scholarly journals Adaptive Parameter Estimation of IIR System-Based WSN Using Multihop Diffusion in Distributed Approach

2020 ◽  
Vol 14 (4) ◽  
pp. 30-41
Author(s):  
Meera Dash ◽  
Trilochan Panigrahi ◽  
Renu Sharma ◽  
Mihir Narayan Mohanty
Keyword(s):  
Impulse Response ◽  
Sensor Node ◽  
Finite Impulse Response ◽  
Lms Algorithm ◽  
Sensor System ◽  
Superior Performance ◽  
Estimation Accuracy ◽  
Infinite Impulse Response ◽  
Communication Overhead ◽  
Least Mean Square

Distributed estimation of parameters in wireless sensor networks is taken into consideration to reduce the communication overhead of the network which makes the sensor system energy efficient. Most of the distributed approaches in literature, the sensor system is modeled with finite impulse response as it is inherently stable. Whereas in real time applications of WSN like target tracking, fast rerouting requires, infinite impulse response system (IIR) is used to model and that has been chosen in this work. It is assumed that every sensor node is equipped with IIR adaptive system. The diffusion least mean square (DLMS) algorithm is used to estimate the parameters of the IIR system where each node in the network cooperates themselves. In a sparse WSN, the performance of a DLMS algorithm reduces as the degree of the node decreases. In order to increase the estimation accuracy with a smaller number of iterations, the sensor node needs to share their information with more neighbors. This is feasible by communicating each node with multi-hop nodes instead of one-hop only. Therefore the parameters of an IIR system is estimated in distributed sparse sensor network using multihop diffusion LMS algorithm. The simulation results exhibit superior performance of the multihop diffusion LMS over non-cooperative and conventional diffusion algorithms.

scholarly journals Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics

2008 ◽  
Vol 2008 ◽  
pp. 1-10
Author(s):  
F. Cadini ◽  
E. Zio ◽  
N. Pedroni
Keyword(s):  
Impulse Response ◽  
Multilayer Perceptron ◽  
Nuclear Reactor ◽  
Finite Impulse Response ◽  
Nonlinear Modeling ◽  
Nuclear Industry ◽  
Superior Performance ◽  
Infinite Impulse Response ◽  
Nuclear Dynamics ◽  
Static Modeling

Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. Their use is becoming increasingly popular in the complex modeling tasks required by diagnostic, safety, and control applications in complex technologies such as those employed in the nuclear industry. In this paper, the nonlinear modeling capabilities of an infinite impulse response multilayer perceptron (IIR-MLP) for nuclear dynamics are considered in comparison to static modeling by a finite impulse response multilayer perceptron (FIR-MLP) and a conventional static MLP. The comparison is made with respect to the nonlinear dynamics of a nuclear reactor as investigated by IIR-MLP in a previous paper. The superior performance of the locally recurrent scheme is demonstrated.

Vibration Suppression Based on Adaptive Feedforward Control With Infinite Impulse Response Filter

2015 ◽  
Author(s):  
Shiying Zhou ◽  
Masayoshi Tomizuka
Keyword(s):  
Impulse Response ◽  
Vibration Suppression ◽  
Finite Impulse Response ◽  
Feedforward Control ◽  
Vibration Signal ◽  
Infinite Impulse Response ◽  
Least Mean Square ◽  
Parameter Adaptation ◽  
Iir Filter ◽  
Adaptive Feedforward

This paper presents adaptive feedforward control for vibration suppression based on an infinite impulse response (IIR) filter structure. The vibration signal and the output signal are available for the algorithm to adaptively update the parameters of the vibration transmission path (VTP) dynamics. Two designs for parameter adaptation are proposed. They provide different methods to get the necessary signals for parameter adaptation of the IIR filter which is different from the conventional finite impulse response (FIR) filter adaptation design. Performance of the proposed designs is compared with the conventional Filtered-x Least Mean Square (FxLMS) method on a hard disk drive (HDD) benchmark problem. The simulation results show that the proposed designs have smaller 3σ value and peak to peak value at steady state.

