FPGA-Based Efficient Programmable Polyphase FIR Filter

The modelling, design and implementation of a high-speed programmable polyphase finite impulse response (FIR) filter with field programmable gate array (FPGA) technology are described. This FIR filter can run automatically according to the programmable configuration word including symmetry/asymmetry, odd/even taps, from 32 taps up to 256 taps. The filter with 12 bit signal and 12 bit coefficient word-length has been realized on a Xilinx VirtexⅡ-v1500 device and operates at the maximum sampling frequency of (160 MHz.)

Optimal Linear Phase Finite Impulse Response Band Pass Filter Design Using Craziness Based Particle Swarm Optimization Algorithm

An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using craziness based particle swarm optimization(CRPSO) approach.Given the filter specifications to be realized,the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics.In this paper,for the given problem,the realizations of the optimal FIR band pass filters of different orders have been performed.The simulation results have been compared with those obtained by the well accepted evolutionary algorithms,such as Parks and McClellan algorithm(PMA),genetic algorithm(GA) and classical particle swarm optimization(PSO).Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO,not only in the accuracy of the designed filter but also in the convergence speed and solution quality.

High-Performance FIR Filter Implementation Using Anurupye Vedic Multiplier

In this, today’s world immeasurable analysis goes within the field of communication and signal processing applications. The FIR filter is mostly employed in filtering applications to enhance the quality of the signal. In any processor, the performance of the system is based on the speed of the multiplier unit involved in its operation. Since multiplier forms the indispensable building blocks of the FIR filter system. Its performance has contributed in determining the execution of the FIR filter system. Also, due to the tremendous development in the technology, many approaches such as an array, Vedic methods are made to speed up the multiplier computations. The problem in speed-up operation and resource utilization of hardware with all the conventional methods due to the critical path found in partial products has to be optimized using proposed method. This paper presents the implementation and execution of a FIR Filter design using Anurupye multiplier. Here the FIR filter is examined by using various multiplier algorithms such as Anurupye, Urdhava Tiryagbhyam, and array multipliers. The FIR filter is simulated for analyzing delay;area and power are meted out and lessened by utilizing proposed Anurupye multiplier. The FIR filter design utilizing proposed multiplier offers delay around 18.99 and only 4% of LUT slice utilization compared to existing methods. This architecture is coded in VHDL, simulated using the ModelSim and synthesized with Xilinx.

An Orthogonal Least Squares Based Approach to FIR Designs

This paper is concerned with the application of forward Orthogonal Least Squares (OLS) algorithm to the design of Finite Impulse Response (FIR) filters. The focus of this study is a new FIR filter design procedure and to compare this with traditional methods known as the fir2() routine provided by MATLAB.

Measuring Mental Workload Using ERPs Based on FIR,ICA,and MARA

Mental workload is considered to be strongly linked to human performance,and the ability to measure it accurately is key for balancing human health and work.In this study,brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload.In addition,a finite impulse response(FIR)filter,independent component analysis(ICA),and multiple artifact rejection algorithms(MARAs)were used to filter event-related potentials(ERPs).Then,the data consisting of ERPs,subjective ratings of mental workload,and task performance,were analyzed through the use of variance and Spearman’s correlation during a simulated computer task.We found that participants responded faster and performed better in the easy task condition,followed by the medium and high-difficulty conditions,which verifies the validity of the ERP filtering.Moreover,larger P2 and P3 waveforms were evoked as the task difficulty increased,and a higher task difficulty elicited a more enhanced N300.Correlation analysis revealed a negative relationship between the amplitude of P3 and the subjective ratings,and a positive relationship between the P3 amplitude and accuracy.The results presented in this paper demonstrate that a combination of FIR,ICA,and MARA methods can filter ERPs in the non-invasive real-time measurement of workload.Additionally,frontocentral P2,N3,and parietal P3 components showed differences between genders.The proposed measurement of mental workload can be useful for real-time identification of mental states and can be applied to human-computer interaction in the future.

