Elliptic Filter Research Articles

MmWave-RM: A Respiration Monitoring and Pattern Classification System Based on mmWave Radar.

Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring.

FPGA implementation of IIR elliptic filters for de-noising ECG signal

De-noising of ECG signal is very much necessary to monitor the heart health and to diagnose disease. This paper presents a real time de-noising system that efficiently wiped out the noises from corrupted ECG signal and improves its SNR which enhances the interpretation of features present in ECG signal. This proposed system is designed with three Infinite Impulse Response (IIR) elliptic filters in cascaded direct form II structure and is implemented on the FPGA platform. Total 376 ECG samples are taken from the open access public database physiobank. All the architectural level FPGA designs have been developed in Xilinx System Generator (XSG) and implemented on Xilinx Zynq 7000 board. Our design utilized only 3.87 % of the total available resource and consumed 0.321 watt on-chip power. Worst Negative Slack (WNS) for the critical path is 2.101 ns. The positive value of WNS signifies our hardware design implementation run successfully by meeting the required time constraint. To judge the performance of the proposed design, the measurement matrices SNR Improvement, RMSE, PRD, SSNR and SINAD are calculated and achieved 81.11% , 88.89% and 85.41% accuracy for ECG recordings of ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia database respectively. The novelty of this proposed denoising system is its robustness, which work satisfactorily for Baseline Wander (BW), Electrode Motion (EM), Power Line Interference (PLI), Muscle or Electromyography (EMG) and as well as Additive White Gaussian Noise (AWGN).

Estimation of phase distortions of the photoplethysmographic signal in digital IIR filtering

Pre-processing of the photoplethysmography (PPG) signal plays an important role in the analysis of the pulse wave signal. The task of pre-processing is to remove noise from the PPG signal, as well as to transmit the signal without any distortions for further analysis. The integrity of the pulse waveform is essential since many cardiovascular parameters are calculated from it using morphological analysis. Digital filters with infinite impulse response (IIR) are widely used in the processing of PPG signals. However, such filters tend to change the pulse waveform. The aim of this work is to quantify the PPG signal distortions that occur during IIR filtering in order to select a most suitable filter and its parameters. To do this, we collected raw finger PPG signals from 20 healthy volunteers and processed them by 5 main digital IIR filters (Butterworth, Bessel, Elliptic, Chebyshev type I and type II) with varying parameters. The upper cutoff frequency varied from 2 to 10 Hz and the filter order—from 2nd to 6th. To assess distortions of the pulse waveform, we used the following indices: skewness signal quality index (SSQI), reflection index (RI) and ejection time compensated (ETc). It was found that a decrease in the upper cutoff frequency leads to damping of the dicrotic notch and a phase shift of the pulse wave signal. The minimal distortions of a PPG signal are observed when using Butterworth, Bessel and Elliptic filters of the 2nd order. Therefore, we can recommend these filters for use in applications aimed at morphological analysis of finger PPG waveforms of healthy subjects.

Design of a Microwave Quadrature Hybrid Coupler with Harmonic Suppression Using Artificial Neural Networks

In this paper, a compact and simple structure of an elliptic microstrip lowpass filter (LPF) is designed for harmonic suppression in microwave quadrature hybrid coupler (QHC) applications. A radial resonator and a rectangular resonator are used to produce an elliptic LPF. The proposed LPF is used on the outer sides of the branch line coupler, which has improved the coupler harmonic suppression. Furthermore, artificial neural networks (ANNs) are incorporated to improve the LPF design process. The LPF best structure is obtained using the proposed ANN model. The proposed LPF has a compact size, which only occupies 16.4 mm × 7.3 mm equals to 0.164 λg × 0.073 λg, has a cut frequency of 2.2 GHz, and shows a sharp transmission band with a roll-off rate of 158.3 dB/GHz. Finally, the deigned QHC operates correctly at 1 GHz, which shows high harmonic suppression ability. The proposed QHC provides wide suppression band from 2.25 GHz up to more than 14 GHz, which can effectively suppress 3rd, to 14th harmonics. The proposed coupler features desirable parameters of S11, S21, S31, and S41, with magnitude of −21 dB, −3.4 dB, −3.3 dB, and −22.5 dB, at the operating frequency. The proposed approach mitigates the complexity of the circuit fabrication, compared with the previous methods while achieved desirable performances for the proposed QHC.

Reconfigurable integrated photonic filters for optical signal processing using a silicon photonic platform.