Application of Kautz Models for Adaptive Vibration Control

2001 ◽  
Author(s):  
Dirk Mayer ◽  
Sven Herold ◽  
Holger Hanselka
Keyword(s):  
Vibration Control ◽  
Impulse Response ◽  
Adaptive Filter ◽  
Finite Impulse Response ◽  
Active Vibration Control ◽  
Active Noise Control ◽  
Lms Algorithm ◽  
Infinite Impulse Response ◽  
Fir Filters ◽  
Allpass Filters

Abstract Both for active noise control (ANC) and active vibration control (AVC) the well known F-X-LMS-algorithm can be used. This approach requires a proper model of the path from the actuator to the error sensor, preferably received with an on-line identification. In the field of ANC adaptive finite impulse response (FIR) filters work well for this task, but for lightly damped mechanical systems with long impulse responses FIR filters with up to several thousand coefficients would have to be used. One alternative are adaptive IIR filters, but these can get unstable while adapting or the adapting process can get stuck in local minima. In this work, adaptive Kautz models are introduced, which need some a priori knowledge about the poles of the system. On the other hand, they represent an infinite impulse response while maintaining the transversal structure of the adaptive filter. This is reached by generalization of the FIR filter, for which the delay operator is substituted by discrete allpass filters, the Kautz filters. The adaptive filter bank is implemented by means of the straightforward LMS algorithm in the Matlab/Simulink environment. As an example, system identification with Kautz models and their usage in AVC for a simple mechanical system will be studied.

scholarly journals NANO-studio, the design environment of filter banks implemented in standard CMOS technology

2021 ◽  
Author(s):  
Andrzej Handkiewicz ◽  
Mariusz Naumowicz
Keyword(s):  
Impulse Response ◽  
Filter Banks ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Cmos Process ◽  
Digital Cmos ◽  
Additional Advantage ◽  
Analysis And Synthesis ◽  
Optimization Parameters ◽  
Synthesis Filter

AbstractThe paper presents a method of optimizing frequency characteristics of filter banks in terms of their implementation in digital CMOS technologies in nanoscale. Usability of such filters is demonstrated by frequency-interleaved (FI) analog-to-digital converters (ADC). An analysis filter present in these converters was designed in switched-current technique. However, due to huge technological pitch of standard digital CMOS process in nanoscale, its characteristics substantially deviate from the required ones. NANO-studio environment presented in the paper allows adjustment, with transistor channel sizes as optimization parameters. The same environment is used at designing a digital synthesis filter, whereas optimization parameters are input and output conductances, gyration transconductances and capacitances of a prototype circuit. Transition between analog s and digital z domains is done by means of bilinear transformation. Assuming a lossless gyrator-capacitor (gC) multiport network as a prototype circuit, both for analysis and synthesis filter banks in FI ADC, is an implementation of the strategy to design filters with low sensitivity to parameter changes. An additional advantage is designing the synthesis filter as stable infinite impulse response (IIR) instead of commonly used finite impulse response (FIR) filters. It provides several dozen-fold saving in the number of applied multipliers.. The analysis and synthesis filters in FI ADC are implemented as filter pairs. An additional example of three-filter bank demonstrates versatility of NANO-studio software.

Comparison of Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) Filters in Cardiac Optical Mapping Records

2021 ◽  
pp. 207-224
Author(s):  
David Rivas-Lalaleo ◽  
Sergio Muñoz-Romero ◽  
Monica Huerta ◽  
Víctor Bautista-Naranjo ◽  
Jorge García-Quintanilla ◽  
...  
Keyword(s):  
Impulse Response ◽  
Optical Mapping ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Fir Filters

The Spectra of Filters

2021 ◽  
pp. 204-268
Author(s):  
Victor Lazzarini
Keyword(s):  
Fourier Transform ◽  
Impulse Response ◽  
Discrete Time ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Main Body ◽  
Analysis Tool ◽  
The Fourier Transform ◽  
Definition Of

This chapter now turns to the discussion of filters, which extend the notion of spectrum beyond signals into the processes themselves. A gentle introduction to the concept of delaying signals, aided by yet another variant of the Fourier transform, the discrete-time Fourier transform, allows the operation of filters to be dissected. Another analysis tool, in the form of the z-transform, is brought to the fore as a complex-valued version of the discrete-time Fourier transform. A study of the characteristics of filters, introducing the notion of zeros and poles, as well as finite impulse response (FIR) and infinite impulse response (IIR) forms, composes the main body of the text. This is complemented by a discussion of filter design and applications, including ideas related to time-varying filters. The chapter conclusion expands once more the definition of spectrum.

scholarly journals A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 683 ◽  
Author(s):  
Yingsong Li ◽  
Yanyan Wang ◽  
Laijun Sun
Keyword(s):  
Impulse Response ◽  
Adaptive Filter ◽  
Finite Impulse Response ◽  
Least Mean Square ◽  
Sparse Systems ◽  
Simulation Results ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Fir Systems ◽  
Filter Algorithms

