A Diversity Controlled Genetic Algorithm for Optimization of FRM Digital Filters over DBNS Multiplier Coefficient Space

In a preceding paper, it was shown that the canonical signed-digit (CSD) representation of the multiplier coefficients in frequency response-masking (FRM) FIR digital filters leads to a substantial reduction in the hardware complexity of the FIR digital filter. However, a direct approximation of the infinite-precision multiplier coefficients to their CSD counterparts may cause the FIR digital filter to cease to satisfy the given filter design specifications. This paper presents a novel technique based on diversity controlled (DC) genetic algorithm (GA) for the discrete optimization of FRM FIR digital filters over the CSD multiplier coefficient space. The salient feature of the DCGA technique is that it permits external control over population diversity and parent selection pressure, giving rise to a rapid convergence to an optimal solution. It is shown that the application of the proposed DCGA technique to the optimization of a benchmark low-pass FRM digital filter over CSD multiplier coefficient space leads to an order of magnitude speed-up factor as compared to that associated with a conventional GA. Moreover, the optimized CSD FRM digital filter outperforms the corresponding infinite-precision digital filter obtained by the classical Parks-Mclellan approach.

Present paper deals with design and development of digital FIR filters. Heart diseases, which are one of the death reasons, are among the several serious problems in this century and as per the latest survey, 60% of the patients die due to Heart diseases. Electrocardiogram (ECG) signal plays a vital role in the monitoring and diagnosis of the health conditions of human heart. The basic ECG signal has the frequency range from .5Hz to 100Hz. The most significant noises that corrupt ECG signal are power line interference and baseline wanders. It becomes difficult for the Specialist to diagnose the diseases if artifacts are present in the ECG signal. Designing digital filters to reduce these noises is very important in ECG signal processing. In this work a digital FIR filter for reducing 50Hz power line noise in ECG signal is designed and implemented with different windowing methods as of Hamming, Kaiser, and Chebyshev. The work has been done in MATLAB environment. The result obtained for all FIR filters are compared by comparing the waveforms of the original and filtered ECG signals. The filter which gives the best results is the one using Chebyshev Window.

The computational cost for the design of FIR digital filters with short or medium length impulse responses is insignificant. However, the computational cost of conventional FIR filter design programs is o(L^{2}) , where L denotes the length of the impulse response. Several hours of CPU time may be required to compute the coefficients in a very long filter, as for example in the case of L = 1024 . Here we discuss an improved method which can significantly reduce the computational requirements for the design of very long FIR digital filters.

One ideal performance of this design is in the areas of decimation where a decimation factor of 10, 45-order and pass band ripple of 1dB and interpolation of sampled rates where a sinusoidal signal input produced a ripple free output with interpolation factor of 10, 52-order and stopband attenuation of 60dB. Owing to the multiple samples of filter length of 200, the filter performed down sampling preceded with filtering as well as up sampling preceded with filtering, hence multi-rate filter by allowing a low threshold of frequency of to be passed, blocking a high threshold of and vice versa. There was resampled output increased to 150% preceded by filtering. The filter coefficients for low pass and high pass Digital FIR filter, through the least square regression method, parks McClellan Algorithm and window methods were employed for easy optimization. More so, there was creation of 2-4-5 filter channel banks through the 2nd-level convolution of their down sampling and up sampling filtering techniques during the multi-irate filtering to ensure the design of error-free Digital FIR Filter using MATLAB File editor(M-File) and tool boxes for writing the C-programming of the design. In the analysis, the mean and standard deviation of the low pass Digital FIR Filter output during decimation and interpolation are (0.26, 6.13) and (0.004,1.22) respectively.

This paper presents an alternative approach for the design of linear phase digital high pass FIR filter using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSOCFIWA). FIR filter design is a multi-modal optimization problem. The conventional gradient based optimization techniques are not efficient for digital filter design. Given the filter specifications to be realized, PSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency characteristic. In this paper, for the given problem, the realization of the optimal FIR high pass filters of different orders has been performed. The simulation results have been compared to those obtained by the well accepted evolutionary algorithm such as genetic algorithm (GA). The results justify that the proposed optimal filter design approach using PSOCFIWA outperforms GA, not only in the accuracy of the designed filter but also in the convergence speed and solution quality.

An improved bee colony algorithm IBCA is proposed, which is a global stochastic searching optimisation algorithm possessing properties of both cultural algorithm and artificial bee colony ABC algorithm. Digital filter design involves multi-parameter optimisation, on which the existing optimisation algorithms don't work efficiently. This paper focuses on introducing IBCA and its performance in designing FIR digital filter and IIR digital filter. After presenting the basic knowledge about IBCA, we show how to implement it in FIR digital filter and IIR digital filter design with some adaptive measures to enhance its performance. It has been demonstrated by simulation results that the proposed IBCA outperforms particle swarm optimisation PSO, quantum particle swarm optimisation QPSO and ABC algorithms in finding out the global optima of the problem more rapidly.

In this paper, a novel optimization method called multi-criterion satisfactory optimization method (MCSOM) is proposed to design FIR digital filters. The main idea of the method is that multiple criterions can be adjusted easily, according to different requirements of different designers for performances of pass bands, stop bands and transition bands of FIR digital filters. By designing performance specification satisfactory rate functions and synthesis satisfactory rate function of FIR digital filters, satisfactory optimization mathematic model is constructed. The model is characterized by considering satisfactory design of performance specifications and optimization of parameters of FIR digital filters, simultaneously. At the same time, a new quantum genetic algorithm (NQGA) is also proposed to search automatically satisfactory solution in FIR digital filter design in this paper. Rapid convergence and good global search capability characterize the performance of NQGA. The effectiveness, applicability and flexibility of MCSOM based on NQGA are demonstrated by experimental results on the lowpass and bandpass FIR digital filters design. The results show that the introduced method is superior to look-up table method and the method in reference.