We present a systematic photonic filter design approach by deploying pole-zero optimization. The filter transfer function is derived from its specifications by formulating closed-form optimization objective functions and subsequently translating them into optical design parameters. Two distinct filter examples, namely Chebyshev and elliptic filters, are considered for the design and validation. A compact reconfigurable three-pole photonic filter is fabricated on a silicon photonic platform to illustrate the proposed design technique including transmission tunability. Integrated thermal phase shifters coupled with micro-ring resonators are used to reconfigure filter responses. A well-matched experimental demonstration is presented to validate the proposed tuning method. We achieved a sharp out-of-band edge rejection of at least 20 and 40 dB for the elliptic and Chebyshev filter, respectively.

Enhancing power quality in vehicle-to-grid (V2G) operations of FCEVs through the integration of real-time digital IIR filters in power calculations

Fuel cell electric vehicles (FCEVs) are estimated as the future's mobile distributed generators with vehicle-to-grid (V2G) functions. Besides, V2G topologies are required to be designed with an appropriate analog filter by accurately measuring the reference current and voltage values of inverters and converters. Thus, the highest accuracy is required in control structures for charging and grid connection of FCEVs to improve the power quality (PQ). In this study, PQ improvement of V2G operation of FCEVs is proposed by considering the developed real-time digital infinite impulse response (IIR) filters in generating reference signals in the power calculation stage. A band-pass digital IIR filter design is realized with the inverter using LabVIEW infrastructure, and reference signals are smoothed by using IIR filters. The performances of developed different IIR filters' results are examined in the study. Developed filters are designed using the order estimation method. Elliptic, Chebyshev, and Butterworth filters are applied to the voltage and current signals, and the best results are obtained in reference power signals with the Butterworth filter. The Butterworth filter maintains stability in current RMS, induces a 0.09 % decrease in voltage RMS, and shows a slight 0.30 % increase in active power. The proposed approach removes distortions in reference power signals by using a designed filter for a reliable V2G operation of FCEVs.

Design of multi-ensemble hybrid filtering approach for removal of heart sound from lung auscultations

Successful lung sound extraction from lung auscultations requires precise filters. Lung auscultations include heart and lung sounds. Therefore, the filter design should identify heart sound characteristics. These include periodicity, pitch, frequency, amplitude, etc. Auscultation acquisition sensors are inefficient, so these signals have noise. To get a clean auscultation signal, ambient noise, periodic noise, DC offsets, baseline errors, harmonic noise, etc. must be addressed separately. Data cleaning must maintain a high peak-signal-to-noise ratio without removing desirable signal information. Heart sound patterns are extracted from input auscultations using filter banks on these signals. After heart sound extraction, the output sound sample should only contain lung sounds. Still, heart sound harmonics corrupt output lung sound due to similarities between heart and lung sound patterns. This lowers lung-sound extraction quality and automatic lung-based disease identification efficiency levels. This article proposes a 2-level multi-ensemble filtering model with 43 filters that analyze 15 other parameters to denoise and extract lung sound from lung auscultations to eliminate these drawbacks. Combining LMS, NLMS, and RLS is used for denoising process. For heart sound identification, an ensemble of Savitzky-Golay, FIR equiripple, Butterworth, Chebyshev, Elliptic, and wavelet filters is used. This selective combination of filters improves PSNR by 20% compared to sole filter performance, while signal entropy, crest factor, root mean squared error, kurtosis, etc. also improves for different scenarios.

A Detailed Study on IIR-FIR Filters and Design of a Graphical User Interface for Simulation of EEG Signals

Electroencephalography (EEG) plays a pivotal role in accepting brain activity and identifying neurological disorders. The precise analysis and explanation of EEG signals require the solicitation of digital signal processing techniques, such as Infinite Impulse Response (IIR) as well as Finite IJmpulse Response (FIR) filters. This paper investigates into a complete exploration of IIR-FIR filters and their application in EEG signal processing. The first part of this research paper involves a detailed examination of IIR and FIR filter types, their mathematical foundations, pros, and cons. Various filter design methods, such as Chebyshev, Butterworth and Elliptic filters, are discussed as well as compared through literature survey. Speculative aspects, including filter design, transfer functions, and frequency responses, are presented in a clear and much accessible manner. The second phase of the study introduces the design of a Graphical User Interface (GUI) on mathematical modelling tool aimed at enabling EEG signal simulation and analysis. This GUI is designed to enable users, including researchers and clinicians, to generate synthetic EEG signals with controllable parameters, apply IIR-FIR filters in real-time, and thereby visualize the filtered signals. The interface offers user-friendly controls for customizing filter characteristics, such as filter order, cutoff frequencies, and filter type. To validate the efficiency of the designed GUI and the selected IIR-FIR filters, general simulations are conducted using EEG datasets. The results showcase the GUI's efficacy in real-time EEG signal processing, demonstrating its prospective in research, clinical diagnostics, and educational settings and many areas. In summary, this paper presents a full investigation into IIR-FIR filters, proposing insights into their theory and practical application for EEG signal processing. The development of a intelligible GUI for EEG signal simulation and study further enhances the approachability of these mathematical modelling tools to a wider audience, eventually contributing to progressions in the field of neuroscience and brain signal analysis.

