scholarly journals Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1468
Author(s):  
Xiang An ◽  
George K. Stylios
Keyword(s):  
Impulse Response ◽  
Adaptive Filter ◽  
Finite Impulse Response ◽  
Median Filter ◽  
Infinite Impulse Response ◽  
Ecg Signal ◽  
Motion Artefact ◽  
Iir Filter ◽  
Adaptive Motion ◽  
Artefact Reduction

A motion artefact is a kind of noise that exists widely in wearable electrocardiogram (ECG) monitoring. Reducing motion artefact is challenging in ECG signal preprocessing because the spectrum of motion artefact usually overlaps with the very important spectral components of the ECG signal. In this paper, the performance of the finite impulse response (FIR) filter, infinite impulse response (IIR) filter, moving average filter, moving median filter, wavelet transform, empirical mode decomposition, and adaptive filter in motion artefact reduction is studied and compared. The results of this study demonstrate that the adaptive filter performs better than other denoising methods, especially in dealing with the abnormal ECG signal which is measured from a patient with heart disease. In the implementation of adaptive motion artefact reduction, the results show that the use of the impedance pneumography signal as the reference input signal for the adaptive filter can effectively reduce the motion artefact in the ECG signal.

FPGA-based Implementation of IIR Filter for Real-Time Noise Reduction in Signal

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Aladin Kapić ◽  
Rijad Sarić ◽  
Slobodan Lubura ◽  
Dejan Jokić
Keyword(s):  
Transfer Function ◽  
Impulse Response ◽  
Discrete Time ◽  
Time Domain ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Main Role ◽  
Digital Implementation ◽  
Iir Filter

Filtering of unwanted frequencies represents the main aspect of digital signal processing (DSP) in any modern communication system. The main role of the filter is to perform attenuation of certain frequencies and pass only frequencies of interest. In a DSP system, sampled or discrete-time signals are processed by digital filters using different mathematical operations. Digital filters are commonly categorized as Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). This research focuses on the full VHDL implementation of digital second-order lowpass IIR filter for reducing the noisy frequencies on the FPGA board. The initial step is to determine, from continuous time domain function, the transfer function in the complex {s} domain, then map transfer function in complex {z} domain and finally calculate the difference equation in discrete-time domain of the system with adequate coefficients. Prior to the FPGA implementation, the IIR filter is tested in MATLAB using a signal with mixed frequencies and signal with randomly generated noise. The digital implementation is completed by using fixed-point binary vectors and clocked processes.

Research of Sea Clutter Suppression Algorithm Based on GPU

2012 ◽  
Vol 239-240 ◽  
pp. 1194-1201
Author(s):  
Yan Guo ◽  
Shi Dan Li ◽  
De Sheng Wang
Keyword(s):  
Impulse Response ◽  
Finite Impulse Response ◽  
Graphics Processing Unit ◽  
Main Idea ◽  
Fir Filter ◽  
Infinite Impulse Response ◽  
Processing Unit ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
Iir Filter

This paper presents an algorithm of sea clutter suppression using graphics processing unit (GPU) to meet the real-time requirement in the general radar terminal system. The main idea is to convert an infinite impulse response (IIR) filter to a finite impulse response (FIR) filter, which is suitable for the parallelization processing of GPU. Finally, the converted FIR filter algorithm is implemented on the GPU efficiently, achieving a speed approximately twice as fast as that of the previous IIR filter algorithm implemented on the CPU.

scholarly journals A Phasor Measurement Unit Algorithm Using IIR Filters for FPGA Implementation

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1523
Author(s):  
Cornelis Jan Kikkert
Keyword(s):  
Impulse Response ◽  
Finite Impulse Response ◽  
Block Diagram ◽  
Rate Of Change ◽  
Measurement Unit ◽  
Infinite Impulse Response ◽  
Iir Filters ◽  
Iir Filter ◽  
Phasor Measurement ◽  
Ieee Standard

Phasor measurement units (PMU) are increasingly used in electrical power transmission networks, to maintain stability and protect the network. PMUs accurately measure voltage, phase, frequency, and rate of change of frequency (ROCOF). For reliability, it is desirable to implement a PMU using an FPGA. This paper describes a novel algorithm, suited to implementation in an FPGA and based on a simple PMU block diagram. A description of its realization using low hardware complexity infinite impulse response (IIR) filters is given. The IEC/IEEE standard 60255-118-1:2018 Part 118-1: Synchrophasor measurements for power systems, describes “reference” Finite Impulse Response (FIR) filters for implementing PMU hardware. At the 10 kHz sampling frequency used for our implementation, each “reference” FIR filter requires 100 multipliers, while an 8th order IIR filter only requires 12 multipliers. This paper compares the performance of different order IIR filter-based PMUs with the performance of the same PMU algorithm using the IEC/IEEE FIR reference filter. The IIR-based PMU easily satisfies all the requirements of IEC/IEEE standard and has a much better out of band signal rejection performance than a FIR-based PMU. Steady state errors for a rated voltage ± 10% and a rated frequency ± 5 Hz are < 0.000001% for total vector error (TVE) and < 1 µHz for frequency, with a latency of two mains cycles.

