Digital Filtering Algorithm Research Articles

A fast block FIR adaptive digital filtering algorithm with individual adaptation of parameters

A general formulation for developing a fast-block least-mean-square (LMS) adaptive algorithm is presented. In this algorithm, a convergence factor is obtained that is tailored for each adaptive filter coefficient and is updated at each block iteration. These convergence factors are chosen to minimize the mean-squared error in the processed block and are easily computed from readily available signals. The algorithm is called the optimum block adaptive algorithm with individual adaptation of parameters (OBAI). It is shown that the new coefficient vector obtained from the OBAI algorithm is an estimate of the Wiener solution at each iteration. Implementation aspects of OBAI are examined and a technique is presented that eliminates matrix inversion by processing signals in overlapping blocks and applying the matrix inversion lemma. When the coefficients are updated once per input data sample, the resulting OBAI algorithm requires 7N/sup 2/-5N+9 multiplications and divisions (MAD) per iteration, where N is the number of estimated parameters. The convergence properties of OBAI are investigated and compared with several recently proposed algorithms. >

Pipelined VLSI systolic array and algorithm for digital filtering: R E Siferd (Dept. of Electr. Syst. Eng., Wright State Univ., Dayton, OH, USA) Int. J. Mini & Microcomput. (USA), vol. 9, no. 1, pp.20–22 (1987)

Pipelined VLSI systolic array and algorithm for digital filtering: R E Siferd (Dept. of Electr. Syst. Eng., Wright State Univ., Dayton, OH, USA) Int. J. Mini & Microcomput. (USA), vol. 9, no. 1, pp.20–22 (1987)

Information theory and Kalman filtering methods for the determination of polyaromatic hydrocarbons

The Kalman filter is a recursive, digital filtering algorithm which has been used for state and parameter estimation in several areas of chemistry. The filter is based on a two-part model, consisting of a description of the system dynamics, and a description of the measurement process. The algorithm equations allow both linear and nonlinear models to be employed, and offer several advantages over more conventional fitting procedures. Here, some studies are described which are based on the use of Kalman filtering techniques for the determination of polyaromatic hydrocarbon compounds. Thin-layer chromatographic techniques have been used in conjunction with fluorescence spectroscopic detection to determine these compounds. Several approaches using the Kalman filter have been developed to allow improved analysis characteristics for these compounds. The Kalman filter algorithm has been used to resolve overlapped chromatographic and fluorescence responses. An adaptive Kalman filter algorithm has been combined with a derivative spectroscopic model which allows variable background responses to be subtracted and accurate concentration estimates to be obtained. In addition, the Kalman filter has been used in conjunction with information theory principles in order to determine optimal experimental designs for multidimensional fluorescence measurements. Two related measures of the information yield have been employed to select subsets of an excitation—emission matrix which yield the highest information.

A portable high-speed digital image processing instrument for the enhancement of television images

It is known that digital images may be usefully enhanced using two-dimensional (2D) recursive filters. The associated digital filter algorithms must have acceptable word lengths and be implement-able over the entire image at an acceptable processing speed. Spatial-integrator 2D filtering algorithms and recursion along diagonal lines of pixels are used to permit the design and construction of a fast, portable, and low-cost television image-enhancement instrument. Menu-driven software permits the nonspecialist user to enhance television images in less than 4 s using any of 27 different 2D filter transfer functions.

Some efficient algorithms for digital filtering

Some efficient algorithms for digital filtering

Digital Filters for Real-Time ECG Signal Processing Using Microprocessors

Traditionally, analog circuits have been used for signal conditioning of electrocardiograms. As an alternative, algorithms implemented as programs on microprocessors can do similar filtering tasks. Also, digital filter algorithms can perform processes that are difficult or impossible using analog techniques. Presented here are a set of real-time digital filters each implemented as a subroutine. By calling these subroutines in an appropriate sequence, a user can cascade filters together to implement a desired filtering task on a single microprocessor. Included are an adaptive 60-Hz interference filter, two low-pass filters, a high-pass filter for eliminating dc offset in an ECG, an ECG data reduction algorithm, band-pass filters for use in QRS detection, and a derivative-based QRS detection algorithm. These filters achieve real-time speeds by requiring only integer arithmetic. They can be implemented on a diversity of available microprocessors.