A Low-Power 30MHz ,6th Order Band-pass Differential Gm-C Filter on Chip Using Floating Current Source

A 30MHz center frequency, 6th order Butterworth and elliptic band-pass filter using a leap-frog Gm-C structure in this work. A continuous time differential voltage mode Gm-C biquad and its adjoin voltage - mode version are described, employing FCS (Floating Current Source) circuits as building blocks. The improved current source structure is simple and includes fewer transistors. This provides an effective use of the chip area and brings simplicity to the design of circuits. The proposed filter structure does not contain the resistor which is very important for integration. All capacitors in proposed filter are grounded which reduces the parasitic effects. The proposed filter has been successfully implemented by TSMC 0.18μm CMOS process. Simulation results are given to confirm the theoretical analysis.

Polarization-stable single-mode multi-junction VCSELs with integrated anti-phase mode filter

In this paper, polarization-stable and spectrally single-mode operation of oxide-confined multi-junction vertical-cavity surface-emitting lasers (VCSELs) are achieved by using a non-circular integrated anti-phase transverse mode filter. A record-high single-mode output power is obtained by radially reducing effective mirror reflectivity at the surface areas where the higher order modes are expected to lase, leading to an increase in the threshold gain for these transverse modes, all the while minimal losses are introduced to the fundamental mode. The polarization control of such devices is achieved by using a non-circular mode filter in order to perturb the cylindrical symmetry of the cavity. An elliptically etched surface relief is shown to be effective in realizing a single-mode single-polarization device over a wide range of operating bias currents and temperatures. The continuous-wave light–current–voltage characteristics of 8-junction 940 nm VCSELs show single-mode output power >13 mW at room temperature, spectrally measured with a side-mode suppression ratio of >30 dB. This single-mode performance can be achieved depending on the ratio between the relief opening and the oxide aperture size. Additionally, the single-polarization behavior of such devices (with an extinction ratio of >15 dB) is shown to be dependent on the orientation of the elliptical mode filter, where the light is mainly polarized along the minor axis of the elliptical feature. Overall, combined with a multi-junction active region, the elliptical model filter is able to achieve highly efficient single-mode linearly polarized VCSELs.

Stepped Impedance Optimum Short-Circuited Stub 1–6 GHz and 6–10 GHz Bandpass Filters for Multi-standard Radio

This article presents two ultra-broadband bandpass filter (BPF) designs for multi-standard applications, operating from 1–6 GHz and 6–10 GHz, with wide stopband characteristics. 1–6 GHz BPF (Filter 1) is based on the circuit model for an optimum distributed bandpass filter consisting of 3 shunt short-circuited stubs separated by non-redundant unit elements. By varying the length ratio between the unit elements and stubs and introducing stepped impedance in the unit elements, a 143% fractional bandwidth (FBW) is achieved, which is 20% higher than that obtained through the conventional optimum shunt short-circuited stub method. In addition, a stepped impedance elliptic lowpass filter was cascaded to achieve wide stopband characteristics from 7 GHz to 20 GHz. Filter 2 (6–10 GHz) is constructed by cascading a shunt short-circuited stub optimum high pass filter having a stepped impedance unit element with a stepped impedance low pass filter. The analytical model, transmission line equivalent circuit model, and full wave electromagnetic simulated model are presented along with the measured results. The measured in-band return loss of Filter 1 and Filter 2 is >12 dB, and the measured insertion loss is < 2 dB. The measured group delay of Filter 1 and Filter 2 is <0.8 ns.

