as a constrained optimization problem whose solvability is thoroughly studied. Precisely, a polynomial-time solution technique to get the optimal filter is proposed exploiting the representation of non-negative trigonometric polynomials via linear matrix inequalities, the spectral factorization theorem, and the duality theory. Last but not least, a detailed analysis of the optimum filter performance is provided showing the tradeoffs involved in the design and the gain achievable over some already known counterparts.

Dispersion Compensation FIR Filter With Improved Robustness to Coefficient Quantization Errors

Abstract: In this paper, we propose a new finite impulse response (FIR) filter for chromatic dispersion compensation which is given in closed form. We identify a relation between the out-of-band gain and the in-band error when the filter is implemented with finite-precision arithmetic. In particular, a large out-of-band gain makes the filter more sensitive to coefficient quantization errors due to finite precision digital signal processing. To improve robustness to coefficient quantization errors, our proposed filter is designed based on confining the out-of-band gain. By means of simulations, we show that our filter outperforms other existing FIR filters. The performance gain improves with increasing modulation order and decreasing number of bits used to represent the filter taps.

All-Digital Transmitter With a Mixed-Domain Combination Filter

Abstract: In this brief, we present a new reconfigurable filtering technique for all-digital transmitters using signal interference and cancellation. The proposed method allows the change of some filtering characteristics digitally. This technique is used to reduce the quantization noise from the all-digital transmitter's output, reducing the analog output filter requirements. Without a narrow band filter, a greater degree of flexibility for the transmitter's carrier frequency and bandwidth is attained, improving its suitability for multiband and multistandard operation. The results show the quantization noise reduction without an output analog filter, contributing to an increase of the transmitter's coding efficiency up to more than 95%.

Method and apparatus for canceling self-interference

Abstract: There are provided a method and an apparatus for performing a self-interference cancellation by determining a filter coefficient of an analog filter operated in an analog domain; and canceling self-interference generated in a received signal received by the node by a transmitted signal transmitted from the node, based on the filter coefficient.

Introducing new algorithms for realising an FIR filter with less hardware in order to eliminate power line interference from the ECG signal

Abstract: Power line interference (PLI) corrupts biomedical recordings. A notch filter is one of the filters that has been suggested to suppress the fundamental PLI and its harmonics in electrocardiographic recordings. Using finite impulse response (FIR) filters are one of the interesting ways to filter this interference in order to receive a rather pure signal. The frequent use of FIR filters create a field which studies realising an FIR filter and its various methods, which directly deals with the amount of hardware of the filter. In this study, the authors introduce new algorithms to realise FIR filters using less amount of hardware. By authors' definition, the filter is a black box and the authors focus on the quality of the output signal rather than focusing on the quality of frequency response of the filter. The simulation results show that the proposed algorithms remarkably reduces the hardware complexity of the FIR filter for eliminating PLI from the electrocardiogram (ECG) signal.

Optimization of Flexible Filter Banks Based on Fast Convolution

Abstract: Multirate filter banks can be implemented efficiently using fast-convolution (FC) processing. The main advantage of the FC filter banks (FC-FB) compared with the conventional polyphase implementations is their increased flexibility, that is, the number of channels, their bandwidths, and the center frequencies can be independently selected. In this paper, an approach to optimize the FC-FBs is proposed. First, a subband representation of the FC-FB is derived. Then, the optimization problems are formulated with the aid of the subband model. Finally, these problems are conveniently solved with the aid of a general nonlinear optimization algorithm. Several examples are included to demonstrate the proposed overall design scheme as well as to illustrate the efficiency and the flexibility of the resulting FC-FB.

An optimal selection method for morphological filter’s parameters and its application in bearing fault diagnosis

Abstract: The Mathematical morphological filter (MMF) is widely applied in vibration signal processing for fault diagnosis. The Structure element (SE) and the cutoff frequency of filter have important impacts on the filtering effect, but there is no selection principle of these parameters for vibration signal processing in fault diagnosis. In this paper, the working mechanism of the MMF is studied, and a novel technique with filter characteristics and selection criterion of the MMF is proposed. The filter characteristics of morphological filter are described through frequency response analysis. The relationship between the SE length and the cutoff frequency of MMF is put forward, and the quantitative selection method of SE in engineering is proposed to effectively remove the noise and detect the impulses. The method is evaluated using both simulated signal and experimental bearing vibration signal. The results show that quantized selection method can make MMF have the better filtering effect, and can reliably extract impulsive features for bearing defect diagnosis. The study provides a theoretical basis for the application of MMF in vibration signal processing.