FIR and IIR digital filters design involve multi-parameter optimization, on which some existing intelligent algorithms don’t work efficiently. This paper focuses on employing the proposed differential cultural (DC) algorithm to design FIR and IIR digital filters. DC is a global stochastic searching technique that can find out the global optima of the problem more rapidly. After describing the theory and method of DC, we present how to use it in FIR and IIR digital filters design. It has been proved by simulation experiments that DC outperforms the particle swarm optimization (PSO), quantum particle swarm optimization (QPSO) and adaptive quantum particle swarm optimization (AQPSO) for the problem of filter design.

In this paper, two methods to design frequency-response-masking (FRM) FIR digital filters with discrete coefficients obtained from optimal continuous coefficients are investigated. First, a one-by-one (OBO) quantization from optimal continuous coefficients is described. Then, a novel recursion design (RD) is presented, which reduces computation by searching only possible quantization sequence in next recursion from the minimum magnitude response error in last recursion. To decrease current magnitude response error, the normal quantization is modified by adding an integer multiple of the quantization step value. For a further reduction, a re-optimization is inserted into the recursion procedure. A design example shows that RD can obtain the discrete coefficients with smaller maximum passband magnitude ripple than those using direct quantization (DQ) and OBO; whereas its maximum stopband magnitude ripple is almost identical to that of OBO.

A very efficient technique to drastically reduce the number of multipliers and adders in implementing linear-phase finite-impulse response (FIR) digital filters in applications demanding a narrow transition band is to use the frequency-response masking (FRM) approach originally introduced by Lim. The arithmetic complexity can be even further reduced using a common filter part for constructing the masking filters originally proposed by Lim and Lian. A drawback in the above-mentioned original FRM synthesis techniques is that the subfilters in the overall implementations are separately designed. In order to further reduce the arithmetic complexity in these two FRM approaches, the following two-step optimization technique is proposed for simultaneously optimizing the subfilters. At the first step, a good suboptimal solution is found by using a simple iterative algorithm. At the second step, this solution is then used as a start-up solution for further optimization being carried out by using an efficient unconstrained nonlinear optimization algorithm. An example taken from the literature illustrates that both the number of multipliers and the number of adders for the resulting optimized filter are less than 80% compared with those of the FRM filter obtained using the original FRM design schemes in the case where the masking filters are separately implemented. If a common filter part is used for realizing the masking filters, then an additional reduction of more than 10% is achieved compared with the optimized design with separately implemented masking filters.

Digital filters play a major role in signal processing that are employed in many applications such as in control systems, audio or video processing systems, noise reduction applications and different systems for communication. In this regard, FIR filters are employed because of frequency stability and linearity in their phase response. FIR filter design requires multi-modal optimization problems. Therefore, PSO (Particle Swarm Optimization) algorithm and its variants are more adaptable techniques based upon particles’ population in the search space and a great option for designing FIR filter. PSO and its different variants improve the solution characteristic by providing a unique approach for updating the velocity and position of the swarm. An optimized set of filter coefficient is produced by PSO and its variant algorithms which gives the optimized results in passband and stopband. In this research paper, Digital FIR filter is effectively designed by using PSO Algorithm and its two variants ARPSO and CRPSO in MATLAB. The outcomes prove that the filter design technique using CRPSO is better than filter design by PM algorithm. PSO and ARPSO Algorithms in the context of frequency spectrum and RMS error.

Digital filters are extensively used in video processing, digital communications, frequency analysis, auto control and so on. Digital filter is one of the most important part of digital signal process, almost appeared in all digital signal process system. Based on theory, the digital filters can be divided into FIR digital filter and IIR digital filter. FIR digital filter is achieved for these requirements and then widely used in modern signal processing. IIR digital filter is a kind of digital filter that its unit pulse response is unlimited long, and is also an important research task in modern electronics, communication and signal processing, many scholars did much research on the optimization design of IIR digital filter. High-density FPGA have real-time performance and flexibility, and it can provide powerful hardware support for the implementation of digital filters. In this dissertation, author will make some analysis on digital filters, and make realization of digital filters based on FPGA.

This paper presents a new method for the design of sharp linear phase FIR filters using the frequency-response masking (FRM) technique. The method is based on a weighted least square (WLS) approach, and the design problem is solved iteratively. The original least square (LS) problem is decomposed into two LS problems, each of which can be solved analytically. The effectiveness of the method is demonstrated through the design of a lowpass linear phase sharp FIR digital filter.

Some practical FIR digital filter optimality criteria and constraints are discussed. Algorithms are described and examples are given to demonstrate the effectiveness of these approaches. The discussion focuses on lowpass FIR digital filters with linear phase, but similar methods can be applied to other types of filters, such as IIR filters with bandpass, highpass, or multiband frequency responses.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper designs a FIR digital filter algorithm based on improved neural network. In the process of neural network learning, the algorithm can adjust the learning rate adaptively according to the error value between the frequency response of neural network output and the ideal frequency response, so that the algorithm can avoid the slow convergence or oscillation caused by fixed learning rate. In this paper, four FIR digital filter design examples are given. The simulation results show that the FIR filter design algorithm designed in this paper can achieve faster convergence speed and higher stopband attenuation than the neural network under fixed learning rate, which is an effective design method of FIR digital filter.