A 16-Bit Microcomputer for Audio Bandwidth Digital Filtering

Small microcomputers using 8-bit machines have been used in teaching digital filtering and discrete-time control. These systems have two major limitations: 1) bandwidth limited to approximately 125 Hz for fourth-order algorithms, and 2) limited precision for representing the coefficients in the algorithm. This paper describes the development and application of a high-speed 16-bit machine which largely overcomes the limitations of previous systems. A special-purpose digital filtering and control computer was built using the MC68000 microprocessor operating at 12.5 MHz. This machine uses 16-bit data transfers to memory and 32-bit arithmetic. The multiply and divide instructions are microcoded in the instruction set, and therefore execute much faster than subroutines used in 8-bit machines. The system has a general-purpose monitor routine and a digital filtering algorithm in firmware, user RAM, two channels of A/D, two channels of D/A, a programmable timer, and an RS232 interface for a CRT. The firmware implements any filtering or compensation algorithm through eighth order in the form of a linear difference equation. The greater precision of the 16-bit machine allows the coefficients in the difference equation to be represented accurately without extensive rationalization procedures. The firmware is written for student use in digital filtering and control laboratories. The computer prompts the user for coefficients and the sampling interval, informing him of any input errors. It then continuously runs the selected algorithm until interrupted. This system has been found to be of much wider bandwidth than previous machines. Signals of 4.

Textural Analysis And Real-Time Classification of Sea-Ice Types Using Digital SAR Data

Digital measures of synthetic-aperture radar (SAR) image texture, as well as the local approximation to the mean value of individual ice types, were used to perform discrimination and mapping of ice types. The SAR data described in this paper were gathered in March, 1979, over the Beaufort Sea as part of the Canadian SURSAT project. Digital SAR data from a 3 × 3 km area were obtained using optical processing of the signal film and digital recording of the output image. Prior to performing the textural analysis, a digital filter algorithm was developed that minimizes the effect of radar-system-generated coherent speckle and produces an image approximating local tone while preserving edge definition. This image was used in the analysis to separate image tone from image texture. The textural analysis, which included calculating the entropy and inertia of the image, indicated that first- and multiyear, smooth- and rough-ice types could be distinguished based on the textural values obtained from the data with an overall accuracy of 65 percent. This study has also considered the use of cellular operations based upon neighborhood transformations to calculate the textural values. This computation method can potentially reduce the time to compute textural features on a general-purpose computer to near real-time rates.

Systematic design and programming of signal processors, using project management techniques

This paper presents a systematic method of designing and microprogramming fast signal processors (SP's) while optimally utilizing the inherent parallelism of a given algorithm. The method employs graph-theoretical concepts and scheduling techniques from "project management" and permits an easy evaluation of the following key design parameters: i) the lower bound on the algorithm duration (T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mp</inf> ) for given speeds of the arithmetic components in a signal processor, ii) the minimum arithmetic hardware requirements necessary for the completion of the algorithm within the time T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mp</inf> , and iii) the optimum algorithm schedule and the corresponding minimum attainable duration T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">at</inf> , when constraints on the available SP resources are imposed. In combination with the data-flow analysis and resource allocation, the scheduling procedure permits the influence of the SP architecture on the schedule to be modeled and the basic architectural features of the SP to be determined. The results also provide information for automatic microprogram generation and for the assessment and comparison of signal processor performance and algorithm speeds. The synthesis of architectures is based on an "ideal, data-flow driven signal processor" that is introduced in the paper. The proposed design approach is demonstrated on various digital-filter algorithms.

Determination of Transient Apparent Impedances of Faulted Transmission Lines

The paper describes a method of determining transient apparent impedances of faulted transmission lines, as would be seen by a digital distance relay. Typical relay operating results are presented. The method described in this paper may be applied to any existing digital distance relay and digital filter algorithms, and therefore enables one to seek out what may be considered the best of optimal relay and filter algorithms.

Identification of a Best Algorihthm for Digital Distance Protection of Transmission Lines

The paper describes amethod for seeking out the best or optimal combination of digital distance relay and digital filter algorithm for transmission line protection. It is found that this best combination consists of a 3rd order recursive low pass filter algorithm and distance relay algorithm based on the solution of the differential equation representing the transmission line model. Typical relay transient operating characteristics on R-X co-ordinates are presented.

Reduction of analytical variance by using a discrete-time data-weighting filter to estimate abrupt changes in batch-type processes : Part 2. Applications

A well known characteristic of analytical methods in clinical laboratories is the presence of a significant between-batch variance component. This is confirmed by an analysis of variance of data obtained for the determination of inorganic phosphate in blood serum with a continuous flow system. Based on a model describing the process fluctuations, the proposed discrete-time digital filter algorithm estimates abrupt changes in the process values between two runs of samples. Subsequent correction of the control sera data for these estimates leads to a reduction of about 50% in the variance. Similar results were obtained for other analytical methods including a centrifugal fast analyzer, an automatic discrete system and a semi-automatic coulometric titration.