Classification of Epileptic Electroencephalograms Using Time-Frequency and Back Propagation Methods

Today,electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor.These signals are frequently used to obtain information about brain neurons and may detect disorders that affect the brain,such as epilepsy.Electroencephalogram(EEG)signals are however prone to artefacts.These artefacts must be removed to obtain accurate and meaningful signals.Currently,computer-aided systems have been used for this purpose.These systems provide high computing power,problem-specific development,and other advantages.In this study,a new clinical decision support system was developed for individuals to detect epileptic seizures using EEG signals.Comprehensive classification results were obtained for the extracted filtered features from the time-frequency domain.The classification accuracies of the time-frequency features obtained from discrete continuous transform(DCT),fractional Fourier transform(FrFT),and Hilbert transform(HT)are compared.Artificial neural networks(ANN)were applied,and back propagation(BP)was used as a learning method.Many studies in the literature describe a single BP algorithm.In contrast,we looked at several BP algorithms including gradient descent with momentum(GDM),scaled conjugate gradient(SCG),and gradient descent with adaptive learning rate(GDA).The most successful algorithm was tested using simulations made on three separate datasets(DCT_EEG,FrFT_EEG,and HT_EEG)that make up the input data.The HT algorithm was the most successful EEG feature extractor in terms of classification accuracy rates in each EEG dataset and had the highest referred accuracy rates of the algorithms.As a result,HT_EEG gives the highest accuracy for all algorithms,and the highest accuracy of 87.38%was produced by the SCG algorithm.

Simulation Based Design Analysis of an Adjustable Window Function

Window based Finite Impulse Response filters have the problem that in order to obtain better performance from these filters in terms of minimum stopband attenuation cost has to be paid for half main-lobe width and vice-versa. A solution of this contradictory behavior is to increase the length of the window which in turn requires more hardware hence increasing the cost of system. This paper proposes a novel window based on two shifted hyperbolic tangent functions. The proposed window contains an adjustable parameter, with the help of which desired time and frequency domain characteristics may be achieved for relatively shorter window length. The characteristics of the proposed window are compared with those of the two well-known adjustable windows namely Cosh window and Exponential window. MATLAB simulation results show that for the same value of window length, the proposed window provides improved output, and thus it makes a good compromise between minimum stopband attenuation and half main-lobe width compared to the windows mentioned previously.

A New FIR Filter for State Estimation and Its Application

This paper proposes a new FIR （finite impulse response） filter under a least squares criterion using a forgetting factor. The proposed FIR filter does not require information of the noise covariances as well as the initial state, and has some inherent properties such as time-invariance, unbiasedness and deadbeat. The proposed FIR filter is represented in a batch form and then a recursive form as an alternative form. Prom discussions about the choice of a forgetting factor and a window length, it is shown that they can be considered as useful parameters to make the estimation performance of the proposed FIR filter as good as possible. It is shown that the proposed FIR filter can outperform the existing FIR filter with incorrect noise covariances via computer simulations. Finally, as a useful application, an image sequence stabilization problem is considered. Through this application, the FIR filtering based approach is shown to be superior to the Kalman filtering based approach.

Fault Detection Based on FIR Filters with Variable Discount Factors for Industrial Robot Drive Systems

To enhance dynamic tracking performance and anti-disturbance capacity of finite impulse response(FIR) filters, variable discount factors are introduced to the recursive least squares(RLS) algorithm. By employing improved FIR filters to conduct modelling of industrial robot drive systems, dynamic characteristics of the target systems are identified. Then the fault detection for a target system can be utilized by analyzing the coefficients of the FIR filter. Finally, an application of the fault detection scheme to a kind of brushless DC motor drive system is described. Compared with reference methods, the proposed scheme achieves effective fault detection and performs better in dynamic tracking and robustness according to the final simulation results.