Finite Impulse Response (fir)

Finite Impulse Response filter plays an important role in many Digital Signal Processing applications. In this method, the multiplier less FIR filter is implemented using Distributed Arithmetic which consists of Look Up Table and then partitioning is involved. This architecture provides an efficient area-time power implementation which involves significantly less latency and less area-delay complexity when compared with existing structures for FIR Filter.

The Direct-form finite impulse response (FIR) filter implementations are illustrated in Fig.5 correspondingly. Though each architectures have similar difficulties in hardware, the transposed type is mostly most popular attributable to its higher performance and power potency The multiplication of filter coefficients with filter inputs is recognized has major effects on the complexness and performance of the look as a result of an oversized variety of constant multiplication are needed. This is often ordinarily recognized because the multiple constant multiplication operation. Even though area, delay and power economical multiplier architecture are projected, the total flexibility of a multiplier isn't necessary for the constant multiplications, since filter coefficients are mounted and determined beforehand by the CSD algorithmic rule. Thence filter coefficients multiplication with input file is mostly enforced underneath a typical sub expression constant sharing wherever every constant multiplication expression constant sharing wherever every constant multiplication is realized victimization CSD operation in an MCM operation.

Linear Finite Impulse Response (FIR) filter coefficients design has been extensively studied. Classical methods involve designing a FIR filter based on Fourier Series theory and Inverse-DFT transform. This is also the primary principle of the FIR design in MATLAB. However, researchers still continue to study improved methods for FIR filter design. Lertniphonphun developed an algorithm which designs a FIR filter using a weighted Chebyshev norm based optimization approach [1]. FIR designs subject to upper and lower bounds on the frequency response magnitude were studied by Wu [2]. Yong [3] investigated the design of FIR filters using a cluster of workstations as computing platform. Given the benefits of FIR filter applications to the digital signal processing field, it is reasonable to believe the feasibility of further innovation in this specific area.

In this paper, critical path of multiple constant multiplication (MCM) blocks is analyzed precisely and optimized for high-speed and low-intricacy implementation. A delay model predicated on signal propagation path is proposed for more precise estimation of critical path delay of MCM blocks than the conventional adder depth and the number of cascaded full adders. A dual objective configuration optimization (DOCO) algorithm is developed to optimize the shift-integrate network configuration to derive high-speed and low-intricacy implementation of the MCM block for a given fundamental set along with a corresponding adscititious fundamental set. A genetic algorithm (GA)-predicated technique is further proposed to probe for optimum supplemental fundamentals. In the evolution process of GA, the DOCO is applied to each probed adscititious fundamental set to optimize the configuration of the corresponding shift-integrate network. Experimental results show that the proposed GA-predicated technique reduces the critical path delay,Area, power consumption, area delay product and power delay product by 32.8%, 4.2%, 5.8%, 38.3%, and 41.0%, respectively, over other subsisting optimization methods. Keywords: Efficient Distributed Arithmetic (DA), Finite Impulse Response (FIR), Look-up- table (LUT).

Abstract: In this survey, the authors examine the trade-off between the unbiased, optimal, and in-between solutions in finite impulse response (FIR) filtering. Specifically, they refer to linear discrete real-time invariant state-space models with zero mean noise sources having arbitrary covariances (not obligatorily delta shaped) and distributions (not obligatorily Gaussian). They systematically analyse the following batch filtering algorithms: unbiased FIR (UFIR) subject to the unbiasedness condition, optimal FIR (OFIR) which minimises the mean square error (MSE), OFIR with embedded unbiasedness (EU) which minimises the MSE subject to the unbiasedness constraint, and optimal UFIR (OUFIR) which minimises the MSE in the UFIR estimate. Based on extensive investigations of the polynomial and harmonic models, the authors show that the OFIR-EU and OUFIR filters have higher immunity against errors in the noise statistics and better robustness against temporary model uncertainties than the OFIR and Kalman filters.

Abstract— In the last two decades, many efficient algorithms and architectures have been introduced for the design of low complexity bit-parallel multiple constant multiplications (MCM) operation which dominates the complexity of many digital signal processing systems. On the other hand, little attention has been given to the digit-serial MCM design that offers alternative low complexity MCM operations albeit at the cost of an increased delay. In this paper, we address the problem of optimizing the gate-level area in digit-serial MCM designs and introduce high- level synthesis algorithms, design architectures, and a computer aided design tool. Experimental results show the efficiency of the proposed optimization algorithms and of the digit-serial MCM architectures in the design of digit-serial MCM operations and finite impulse response filters. Finite impulse response (FIR) filters are widely used in digital signal processing applications due to their stability and linear phase characteristics. FIR filters have a large number of multiplications involved in the filter algorithm, which are usually implemented using fixed-point or integer number representations with the filter coefficients being represented by a finite number of bits. In hard-wired ASIC designs, multiplication operations are replaced by shift-and-add operations towards multiplier less FIR filter design. From a power perspective, the fewer the number of adders, the less power the filter will consume. The most common approaches to the implementation of digital filtering algorithms are general purpose digital signal processing chips for audio applications, or special purpose digital filtering chips and application-specific integrated circuits (ASICs) for higher rates. This project describes an approach to the implementation of digital filter algorithms on field programmable gate arrays (FPGAs).