Experimental performance data for slowed-down adaptive algorithms†

Considerable savings in hardware and computation time can be achieved very simply by slowing down the adaptive algorithm for an adaptive digital filter either by updating weights loss often than every sample (typically every n samples, where n is the number of fitter weights), or by updating only one weight per sample cyclically repeating every n samples. This technique has the advantage of being useful with any of the many adaptive algorithms including the LMS non-recursive algorithm and the Stearns-White recursive algorithm. Experiments verify that the slowed down algorithms not only save hardware and computation time, but actually improve filter performance.

A procedure for decomposing the myoelectric signal into its constituent action potentials--Part I: Technique, theory, and implementation.

A technique has been developed which enables the decomposition (separation) of a myoelectric signal into its constituent motor unit action potential trains. It consists of a multichannel (via one electrode) myoelectric signal recording procedure, a data compression algorithm, a digital filtering algorithm, and a hybrid visual-computer decomposition scheme. The algorithms have been implemented on a PDP 11/34 computer. Of the four major segments of the technique, the decomposition scheme is by far the most involved. The decomposition algorithm uses a-sophisticated template matching routine and details of the firing statistics of the motor units to identify motor unit action potentials in the myoelectric signal, even when they are super-imposed with other motor unit action potentials. In general, the algorithms of the decomposition scheme do not run automatically. They require input from the human operator to maintain reliability and accuracy during a decomposition.

Hyperstability and adaptive filtering

Recently, results from the area of system identification concerning the concept of system hyperstability have been applied to the analysis of adaptive filtering configurations. In particular, this analysis tool has allowed the development of a family of convergent adaptive recursive digital filtering algorithms, where the use of conventional gradient analysis techniques had proven insufficient. This correspondence provides a somewhat informal explanation of hyperstability analysis as might be useful to the adaptive signal processing community, and clarifies its implications and limitations.

Time Domain Analysis of Machinery Vibration Signals Using Digital Techniques

The design and implementation of a general purpose processing system to analyze machinery vibration signals in the time domain are presented. The system comprises a data acquisition stage, where analog signals having a maximum frequency content up to 4 kHz and a dynamic range up to 72 dB, are converted into digital form and a processing stage, where modular software algorithms are employed to perform specific operations in an interactive environment. Processing capabilities encompass scaling, integration and the computation of statistical parameters such as means, RMS, standard deviation, maximum and minimum values. The input signal dynamic range and maximum frequency content are shown to be the governing criteria in the design and performance of the analysis system. The influence of hardware precision level and sampling rate on the overall accuracy of the obtained results is illustrated by digitizing standard waveforms under different processing parameters. Extraneous components introduced at the digitization and integration stages are identified and eliminated by employing a versatile digital filter algorithm. The performance of the system in processing acceleration signals from a 3 HP, 14000 RPM internal combustion engine is demonstrated and the different application aspects of such a facility in the machinery maintenance field are outlined.

Design of a Microprocessor Based Receiver for Ripple Control

Ripple control is used as a direct means of load management to minimise peak demand. This paper describes the design considerations involved in designing a microprocessor based receiver that can be used to detect and decode the ripple control signals transmitted over the normal power line. Since the cost of the receiver has to be as low as possible, the receiver design should be simple but effective. A digital filter algorithm with integer coefficients that can be used to detect the ripple signal is discussed. The algorithm can be readily implemented even in microprocessors with limited computational ability. A simple but effective technique for decoding the command message is also described. The use of the microprocessor makes it possible to program a wide variety of command message formats to meet different customer requirements. This paper shows how this can be done.

Design of a computerized emission tomographic system

The design of a computerized tomographic system is considered. The algorithms for digital filtering and two-dimensional interpolation are analyzed. The disturbances introduced by the small number of projections, the finite impulse response of the filter, rounding and crude interpolation are discussed. Finally a recently developed algorithm for tissue absorption compensation is briefly summarized.

Sequential regression considerations of adaptive filtering

The purpose of this communication is two-fold. First, we discuss the notion of adaptive filtering via a sequential regression formulation. The related algorithm is referred to as an s.e.r. (sequential regression algorithm). Secondly, it is shown that this formulation also leads to the adaptive digital filter (a.d.f.) algorithms attributed to Widrow, and Stearns.

A High-Speed Third-Octave Filtering Algorithm for Real-Time Noise Analysis

An efficient third-octave digital filtering algorithm has been designed and implemented, meeting ANSI S1.11-1966, Class III, for real-time applications. The approach is seen to produce significant savings in computational effort compared to recursive realizations (by as much as a factor of 10 on constant confidence limit configurations spanning 10 or more octaves) while avoiding extensive data storage and response inadequacies associated with FFT (fast Fourier transform) methods. Results taken from a prototype program implemented on a high-speed computer show the response to pure tones and white noise over a 10 Hz to 50 kHz bandwidth. Adaptations for real-time applications are discussed for tailoring measurement confidence limits to the data bandwidth and speed of the machine.