Modified decision based median filter for impulse noise removal

Abstract: Removing impulse noise in digital images is one of the major challenges in digital image processing. Pixels in digital images get corrupted during transmission due to impulse noise. In this paper, we propose a modified decision based median filter that removes impulse noise from gray images. For noise removal from digital images, different types of median filters are used: Standard Median Filter (MF), Weighted Median Filter (WMF), Adaptive Median Filter (AMF) and Decision Based Median Filter (DBMF). In most of these methods except DBMF, processing pixels, irrespective of the fact whether it is corrupted or not, are replaced by the median value of the pixels in their nearby region without considering the local features present for example edges. However, our proposed method follows DBMF that considers only the noisy pixels and replaces the pixel value with median value of the pixels present in the processing window. In our method, we increase the window size as per the requirement. Our experimental results show that our proposed method performs better than Standard Median Filter (MF), Weighted Median Filter (WMF), Adaptive Median Filter (AMF) and Decision Based Median Filter (DBMF), especially when the noise intensity level is high. We compare our method to others based on Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values.

Recursive Implementation of the Gaussian Filter Using Truncated Cosine Functions

Abstract: This paper proposes a new recursive implementation of the Gaussian filter. Previous recursive implementations of the Gaussian filter suffer from one or several drawbacks. Some methods require employment of both causal and anti-causal filters. Implementing such filters requires both forward and backward recursions, and thus, significant amounts of memory, in the case of long sequences, to store and combine both recursion results. Moreover, most existing techniques do not ensure that the sum of impulse response weights equals unity, or that the standard deviation of the filter is exactly the desired one. The proposed technique is based on a recursive implementation of cosine-based finite impulse response filters and only requires forward recursions. The sum of filter weights and the standard deviation of the filter are constrained to the desired values during the design process. Experimental results confirm that the proposed filter provides a more suitable tradeoff between computational efficiency and filtering accuracy compared to existing recursive implementations. The performance and advantages of the proposed filter are demonstrated on signal and image processing examples.

Unified Maximum Likelihood Form for Bias Constrained FIR Filters

Abstract: In this letter, the maximum likelihood (ML) finite-impulse response (FIR) filter is proposed for discrete time-variant state-space models with nonsingular system matrix. The ML FIR filter has the deadbeat property and its form is universal for all known bias constrained FIR filters. By the identity weighting matrix, the ML FIR filter becomes the unbiased FIR filter, which ignores the noise statistics and the initial error statistics. Otherwise, the ML FIR filter is equivalent to the optimal FIR filter with embedded unbiasedness and to the minimum variance unbiased FIR filter. An example of a stochastic resonator demonstrates higher immunity of the ML FIR filter against errors in the imprecisely defined noise statistics than in the kalman filter.

The effect of average filter for complementary filter and Kalman filter based on measurement angle

Abstract: The objective of this research project is to construct an Inertial Measurement Units (IMU), which will design filter for users use equation on average filter complementary filter and Kalman filter and use algorithm on MCU. The sensor design, we use low-cost sensor on gyroscope and accelerometer, to measurement angle of system. The filter structures of the system, based on complementary filter and Kalman filter and use series average filter for measurement. This project will get data form experimental results to develop on simulation to illustrate the effectiveness of each filter scheme. Simulation study of average filter on different parameter to using on complementary filter and Kalman filter.

ECG denoising using weiner filter and adaptive least mean square algorithm

Abstract: Electrocardiogram (ECG) is needed for health issues related to heart disease. But sometimes due to mismatches in electrodes signal becomes noisy hence, removal of these interference like noise artifacts, baseline wandering and power line interference different filter approaches has been proposed. Various filter approaches are available for removal of noise artifacts from Electrocardiogram (ECG) signal. Filtering methods like Wiener filter and Adaptive Least Mean Square (LMS) algorithm are utilized for denoisingnoise interference from Electrocardiogram (ECG) signal. The main goal is to implement different filters and to compare based on performance parameters of the respective filter like Signal to Noise Ratio (SNR) and power spectral density (PSD). Testing was implemented on artificially noisy Electrocardiogram (ECG) signal which has taken from standard Physio.net database sampled at 50 Hz. For better utilization testing results are compared in term of their performance parameter such as SNR and PSD.

