Inductanceless high order low frequency filters for medical applications

<p>In this paper, a designed circuit used for low-frequency filters is implemented and realized the filter is based on frequency-dependent negative resistance (FDNR) as an inductor simulator to substitute the traditional inductance, which is heavy and high cost due to the coil material manufacturing and size area. The simulator is based on an active operation amplifier or operation transconductance amplifier (OTA) that is easy to build in an integrated circuit with a minimum number of components. The third and higher-order Butterworth filter is simulated at low frequency for low pass filter to use in medical instruments and low-frequency applications. The designed circuit is compared with the traditional proportional integral controller enhanced (PIE) and T section ordinary filter. The results with magnitude and phase response were compared and an acceptable result is obtained. The filter can be used for general applications such as medical and other low-frequency filters needed.</p>

The voltage-mode first order universal filter using single voltage differencing differential input buffered amplifier with electronic controllability

In this research contribution, the electronically tunable first-order universal filter employing a single voltage differencing differential input buffered amplifier (VD-DIBA) (constructed from two commercially available integrated circuit (IC): the operational transconductance amplifier, IC number LT1228, and the differential voltage input buffer, IC number AD830), one capacitor and two resistors. The features of the designed first order universal filter are as follows. Three voltage-mode first-order functions, low-pass (LP), all-pass (AP) and high-pass (HP) responses are given. The natural frequency (𝜔0) of the presented configuration can be electronically adjusted by setting the DC bias current. Moreover, the voltage gain of the LP and HP filters can be controllable. The phase responses of an AP configuration can be varied from 00 to −1800 and 1800 to 00. The power supply voltages were set at ±5 𝑉. Verification of the theoretically described performances of the introduced electronically tunable universal filter was proved by the PSpice simulation and experiment.

Power quality event classification using complex wavelets phasor models and customized convolution neural network

Origin and triggers of power quality (PQ) events must be identified in prior, in order to take preventive steps to enhance power quality. However it is important to identify, localize and classify the PQ events to determine the causes and origins of PQ disturbances. In this paper a novel algorithm is presented to classify voltage variations into six different PQ events considering the space phasor model (SPM) diagrams, dual tree complex wavelet transforms (DTCWT) sub bands and the convolution neural network (CNN) model. The input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands which are simultaneously processed by the 2D CNN model to perform classification of PQ events. In the proposed method CNN model based on Google Net is trained to perform classification of PQ events with default configuration as in deep neural network designer in MATLAB environment. The proposed algorithm achieve higher accuracy with reduced training time in classification of events than compared with reported PQ event classification methods.

Compact transition enabled broadband propagation of spoof surface plasmon polaritons based on the equivalent circuit model

In this paper, a strategy to develop a compact transition of the spoof surface plasmon polariton (SSPP) transmission line (TL) is proposed. First, an equivalent distributed circuit model is employed for the theoretical analysis and optimization design of the SSPP unit. The mapping relation between the unit performance and the geometric parameters is deduced from the transmission matrix. The calculated results are compared with the numerical ones from the three-dimensional (3D) simulations for validation. Then, a compact transition (only 0.26λg) is built with only two matching units and a tapered strip through optimizations. The optimizations are implemented with the circuit simulations based on the equivalent model, which can remarkably save time in comparison with the 3D simulations. The transition principle is also explained by quantitatively extracting the dispersion properties and impedance characteristics. Finally, a prototype of the proposed SSPP TL is fabricated and measured for demonstration. The measured operating band (0-7.7 GHz) is almost up to the cut-off frequency (about 8 GHz), which remains the inherent broadband low-pass transmission characteristics. Meanwhile, the measured in-band return loss is almost higher than 10dB, which verifies the high-efficiency propagation. This work can pave the way for building up a new SSPP-based framework of microwave circuits.

Theoretical and Empirical Verification of Electrical Impedance Matching Method for High-Power Transducers

In our prior study, a systematic approach was used to devise Langevin transducers for high-power applications where the energy efficiency was not considered in the design criteria. In this paper, the impedance matching methods are thus proposed to evaluate what matching topology is appropriate for their use. Both the series inductor scheme and low pass filter composed of a series inductor and shunt capacitor are examined as matching circuits. According to MATLAB simulation, the resonance frequency is seen at 36.79 kHz due to a series L circuit, and its associated impedance is reduced by 70.45% from that of its non-matching condition. The measured resonance frequency is 36.77 kHz and the corresponding impedance is decreased by 59.52%. Furthermore, the acoustic pressure is measured to determine the effect of the matching circuit on the transducer’s actual behavior. The transducer with a series L circuit shows more efficient matching results, 2.28 kPa of positive acoustic pressure is emitted without matching and 3.35 kPa is emitted with a series L element, respectively. As a result, this study demonstrates how to evaluate the influence of matching circuits by using our customized approach rather than commercial SPICE programs, as well as how to experimentally verify the acoustic behavior of high-power Langevin transducers.

Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces

<div> <div> <div> <p>In this research work, deep machine learning based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous Reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low- pass filters and prediction of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low- pass Butterworth filter is used together with a linear FIR based prediction or long short-term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with a LSTM network is used to filter and predict at the same time. For a 40 ms prediction horizon, the proposed methods provide 2.06%, 0.31%, 0.99% prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5%, 1.90%, and 3.2% reconstruction errors for the above-mentioned methods. Finally, a Simulink and GPU based implementation of predictive APF using Butterworth filter + LSTM and a trivial APF resulted 96% and 60% efficiency on compensation of EAF current interharmonics. </p> </div> </div> </div>

Wave Propagations in Nonlinear Low-Pass Electrical Transmission Lines through Optical Fiber Medium

The present article discovers the new soliton wave solutions and their propagation in nonlinear low-pass electrical transmission lines (NLETLs). Based on an innovative Exp-function method, multitype soliton solutions of nonlinear fractional evolution equations of NLETLs are established. The equation is reformulated to a fractional-order derivative by using the Jumarie operator. Some new results are also presented graphically to understand the real physical importance of the studied model equation. The physical interpretation of waves is represented in the form of three-dimensional and contour graphs to visualize the underlying dynamic behavior of these solutions for particular values of the parameters. Moreover, the attained outcomes are generally new for the considered model equation, and the results show that the used method is efficient, direct, and concise which can be used in more complex phenomena.

Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces

<div> <div> <div> <p>In this research work, deep machine learning based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous Reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low- pass filters and prediction of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low- pass Butterworth filter is used together with a linear FIR based prediction or long short-term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with a LSTM network is used to filter and predict at the same time. For a 40 ms prediction horizon, the proposed methods provide 2.06%, 0.31%, 0.99% prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5%, 1.90%, and 3.2% reconstruction errors for the above-mentioned methods. Finally, a Simulink and GPU based implementation of predictive APF using Butterworth filter + LSTM and a trivial APF resulted 96% and 60% efficiency on compensation of EAF current interharmonics. </p> </div> </div> </div>

Fault Detection of Planetary Gears Based on Signal Space Constellations

A new method to process the vibration signal acquired by an accelerometer placed in a planetary gearbox housing is proposed, which is useful to detect potential faults. The method is based on the phenomenological model and consists of the projection of the healthy vibration signals onto an orthonormal basis. Low pass components representation and Gram–Schmidt’s method are conveniently used to obtain such a basis. Thus, the measured signals can be represented by a set of scalars that provide information on the gear state. If these scalars are within a predefined range, then the gear can be diagnosed as correct; in the opposite case, it will require further evaluation. The method is validated using measured vibration signals obtained from a laboratory test bench.

Does precipitation pattern foretell climatic shift over Punjab state ?

ABSTRACT. Statistical analysis of 82-years (1901-1982) record of precipitation from 27 rain-recording stations in Punjab state of lndia has been carried out to assess the climate shift if any in the state. The central part of the study is the trend and spectrum analysis of annual. monsoon and winter rainfall of different stations in the region. It is seen that frequency distribution of 19 rainfall series out of 81 series is normally distributed. Maikov linear type of persistence is observed in some of the rainfall series. Marin-Kendall test indicates the decreasing trend in winter rainfall of all the stations and is found to be significant in case of Amritsar, Taran Taran, Tanda, Ludhiana and Ranike. Low-pass filter reveals that trend is not linear but oscillatory consisting of periods of 10 years or more. It is seen that winter rainfall of most of the stations exhibits the decreasing trend from 1935-40 to 1965-70. It is also revealed by the low-pass filter curves that winter rainfall of all t1le sla1ions remained below average from 1960 till the end of the study period. The spectral analysis indicates a significant cycle of 4.1 to 27 years in some of the stations and Quasi-Biennial Oscillations (QBO) over many stations.