Fast Synthesis of Elliptic Prototype Filters

Fast Synthesis of Elliptic Prototype Filters

Wide-Passband Reconfigurable Frequency Selective Rasorber Design Based on Fluidity of EGaIn

In this letter, we propose a wide-passband reconfigurable frequency selective rasorber (FSR) design based on the fluidity of liquid metal. To achieve wide-passband reconfigurability, a new type of reconfigurable frequency selective surface with wide-passband elliptic filter response is introduced for the first time, whose bandpass state can be switched to a perfect reflection state by controlling the injection and the extraction of liquid metal in microchannels. Furthermore, a new resistive film loop with a minimum insertion loss of 0.23 dB is presented by loading four circular spiral resonators. Finally, the FSR can realize the reconstruction of the absorption-transmission-absorption (ATA) and absorption-reflection-absorption (ARA) modes. The simulation results show that in ATA mode, the design exhibits a wide passband with a fractional 3 dB bandwidth reaching up to 42.8%, and a passband minimum insertion loss of 0.86 dB at 13 GHz. In ARA mode, the bandwidth for return loss less than 3 dB is 12.8%. Prototypes were fabricated and measured. Compared with previous FSRs, our proposed FSR has advantages such as reconfigurable characteristics, a wide transmission band, and high-frequency selectivity.

Development of variable 2D FIR filter structures using farrow approximation and row-wise polyphase decomposition

This work proposes pre and post Farrow approximation of row-wise polyphase decomposed 2D finite impulse response (FIR) filters to achieve variability in 2D bandwidths with low implementation costs. The proposed method starts with realizing variable 2D filters using the 2D Farrow structure. The 2D Farrow filter structure is then row-wise polyphase decomposed and approximated in terms of fractional delay again to obtain the final set of coefficients. To illustrate the efficiency of the proposed methodology, design examples of variable circular and elliptical filters are provided in this paper. The simulation results show that the proposed methodology can result in variable 2D FIR filters with a considerable reduction in the number of multipliers w.r.t the existing approaches without much degradation in the performance parameters.

Movie recommendation system via fuzzy decision making based dual deep neural networks

The internet and social networks produce an increasing amount of data. There is a serious necessity for a recommendation system because exploring through the huge collection is time-consuming and difficult. In this study, a multi-modal classifier is introduced which makes use of the output from dual deep neural networks: GRU for text analysis and Faster R-CNN for image analysis. These two networks reduce overall complexity with minimal computational time while retaining accuracy. More precisely, the GRU network is utilized to process movie reviews and the Faster RCNN is used to recognize each frames of the movie trailers. Gated Recurrent Unit (GRU) is a well-known variety of RNN that computes sequential data across recurrent structures. Faster RCNN is an enhanced version of Fast RCNN, it combines with the rectangular region proposals and with the features is extract by the ResNet-101. Initially, the trailer of the movie is manually splitted into frames and these frames are pre-processed using fuzzy elliptical filter for image analysis and the movie reviews are also tokenized for text analysis. The pre-processed text is taken as an input for GRU to classify offensive and non-offensive movies and the pre-processed images are taken as an input for Faster R-CNN to classify violence and non- violence movies based on the extracted features from the movie trailer. Afterwards, the four classified outputs are given as input for fuzzy decision-making unit for recommending best movies based on the Mamdani fuzzy inference system with gauss membership functions. The performance of the dual deep neural networks was evaluated using the specific parameters like specificity, precision, recall, accuracy and F1 score measures. The proposed GRU yields accuracy range of 97.73% for reviews and FRCNN yields the accuracy range of 98.42% for movie trailer.

Spreading Magnetic Anomalies Separation of the South China Sea Based on the Nested Elliptical Directional Filters

Spreading magnetic anomalies recorded the paleo-geomagnetic field variation that has great significance in the investigation of the extension process of ocean basins. Interpreting spreading magnetic anomalies under complex geological environments is challenging, especially for marginal sea basins. We proposed nested elliptical directional filters to separate the spreading magnetic anomalies of the South China Sea (SCS). The results show that the spreading magnetic anomalies separated by the nested elliptical directional filters depict the expansion process of the oceanic crust, and the interference magnetic anomalies are effectively suppressed. The separated spreading magnetic anomalies indicate that the expansion process of the SCS is affected by the interactions between the surrounding plates. The spreading magnetic anomalies of the SCS are warped, interrupted, and not strictly parallel. The pattern of the spreading magnetic anomalies reflects multiple ridge jumps during the expansion process and the post-spreading magmatic disturbances. The long-wavelength magnetic anomalies indicate lithospheric fractures and Curie surface variations in the SCS, which are affected by the post-spreading magmatic rejuvenation. The magnetic anomalies of the SCS resulted from the superposition of magnetic anomalies in the ocean crust and the uppermost mantle.