Finite Impulse Response filter is widely used in signal and image processing applications. FIR filter has impulse response to any finite length of input. The filter settles to zero in finite time. The output of this linear time invariant system is determined by convolving its input signal with impulse response which is a weighted sum of the current and finite number of previous values of the input.

Abstract-The advancement in the field of CMOS technology has motivated the research to implement more and more complicated signal processing systems on a VLSI chip. The basic requirements of these signal processing units are to consume less power and have more functionality. The chip area, speed and power consumption are considered to be the criteria for evaluating the quality of the system. The increase in functionality, operating frequency and long battery life has made the low power portable electronic devices pragmatic in the recent years. The reduction in power consumption of a system, increases its battery life. In most of the signal processing algorithms, multiplication operation dominates other operations. The low power high performance multiplier plays a vital role in high performance Digital Signal Processing (DSP) systems developed using Multiply and Accumulator (MAC) unit and Finite Impulse Response (FIR) filter. Hence the designing of a low power multiplier becomes an important part in low power VLSI system design. In the recent years, the consideration of multiplier design has been focused to enhance its speed and throughput rate which are expected to affect the performance of the digital signal processing systems.

Abstract: The vulnerability of civil GPS receiver to interference may be intentional or unintentional. Among all types of interference, replay attack intended as the most dangerous intentional one. The signal structure of replay attack is almost the same with the satellite signal. The interference effects can be reduce with the design of an appropriate filter in the receiver. This paper presents two methods based on Finite Impulse Response (FIR) filter in frequency and time domain to mitigate the interference effect on GPS signals. Designed FIR filter protects GPS against the replay attack. The suggested filter is applied in the acquisition of the receiver. The proposed method has been implemented on collected dataset. The results show that the proposed algorithms significantly reduce interference. Also, they improve Position Dilution of Precision (PDOP) parameter. Based on the results, the FIR filter technique in time domain has better performance than the frequency domain.

Genetic Algorithms (GAs) are used to solve many optimization problems in science and engineering such as pattern recognition, robotics, biology, medicine, and many other applications. The aim of this paper is to describe a method of designing Finite Impulse Response (FIR) filter using Genetic Algorithm (GA). Digital filters are an essential part of DSP. The purpose of the filters is to allow some frequencies to pass unaltered, while completely blocking others. The digital filters are mainly used for two purposes: separation of signals that have been combined, and restoration of signals that have been distorted in some way. In this present work, FIR filter is designed using Genetic Algorithm (GM) and its comparison is done with Kaiser window function parameters. Out of the two techniques, GA offers a quick, simple and automatic method of designing low pass FIR filters that are very close to optimum in terms of magnitude response, frequency response and in terms of phase variation. With the help of GA, the numbers of operations in design process are reduced and coefficient calculation is easily realized.

Abstract. The main aim of the paper is to develop simplified tools and methods for design and analysis of linear parameter-varying (LPV) finite impulse response filters (FIR). FIR filters with constant coefficients have comprehen- sive theoretical foundations and design methods with the main advantages: good linearity of phase diagram, guaranteed stability, simple practical implementation. Although filters with constant coefficients guarantee particular properties in frequency domain, i.e. noise damping, they also increase rise time for rapid signal changes. In order to avoid such blur- ring effects a simplified design method for low-pass LPV FIR filters is developed. To synthesize the filter, two cut-off frequencies are needed accompanied with given filter order, shape tuning function and threshold detection condition for sequential operation. Quantitatively assess the filter quality and properties of the tuning functions are analyzed using both time and frequency dependent criteria. In the first case, difference Euclidean norm is used, while the frequency approach for filter analysis takes advantage of SVD-DFT transformation of linear time-varying discrete-time system, as previously defined by the author, employing singular value decomposition, discrete Fourier transformation and power spectral density properties.

Filter is a network used to remove unwanted component of a signal, such as noise. Digital filter better than analog filters because of their better stability, reliability and precision and also do not have matching problem. Communication, image processing, speech processing are the main application area of digital filters. There are two types of digital filters: FIR (Finite Impulse Response), IIR (Infinite Impulse Response). As compare to IIR filter, the FIR filter is a non-recursive (without feedback) structure, finite precision mathematical error is very small, while IIR filter is recursive (with feedback) structure and parasitic oscillation may occur because of feedback mechanism in the operation of IIR filter. FIR filter gives better amplitude and linear phase characteristic and also avoids the drift, noise and distortion as compare to IIR filters. The finite impulse response (FIR) filter is one of the most basic elements in a digital signal processing system, and it can guarantee a strict linear phase frequency characteristic with any kind of amplitude frequency characteristic. FIR used for higher order filter design to meet the design specification.

The characteristics of FIR low pass filter[3], the windowing technique and the required equations for the composite windows are explained. The implementation of the filters was carried out using MATLAB tool. The frequency response of FIR filter using composite window and individual windows are represented further[2]. Tabular forms for Relative side lobe attenuation, peak amplitude of side lobe, main lobe width and leakage factor are given in this paper for comparison .

