Butterworth filter

About: Butterworth filter is a research topic. Over the lifetime, 6187 publications have been published within this topic receiving 69070 citations.

Optimized low-insertion-loss millimetre-wave fin-line and metal insert filters

Abstract: Low passband insertion-loss is achieved (1) by large-gap fin-lines, by which the high-Q potential increasing with gap-width is fully utilized, and (2) by pure metal inserts mounted in the E-plane of rectangular waveguides requiring no supporting dielectrics. This design combines the advantages of low-cost etching techniques and the low-loss performance of usual waveguide circuits. The theory described includes both higher-order mode interaction of the discontinuities and the finite thickness of dielectrics, metal fins as well as inserts. An optimizing computer program varies the filter parameters for a given number of resonators until the insertion loss yields a minimum in passband and an optimum in stopband. Data for optimized X-, Ka-, V-, E-, and W-band filters are given. Measurements verify the described theory. Measured minimum pass-band insertion losses are 0.3, 0.7, 1.5 dB for the fin-line filter for midband frequencies of about 12, 34, 75 GHz, and for the metal insert filter 0.1, 0.6, 0.5, and 0.7 dB at 12, 33, 63, and 76 GHz, respectively.

Device and method for adjusting a bandpass filter, especially a combiner filter

Abstract: The invention relates to a frequency adjustment method for an RF bandpass filter, especially a combiner filter, wherein the center frequency of the passband of the RF bandpass filter (22) is adjusted in response to the RF power reflected from the input of the bandpass filter or passed through the bandpass filter. In the method a sample signal (Pr) proportional to the RF power reflected from the input of the bandpass filter or passed through the bandpass filter is mixed (29) with a signal of substantially the transmitting frequency, the mixing result is lowpass-filtered (24) and the bandpass filter (22) is adjusted in response to the lowpass-filtered mixing result (24a).

Tunable and bandwidth programmable multi-element filter system

Abstract: A method and apparatus which realize an RF bandpass filter having a tunable bandwidth and center frequency over a large range of the radio frequency spectrum. The filter is comprised of a cascade of filter elements. Each element is comprised of a signal splitter which simultaneously feeds a signal to a delay line and a phase shifter in a parallel path. The delayed and phase shifted signals are summed. Each filter element exhibits periodic passbands and alternating transmission zeroes. Each successive filter element has a decreased delay by an incremental factor (e.g., typically 1.2), and exhibits an increase in the frequency intervals between stopband zeroes. The bandwidth is controlled by selecting a number of "activated" ones of the successive elements. A center frequency tuning is performed by adjusting a phase shift coefficient of at least one filter element. Such adjustments are performed by applying a test signal at each desired stopband zero frequency and adjusting the phase shift quantity of the element that is required to create the desired transmission zero. The residual power at the test frequency and transmission zero is measured at the filter output at the test signal frequency and the element's phase shifter is adjusted until a minimum output power is observed. Per the continuous sequential applications of the test signal at each of the stopband zero frequencies, and readjustments of each of the corresponding phase shifts, the filter parameters can be maintained as desired. The test sequencing and parameter controls are performed by digital computations.

IIR based digital filter design and performance analysis

Abstract: Digital filters are mandatory for digital signal processing. This paper presents digital filter dispelling the unwanted signals or noise from the required signal and enhances the better performances of the signal. The extracted features of the digital filter have been analyzed to acquire the better output of the signal by using IIR Butterworth filter. It provides different designed parameters of IIR filter to achieve the desired result. MATLAB FDA tool is considered to find out the different responses of a digital filter. About eight parameters like-Phase response, Magnitude response, Magnitude & Phase response, Step response, Group delay, Pole/Zero Plot, Phase delay, Impulse response are used to analyze the filter responses. Some selected audio signals are used for observing the empirical response of high pass, low pass, band stop filter and band pass filter. A special tool is developed for this observation purpose.

An Integrated Machine Learning Algorithm for Separating the Long-Term Deflection Data of Prestressed Concrete Bridges

Abstract: Deflection is one of the key indexes for the safety evaluation of bridge structures. In reality, due to the changing operational and environmental conditions, the deflection signals measured by structural health monitoring systems are greatly affected. These ambient changes in the system often cover subtle changes in the vibration signals caused by damage to the system. The deflection signals of prestressed concrete (PC) bridges are regarded as the superposition of different effects, including concrete shrinkage, creep, prestress loss, material deterioration, temperature effects, and live load effects. According to multiscale analysis theory of the long-term deflection signal, in this paper, an integrated machine learning algorithm that combines a Butterworth filter, ensemble empirical mode decomposition (EEMD), principle component analysis (PCA), and fast independent component analysis (FastICA) is proposed for separating the individual deflection components from a measured single channel deflection signal. The proposed algorithm consists of four stages: (1) the live load effect, which is a high-frequency signal, is separated from the raw signal by a Butterworth filter; (2) the EEMD algorithm is used to extract the intrinsic mode function (IMF) components; (3) these IMFs are utilized as input in the PCA model and some uncorrelated and dominant basis components are extracted; and (4) FastICA is applied to derive the independent deflection component. The simulated results show that each individual deflection component can be successfully separated when the noise level is under 10%. Verified by a practical application, the algorithm is feasible for extracting the structural deflection (including concrete shrinkage, creep, and prestress loss) only caused by structural damage or material deterioration.