A Notch Filter-Based Coupling Circuit for UNB and NB PLC Systems

This paper introduces an analog notch filtering-based coupling circuit for receivers in ultra-narrowband and narrowband power line communication systems, which are connected to low-voltage electric power grids. It is composed of a twin-T notch analog filter, which is responsible for imposing a significant attenuation on the main frequency (i.e., f0∈{50,60} Hz) in cascade with an elliptic low-pass analog filter, designed with a 3 dB cut-off frequency of fc≫f0. For f0=60 Hz and fc=2 MHz, the prototype of the analog notch filtering-based coupling circuit attains attenuation values of 22 dB and less than 2 dB at the main frequency and in the rest of the frequency bandwidth, respectively, when practical scenarios are considered. Lastly, it shows that the analog notch filtering-based coupling circuit is more effective than a typical capacitive coupling circuit when frequencies lower than 3 kHz are considered for data communication and sensing purposes.

Cognitive Computing for Brain–Computer Interface-Based Computational Social Digital Twins Systems

To accurately and effectively analyze electroencephalogram (EEG) with high complexity, large amount of data, and strong uncertainty, brain–computer interface (BCI) cognitive computing and its signal analysis algorithms are studied based on the digital twins (DTs) cognitive computing platform. To avoid the influence of noise on EEG analysis results, it is necessary to use filtering and defalsification methods to process EEG. Four methods, including Butterworth filter, finite impulse response (FIR) filter, elliptic filter, and wavelet decomposition, are summarized. Based on the Riemann manifold theory, a feature extraction algorithm under transfer learning based on tangent space selection (TL-TSS) is proposed. In the process of decoding EEG, an EEG decoding method combining entropy measure and singular spectrum analysis (SSA) is proposed. An algorithm performance is tested on the motor imagery dataset of the two International BCI Competitions. It is found that when the training sample size accounts for 5%, the TL-TSS algorithm proposed in this work is superior to other algorithms in classification accuracy. In particular, compared with common spatial pattern (CSP) algorithm, it has great advantages. The classification accuracy of A2, A4, A8, and A9 users is the best, and especially for A8 users, the classification accuracy reaches 97.88%. In summary, in the EEG interface technology of DT cognitive computing platform, the combination of cognitive computing and deep learning can improve the recognition and analysis effect of EEG, which is of great value for further optimization of DT cognitive computing system.

Design and Optimization of Butterworth and Elliptic Band Pass Filters in 5G Application

Filters are two-port networks that may pass or attenuate frequencies within defined ranges and can alter the frequency response of any system. In the present research study, the goals and optimization controller embedded with (ADS2019) are used to design Butterworth and Elliptic bandpass filters with frequency ranges of (18GHz – 38GHz), a bandwidth of (7GHz), stopband attenuation of (S21=-60dB), and passband attenuation of (S21=-1dB). Three types of each filter (Hp-Lp 6th order – 3rd order – 6th order) are simulated and optimized to choose the best (C, L) values. The selected filters are redesigned using the Design Filter Guide, and the simulation during this phase yields different values for (C, L). The designed circuit is then transformed into a microstrip model using transmission lines for open and short circuits. The study investigates the differences between each filter in BW-f center- attenuation at the stopband.In the last phase of the study, the circuit of each filter is transformed using a microstrip transmission line to obtain the (W, L) for each component of each filter. Finally, the study compares past studies and research projects in this field.

Universal Graph Filter Design Based on Butterworth, Chebyshev, and Elliptic Functions

Graph filters are crucial tools in processing the spectrum of graph signals. In this paper, we propose to design universal IIR graph filters with low computational complexity by using three kinds of functions, which are Butterworth, Chebyshev, and elliptic functions, respectively. Specifically, inspired by the classical analog filter design method, we first derive the zeros and poles of graph frequency responses. With these zeros and poles, we construct the conjugate graph filters to design the Butterworth high-pass graph filter, Chebyshev high-pass graph filter, and elliptic high-pass graph filter, respectively. On this basis, we further propose to construct a desired graph filter of low pass, band pass, and band stop by mapping the parameters of the desired graph filter to those of the equivalent high-pass graph filter. Furthermore, we propose to set the graph filter order given the maximum passband attenuation and the minimum stopband attenuation. Our numerical results show that the proposed graph filter design methods realize the desired frequency response more accurately than the autoregressive moving average graph filter design method, the linear least-squares fitting-based graph filter design method, and the Chebyshev FIR polynomial graph filter design method.