ABSTRACT: Digital finite impulse response filters has a lot of arithmetic operation. Arithmetic operation modules such as adder and multiplier modules consume much power, energy and area in general. In order to reduce the area, delay and power consumption the multiplier module in FIR (finite impulse response filter) architecture is replaced by SMB (sum to modified booth) re-coder. . SMB performs direct recoding of sum of two numbers in its modified booth form. Modified booth is a prevalent form used in multiplication; it reduces the number of partial products into half. The proposed design for FIR filters have been designed using Verilog HDL and synthesized, implemented using Xilinx ISE and Modelsim.

The electrocardiogram (ECG) signal is susceptible to noise and artifacts and it is essential to remove the noise in order to support any decision making for specialist and automatic heart disorder diagnosis systems. In this paper, the use of Particle Swarm Optimization Neural Network (PSONN) for automatically identifying the cutoff frequency of ECG signal for low-pass filtering is investigated. Generally, the spectrums of the ECG signal are extracted from four classes: normal sinus rhythm, atrial fibrillation, arrhythmia and supraventricular. Baseline wander is removed using the moving median filter. A dataset of the extracted features of the ECG spectrums is used to train the PSONN. The performance of the PSONN with various parameters is investigated. The PSONN-identified cutoff frequency is applied to a Finite Impulse Response (FIR) filter and the resulting signal is evaluated against the original clean and conventional filtered ECG signals. The results show that the intelligent PSONN-based system successfully denoised the ECG signals more effectively than the conventional method.

Manchester encoding that transmits data and the format of the wireless transmission of data under the down-hole and develops a set of ground decoding systems. The ground decoding algorithm uses FIR (Finite impulse response) digital filtering to make de-noising on the mud pulse signal, then adopts the related base value modulating algorithm to eliminate the pump pulse base value of the denoised mud pulse signal, finally analyzes the mud pulse signal waveform shape of the selected Manchester encoding in three bits cycles, and applies the pattern similarity recognition algorithm to the mud pulse signal recognition. The field experiment results show that the developed device can make correctly extraction and recognition for the mud pulse signal with simple and practical decoding process and meet the requirements of engineering application.

The finite impulse response (FIR) filter uses finite measure- ments over the most recent time horizon of N discrete points. Basically, the unbiasedness can be met in FIR filters using two different strategies: 1) one may test an estimator by the unbiasedness condition or 2) one may embed the unbiased- ness constraint into the filter design. We therefore recognize below the checked (tested) unbiasedness (CU) and the em- bedded unbiasedness (EU). Accordingly, the FIR filter with CU and EU are denoted as FIR-CU filter and FIR-EU filter respectively.

S.Vijay et al. (2007) in [4] represented the complexity of Finite Impulse Response (FIR) filters and reduced by using number of adders and sub tractors for implementation of coefficient multipliers. A Common Sub expression Elimination (CSE) algorithm with a based on the Canonic Signed Digit (CSD) representation of coefficients of filter for implementing low complexity FIR filters. In Common Sub Expression Elimination (CSE), first write the expression in binary form. According to bit position, shift the variable and added up the shifted variable. Then maximizes grouping of the sub expressions for the reduction of operators.Canonical signed digits used where there is occurrence of consecutive non zero digits. Steps of converting a binary number into canonical digits repeated again and again until there is no consecutive non zero digits. Thus it’s very difficult to deal with canonical signed digits .

Finite Impulse Response filters have finite impulse response i.e. its response becomes zero at certain point whereas for IIR filters have impulse response that never settle downs to zero. FIR filters have linear phase response and has only poles. On the contrary IIR filters do not provide linear phase response and have both zeros and poles. FIR filters are non-recursive filters i.e. it does not require feedback whereas, IIR filters require feedback i.e. its output depends on past and present input as well as past outputs and hence are known as Recursive filters. FIR filters are more popular because of the convenience and flexibility it offers. FIR filters are symmetrical in nature and are always stable. FIR digital filters have certain disadvantages: FIR filters work on higher filter order, involve more computational complexity, require more memory and work slower than IIR filters. FIR filters are widely used in image processing, signal and speech processing and data transmission [7, 9].

Over the last few decades the field of Digital filters has grown to important both theoretically and technologically. Digital filters are capable of performance specifications that would, at best, be extremely difficult, if not impossible, to achieve with an analog implementation. In addition, the characteristics of a digital filter can be easily changed under software control. Digital filter can be broadly classified into two groups: recursive (infinite impulse response (IIR)) and non-recursive (finite impulse response (FIR)). An IIR filter can provide a much better performance than the FIR filters having the same number of coefficients. However, IIR filters might have a multi-modal error surface. Therefore, a reliable design method proposed for IIR filters must be based on a global search procedure. Digital IIR filters are widely used in the fields of automatic control, telecommunications, speech processing and etc. There are two main methods for IIR digital filters design. However, because the error surface of IIR filters is usually nonlinear and multimodal, conventional gradient-based design methods may easily get stuck in the local minima of error surface]. Therefore, some researchers have attempted to develop design methods based on modern heuristic optimization algorithms.