Infinite-impulse Filters Research Articles

Development of a Low-Cost and Effisient ECG devices with IIR Digital Filter Design


 
 
 
 Measurement of biosignals such as electrocardiograph has the interpretation of noise from other signals. The noise can interfere with the measurement of the heart signal and make the measurement inaccurate, so the purpose of this study is to make a 6-Lead Electrocardiogram module with an Arduino-Based Digital Filter. By using a digital filter. The contribution of this research is the use of digital filters to eliminate noise in electrocardiograph signals. This research uses Infinite Impulse Filter digital filters such as Butterworth, Chebyshev I, Chebyshev II, and Elliptic in order 2, 4, 6, 8, and 10. The study was conducted by providing input from the Function Generator on Arduino which has been applied digital filters with Frequency with 0.5Hz – 100Hz cut-off. The instrument is compared with a factory electrocardiograph. Filter measurements using 460 input data. Butterworth filter with the greatest emphasis on order 8 frequency 0.5Hz produces an emphasis of -5.74298158 dB and a frequency of 100Hz produces an emphasis of -5.93529424 dB. The Chebyshev I filter has the greatest emphasis on order 6 frequency 0.5Hz producing an emphasis of -3.27104076 dB and on order 8 frequency 100Hz producing an emphasis of -5.08730424 dB. Chebyshev II filter the biggest emphasis on the order of frequency 0.5Hz produces a suppression of -44,66011104 dB and 80Hz frequency produces a suppression of -37,3653957 dB. Elliptic filters the greatest emphasis on order 6 frequency 0.5Hz produces an emphasis on -1.55429354 dB and 100Hz frequency on order 8 produces an emphasis on -2.2849115 dB. The results showed that what was appropriate with the cut-off frequency was the Butterworth order 8 filter which was suitable for the application of the Electrocardiograph signal filter because it had bandwidth that suppressed the signal outside the cut-off frequency. The results of this study can be implemented on a 6-Lead ECG module to eliminate noise or interference when tapping ECG signals.
 
 
 

Filtering Sinyal Menggunakan Band Pass Filter

Sound is a signal or wave that propagates with a certain frequency and amplitude through intermediary media that are delivered such as water, air and solid objects. Humans can communicate with other humans with sound. But the sound that is released by humans, musical instruments, or other objects does not always sound clear and good, some of the recorded sound has a lot of noise which makes the sound quality is disturbed and not good. The solution for making sound in an object better and cleaner is filtering. [2]. Filters can be interpreted as a circuit that passes a certain frequency band desired and dampens other frequency bands. Filters are divided into two types, namely analog filters and digital filters. According to the impulse response the digital filter is divided into two, namely the Infinite Impulse Filter (IIR) filter and the Finite Impulse Filter (FIR) filter. In this study a filtering process will be carried out on the sound of the guitar. The filter used is the Band Pass Filter, a filter that can be used to isolate or filter certain frequencies in a particular band or frequency range.

스플라인 기어부 결함의 와전류검사 신호처리에 관한 연구

금속성부품의 자동화 생산라인 상에서 결함검사는 통상 시스템 가격이 합리적이고 고속검사가 가능한 와전류검사(ECT, eddy current testing) 기법이 많이 사용된다. 이러한 금속성 피검사체 가운데 특별히 스플라인 샤프트(spline shaft)의 스플라인 기어부(spline gear)와 같이 표면이 고르지 못한 피검사 대상에 대하여 ECT검사를 적용할 경우 주파수 분포도가 유사하면서 동시에 상대적으로 큰 표면신호로 인해 센서로부터 획득한 원신호와 결함에 의해 발생한 신호를 분리해내기가 어렵다. 이러한 스플라인 기어부의 결함신호 검출을 용이하게 하기 위해서는 주변 잡음신호에서 결함신호만을 구분해낼 수 있는 고차필터의 구현이 필수적이고 동시에 각 생산라인과 피검사체의 상황에 따라 필터의 통과대역을 조절할 수 있어야 한다. 이러한 통과대역 조절이 가능한 고차필터 구현을 위해 디지털 방식 중 하나인 IIR (infinite impulse filter) 필터에 의한 구현방안을 검토하고, 신호검출을 위해 시스템 레벨에서 설계요소들의 최적화를 통해 결함신호검출을 시도하였다. Eddy current testing (ECT) is commonly applied for the inspection of automated production lines of metallic products, because it has a high inspection speed and a reasonable price. When ECT is applied for the inspection of a metallic object having an uneven target surface, such as the spline gear of a spline shaft, it is difficult to distinguish between the original signal obtained from the sensor and the signal generated by a defect because of the relatively large surface signals having similar frequency distributions. To facilitate the detection of defect signals from the spline gear, implementation of high-order filters is essential, so that the fault signals can be distinguished from the surrounding noise signals, and simultaneously, the pass-band of the filter can be adjusted according to the status of each production line and the object to be inspected. We will examine the infinite impulse filters (IIR filters) available for implementing an advanced filter for ECT, and attempt to detect the flaw signals through optimization of system design parameters for detecting the signals at the system level.

On-line motor condition monitoring system for abnormality detection

An economical and portable on-line motor condition monitoring system based on an advanced microprocessor with a graphic user interface for abnormality detection is described. Both electrical and vibrational signal analysis for fault detection of motors were applied. A Cortex-M4 microcontroller and its built-in high speed analog-to-digital converter were used to process the on-line voltage, current, and vibration signals of the motors during operation. The acquired signals were digitally filtered through an infinite impulse filter and then, fast Fourier transform for spectral analysis was applied for abnormal pattern recognition. The analytic results were displayed in real time on an embedded touch-screen monitor to instantly provide users with the motor's operational conditions. Finally, a prototype was devised and verified through onsite experimentation. Comparing the results to commercial tools showed similar spectral characteristics; the difference for electrical and vibration signals was less than 10%. Thus, the system proposed shows promising ability and feasibility for online use.

Application of Autoregressive Moving Average Parametric Modeling in Magnetic Resonance Image Reconstruction

The modeling of data is an alternative to conventional use of the fast Fourier transform (FFT) algorithm in the reconstruction of magnetic resonance (MR) images. The application of the FFT leads to artifacts and resolution loss in the image associated with the effective window on the experimentally-truncated phase encoded MR data. The transient error modeling method treats the MR data as a subset of the transient response of an infinite impulse filter (H(z) = B(z)IA(z)). Thus, the data are approximated by a deterministic autoregressive moving average (ARMA) model. The algorithm for calculating the filter coefficients is described. It is demonstrated that using the filter coefficients to reconstruct the image removes the truncation artifacts and improves the resolution. However, determining the autoregressive (AR) portion of the ARMA filter by algorithms that minimize the forward and backward prediction errors (e.g., Burg) leads to significant image degradation. The moving average (MA) portion is determined by a computationally efficient method of solving a finite difference equation with initial values. Special features of the MR data are incorporated into the transient error model. The sensitivity to noise and the choice of the best model order are discussed. MR images formed using versions of the transient error reconstruction (TERE) method and the conventional FFT algorithm are compared using data from a phantom and a human subject. Finally, the computational requirements of the algorithm are addressed.