A new strategy of image denoising using multiplier-less FIR filter designed with the aid of differential evolution algorithm

Abstract: Due to the rapid development of one-dimensional signal processing in last few decades, it has spread out its wings in the field of multi-dimensional signal processing too This has mainly been dominated by the proposition and implementation of robust algorithms which have focused on efficient storage and reliable transmission of digital images of various kinds During the transmission through wired or wireless medium, digital images often encountered different types of channel noise which can significantly distort its appearance As a matter of fact, filtering operation of digital images forms one of the most important tasks to be performed at the receiving end In this paper, we have proposed a novel design strategy of two-dimensional (2-D) low-pass filter by means of a powerful evolutionary optimization technique called Differential Evolution (DE) algorithm Mask coefficients of the proposed filter are constrained to assume values as sum of powers-of-two, thus making the filter hardware friendly Experimental results have demonstrated the power of the algorithm in reducing the effect of Gaussian noise from digital image in terms of various performance parameters like peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), image enhancement factor (IEF) and image quality index (IQI) and so on A number of test images have been taken into our consideration for the purpose of establishing our proposition Simulation results have confirmed the superiority of the proposed DE-based filter over the conventional low-pass filtering method

High-Accuracy FIR Filter Design Using Stochastic Computing

Abstract: Finite impulse response (FIR) filter is the basic functional component in various signal processing and communication systems. In many practical applications that have stringent requirement on spectrum, long FIR filters are needed to achieve the desired filtering performance. However, because a T-tap FIR filter requires T copies of high-complexity multiplier, the conventional design of long FIR filter consumes a large amount of silicon area and power dissipation. This paper, for the first time, proposes a high-accuracy stochastic computing (SC)-based FIR filter design. By utilizing the simplicity of stochastic arithmetic unit, the proposed stochastic FIR filter achieves significant reduction in hardware complexity as compared to the conventional design. More importantly, this paper proposes a new high-accuracy non-scaled stochastic adder that has significant increase in computation accuracy than the conventional stochastic adder. Built on this new stochastic adder, the proposed stochastic FIR filter achieves much higher accuracy than the existing stochastic FIR filter design, especially for large T cases, thereby unlocking the potentiality for the widespread applications of stochastic FIR filters in practical signal processing systems.

Localised rank-ordered differences vector filter for suppression of high-density impulse noise in colour images

Abstract: This research presents a complete study of a new alternating vector filter for the removal of impulsive noise in colour images. The method is based on an impulsive noise detector for greyscale images that has been adapted in a localised manner using geometric information for processing colour images. Based on this statistic, a filtering scheme alternating between the identity and a non-linear vector filter is proposed. A geometric and experimental study was performed to obtain the optimal filter design. Experimental studies show that the proposed technique is simple, easy to implement, robust to noise, and outperforms the classic vector filters, as well as more recent filters.

Filter optimization of low out-of-subband emission for universal-filtered multicarrier systems

Abstract: In this paper, a filter optimization method for suppressing out-of-subband (OOSB) emission in universal-filtered multicarrier (UFMC) systems is proposed. UFMC is an attractive waveform candidate for 5G mobile communication systems because it has both the advantages of OFDM and filter bank multicarrier (FBMC). Typically, UFMC employs transmit filtering on a subband of a particular set of subcarriers so that the OOSB emission can be suppressed. However, the variation of frequency response in the subband caused by filtering could be harmful to the performance of data reception at the receiver. Therefore, this paper presents a filter optimization method that suppresses the OOSB emission with constraints on the subband frequency response. With these constraints the filter design problem can be formulated as an optimization problem with nonconvex constraints. By an appropriate transformation, this problem is relaxed as a linear-constrained convex optimization problem, which can be solved efficiently by using well-known interior-point methods. Simulation results demonstrate that the filter designed by the proposed method has an advantage in bit error probability compared to the filter used in the existing UFMC literature.

Electronically tunable fractional-order low-pass filter with current followers

Abstract: Proposal of a fractional (1+α)-order low-pass filter is presented in this paper. The proposed filter operates in the current-mode and it is designed using multi-output current followers (MO-CFs) and adjustable current amplifiers (ACAs). The filter possesses ability to electronically control its order and also the pole frequency. Verification of actual function of the filter is supported by the simulation results. The simulations were performed using Pspice simulator. The simulation results for three different values of its order and also three different values of pole frequency are illustrated and compared in this contribution.