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 OPEN CORES FOR DIGITAL SIGNAL PROCESSING

2014 ◽  
Vol 25 (1) ◽  
pp. 53-62
Author(s):  
Juan Camilo Valderrama-Cuervo ◽  
Alexander López-Parrado
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Fir Filter ◽  
Hardware Verification ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
On Chip

This paper presents the design and implementation of three System-on-Chip (SoC) cores, which implement the Digital Signal Processing (DSP) functions: Finite Impulse Response (FIR) filter, Infinite Impulse Response (IIR) filter and Fast Fourier Transform (FFT). The FIR-filter core is based on the symmetrical realization form, the IIRfilter core is based on the Second Order Sections (SOS) architecture and the FFT core is based on the Radix 22 Single Delay Feedback (R22SDF) architecture. The three cores are compatible with the Wishbone SoC bus, and they were described using generic and structural VHDL. In-system hardware verification was performed by using an OpenRisc-based SoC synthesized on an Altera FPGA. Tests showed that the designed DSP cores are suitable for building SoC based on the OpenRisc processor and the Wishbone bus.

scholarly journals Analisis Aplikasi Filter FIR dan Filter IIR dalam Pra-pemrosesan Sinyal Elektroensefalografi

2020 ◽  
Vol 12 (1) ◽  
pp. 40-48
Author(s):  
Caroline Caroline ◽  
Nabila Husna Shabrina ◽  
Melania Regina Ao ◽  
Nadya Laurencya ◽  
Vanessa Lee
Keyword(s):  
Impulse Response ◽  
Wavelet Decomposition ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Amplitude Ratio ◽  
Processing Parameters ◽  
Infinite Impulse Response ◽  
Eeg Signal ◽  
Iir Filter ◽  
Signal Preprocessing

Abstract – Electroencephalography (EEG) is a method used to analyze brain activities, detect abnormalities in brain, and diagnose brain-related disease. To extract information from EEG signal, preprocessing steps such as Fast Fourier Transform (FFT), filter, and wavelet decomposition will be needed. This paper primarily focuses on implementation of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filter design in EEG signal preprocessing in MATLAB software. The result of the simulation indicates that each filter design implemented in EEG preprocessing has different performance and side effect toward signal processing parameters such as phase distortion, amplitude ratio, and processing time. Filter design type implementation also affect power and entropy calculation result. Keywords – EEG, FIR filter digital, IIR filter digital, Wavelet Decomposition, GUI-MATLAB

Recursive Identification of Wiener-Hammerstein Systems with Nonparametric Nonlinearity

2013 ◽  
Vol 3 (4) ◽  
pp. 311-332 ◽  
Author(s):  
Xiao-Li Hu ◽  
Yue-Ping Jiang
Keyword(s):  
Impulse Response ◽  
Finite Impulse Response ◽  
Structural Information ◽  
Nonlinear Function ◽  
Recursive Identification ◽  
Infinite Impulse Response ◽  
Hammerstein System ◽  
Single Input Single Output ◽  
Iir Filter ◽  
Static Nonlinearity

AbstractA recursive scheme is proposed for identifying a single input single output (SISO) Wiener-Hammerstein system, which consists of two linear dynamic subsystems and a sandwiched nonparametric static nonlinearity. The first linear block is assumed to be a finite impulse response (FIR) filter and the second an infinite impulse response (IIR) filter. By letting the input be a sequence of mutually independent Gaussian random variables, the recursive estimates for coefficients of the two linear blocks and the value of the static nonlinear function at any fixed given point are proven to converge to the true values, with probability one as the data size tends to infinity. The static nonlinearity is identified in a nonparametric way and no structural information is directly used. A numerical example is presented that illustrates the theoretical results.

scholarly journals High performance IIR filter implementation on FPGA

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Debarshi Datta ◽  
Himadri Sekhar Dutta
Keyword(s):  
Impulse Response ◽  
High Performance ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
Look Ahead ◽  
Parallel Pipeline ◽  
Field Programmable ◽  
Hardware Description ◽  
Improved Design

AbstractThis paper presents an improved design of reconfigurable infinite impulse response (IIR) filter that can be widely used in real-time applications. The proposed IIR design is realized by parallel–pipeline-based finite impulse response (FIR) filter. The FIR filters have excellent characteristics such as high stability, linear phase response and fewer finite precision errors. Hence, FIR-based IIR design is more attractive and selective in signal processing. In addition, the other two modern techniques such as look-ahead and two-level pipeline IIR filter designs are also discussed. All the said designs have been described in hardware description language and tested on Xilinx Virtex-5 field programmable gate array board. The implementation results show that the proposed FIR-based IIR design yields better performance in terms of hardware utilization, higher operating speed and lower power consumption compared to conventional IIR filter.

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