A proportionate-type normalized maximum correntropy criterion (PNMCC) with a correntropy induced metric (CIM) zero attraction terms is presented, whose performance is also discussed for identifying sparse systems. The proposed sparse algorithms utilize the advantage of proportionate schemed adaptive filter, maximum correntropy criterion (MCC) algorithm, and zero attraction theory. The CIM scheme is incorporated into the basic MCC to further utilize the sparsity of inherent sparse systems, resulting in the name of the CIM-PNMCC algorithm. The derivation of the CIM-PNMCC is given. The proposed algorithms are used for evaluating the sparse systems in a non-Gaussian environment and the simulation results show that the expanded normalized maximum correntropy criterion (NMCC) adaptive filter algorithms achieve better performance than those of the squared proportionate algorithms such as proportionate normalized least mean square (PNLMS) algorithm. The proposed algorithm can be used for estimating finite impulse response (FIR) systems with symmetric impulse response to prevent the phase distortion in communication system.

An Improved ZA-LMS Algorithm for Sparse System Identification with Variable Sparsity

2014 ◽  
Vol 602-605 ◽  
pp. 2415-2419 ◽  
Author(s):  
Hui Luo ◽  
Yun Lin ◽  
Qing Xia
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse System ◽  
Sparse System Identification ◽  
Least Mean Square Algorithm ◽  
Improved Algorithm

The standard least mean square algorithm does not consider the sparsity of the impulse response,and the performs of the ZA-LMS algorithm deteriorates ,as the degree of system sparsity reduces or non-sparse . Concerning this issue ,the ZA-LMS algorithm is studied and modified in this paper to improve the performance of sparse system identification .The improved algorithm by modify the zero attraction term, which attracts the coefficients only in a certain range (the “inactive” taps), thus have a good performance when the sparsity decreases. The simulations demonstrate that the proposed algorithm significantly outperforms then the ZA-LMS with variable sparisity.

Signal Processing Techniques for Ultrasonic Waves to Measure Stresses in Oil Pipelines

2013 ◽  
Author(s):  
Paulo Pereira ◽  
Cleudiane S. Santos ◽  
Auteliano A. dos Santos
Keyword(s):  
Impulse Response ◽  
Structural Integrity ◽  
Finite Impulse Response ◽  
Ultrasonic Waves ◽  
Economic Losses ◽  
Infinite Impulse Response ◽  
Environmental Damage ◽  
Discrete Wavelet ◽  
Material Sources ◽  
Oil Pipelines

Ensuring the structural integrity of oil pipelines is vital to prevent environmental damage and economic losses. In that sense, it is important to know the magnitude of the stress in the pipe, which must be done using non-destructive techniques. Measuring stress using ultrasonic longitudinal critically refracted waves (LCR) has been applied to pipelines with very promising results. The technique is based on the acoustoelastic theory that relates speed variation of elastic waves traveling in the material with its state of strain. Nevertheless, the signals acquired from piezoelectric transducers in such application may show high levels of noise coming mainly from material sources (grain boundaries, irregularities). The noise makes the measurement of wave velocity difficult, resulting in imprecise evaluations of the stress in the pipeline. The aim of this study is to evaluate techniques for filtering digital signals of LCR waves propagating in an oil pipe fabricated with API 5L X70 steel. We analyzed the signal-to-noise ratio (SNR) of digitalized acquired signals in four circumstances: without treatment; signals treated with successive averages; treated with FIR (Finite Impulse Response) and IIR (Infinite Impulse Response) digital filters, and with the Discrete Wavelet Transform (DWT). The results show that the signals treated with DWT present better SNR compared to the other techniques.

scholarly journals Comprehensive Optimal Fir Filter Design Procedures With Various Impacts

2019 ◽  
Vol 8 (3) ◽  
pp. 1562-1566
Keyword(s):  
Signal Processing ◽  
Impulse Response ◽  
Adaptive Filters ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Infinite Impulse Response ◽  
Processing Unit ◽  
Filter Noise ◽  
Fir Filter Design

Digital-signal-processing (DSP) is one of the recent emerging techniques contain more filtering operations. It may an image type or audio/ video signal processing. Each processing unit has filtering sections to filter noise elements. Hence, there is a need for efficient and secure algorithmic scheme. Here, a exhaustive scrutiny use of complex optimization algorithms towards the digital-filter construction is conferred. In appropriate, the scrutiny target on the identification of various suggestions and limitations in FIR system design. For exact representations, the infinite impulse response adaptive filters and finite impulse response models are considered for estimation. It is designed to review a various swarm and evolutionary computing structures employed for filter design schemes. Some popular computing algorithms are noticed to recover characteristics of percolate design approach. Further, compared with recent research for identifying the updating features in optimization schemes. Finally, this review suggested that the swarm intelligence based researchers improved the constraints and its attributes.

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