Elliptic Filter Research Articles

Bridge damage detection using reconstructed mode shape by improved vehicle scanning method

In this study, the improved vehicle scanning method (VSM) is adopted for structural health monitoring to identify healthy and damaged states of bridges. The improved VSM uses a designed elliptic filter (instead of the conventional Butterworth filter) to obtain the narrowband signals from the contact point response. These high-quality narrowband signals are able to furnish accurate vibration modes that reveal kinks that indicate the damage locations in the bridges. This improved VSM works best with a series of vehicles that are modeled as moving sprung masses passing over the bridge. The exciting vehicles modeled by heavier masses excite the bridge, and they are followed by a testing vehicle modeled by lighter masses to capture the acceleration response at the contact point. Some example problems involving bridges with different damage locations and severities were studied. It is shown herein that the improved VSM extracted mode shapes have very visible kinks to mark the damage positions of the bridges when compared to mode shapes extracted from existing VSMs. So, the improved VSM is able to directly pinpoint the damage locations from the extracted mode shapes with minimal postprocessing effort, whereas existing VSMs generally require additional effort, e.g., constructed damage indices, for damage identification. To better identify the damage locations, a few higher modes should be used so as to avoid missing the damage locations at nodes of a particular vibration mode.

Analytical Criteria for Designing Multiresonance Filters in Scattering Systems, with Application to Microwave Metasurfaces

We present general analytical criteria for the design of lossless reciprocal two-port systems, which exhibit prescribed scattering spectra $S(\omega)$ satisfying $S_{22}(\omega)=e^{i\varphi}S_{11}(\omega)$, including symmetric ($S_{22}=S_{11}$) or "antimetric" ($S_{22}=-S_{11}$) responses, such as standard filters (Butterworth, Chebyshev, elliptic, etc.). We show that the non-normalized resonant (quasi-normal) modes (QNMs) of all such two-port systems couple to the input and output ports with specific unitary ratios, whose relative signs determine the position of the scattering zeros on the real frequency axis. This allows us to obtain design criteria assigning values to the poles, background response, and QNM-to-ports coupling coefficients. Filter devices can then be designed via a well-conditioned nonlinear optimization (or root-finding) problem using a numerical eigensolver. As an application, we design multiple microwave metasurfaces configured for polarization-preserving transmission, reflective polarization conversion, or diffractive "perfect anomalous reflection", to realize filters that precisely match standard bandpass or bandstop filters of various types, orders and bandwidths, with focus on the best-performing elliptic filters.

Modal properties identification of damped bridge using improved vehicle scanning method

This paper is concerned with an improved Vehicle Scanning Method (VSM) for modal properties identification of bridges with allowances for damping and road roughness. Owing to the bridge damping, signals are small and degrade over time and may be corrupted by the road roughness. In order to enhance VSM in reading these weak signals, an appropriately tuned elliptic filter is employed instead of the conventional bandpass filter, such as the Butterworth filter. The VSM is improved further by adopting the recently developed Moving Internal Node Element (MINE) method that can furnish more accurate predictions of the higher natural frequencies of the bridge. Illustrative example problems involving undamped and damped bridges with or without road roughness are presented to show the superiority of the improved VSM over conventional VSM that uses a standard bandpass filter and finite element method for analysis. It will be shown herein that the improved VSM can identify the higher mode shapes even with a bridge damping ratio of 3% and standard road roughness, whereas the existing VSM could not do so.

Multi-source data fusion technique for parametric fault diagnosis in analog circuits

Input test signal plays important role in testing of analog circuits. Single type of input stimulus cannot maximally reveal the state of the circuit. To combat this shortcoming, this work proposes to integrate information from the output responses corresponding to different input stimuli and to use the combined information to improve the accuracy of fault diagnosis in analog circuits. The circuit under test is excited with different input signals and wavelet features are extracted from the output responses of the circuit. Ultimate fault features have been defined by applying data fusion algorithm to the sets of wavelet features obtained from individual output. Data fusion has been performed in two steps, data whitening and Principal component analysis (PCA). Fused features are used to train SVM (Support Vector Machine) classifier for fault diagnosis. The proposed approach is validated with three types of filter circuits, i.e. Sallen-Key band pass filter, four OPAMP high pass filter and elliptic low pass filter. The average accuracy of the proposed fault classification method has been found greater than 99.8% for all the test circuits. The proposed technique offers improved classification accuracy, lower computational burden and lesser implementation complexity.

Wideband 1-Bit Bandpass Delta Sigma Modulator Using Elliptic Filter in Noise Transfer Function

A 1-bit band-pass delta-sigma modulator (BP-DSM), which utilizes oversampling technology, allows a modulated signal to be directly output by using a high-speed 1-bit digital pulse train, thus realizing miniaturization of transmitters. For 1-bit BP-DSM, the noise transfer function (NTF) is used to suppress quantization noise power in the transmission band and a guideline of out-of-band gain of |NTF|< 1.5 is used to prevent oscillation. However, in previous studies, such as Butterworth and inverse Chebyshev filters, the out-of-band gain was designed indirectly by tuning the zeros and poles in the transmission band and thus, was not stabilized sufficiently. Furthermore, even though the zeros of the NTF are identical to the poles of the loop filter, there are still widely used designs in which the zeros are set on the unit circle, making stabilization quite difficult. Therefore, in this paper, we propose a feasible implementation of the NTF for 1-bit BP-DSM with an elliptic filter that can be used to set not only in-band but also out-of-band gain, in which both the zeros and poles are set inside the unit circle. As a design result, a modulation bandwidth of 400 MHz as a relative bandwidth of 11%, a noise suppression bandwidth of 800 MHz, and an adjacent channel leakage power ratio of 50 dB were achieved at a center frequency of 3.6 GHz, enabling a wider bandwidth and higher SNR than before by improving the stability.

Convolutional Neural Network Based on Diverse Gabor Filters for Deepfake Recognition

Media synthesis and manipulation has reached unprecedented levels of realism owing to the proliferation of deep learning. Deepfake has been the de-facto tool for media manipulation. Although this technology has potential in the entertainment industry, its threats include political manipulation and bypassing biometric security systems. As a result, deepfake detection has garnered widespread attention among research communities. The intuition is to use deep learning to fix the problems created by deep learning. Although convolutional neural networks have shown their dominance in the filed of pattern recognition, the receptive field-model size dilemma still persists along with the lack of interpretation for such models. While the traditional Gabor function was proposed to fix these problems, it can only generate limited linear Gabor filters which makes it optimal for limited data and applications. The contribution of this paper is quadruple: (i) proposing a unified Gabor function capable of generating linear, elliptical, and circular Gabor filters. (ii) leveraging the back-propagation learning framework to incorporate the proposed function in convolutional neural networks and generate adaptive Gabor filters. (iii) presenting a dual scale large receptive field network for deepfake image recognition. (iv) demonstrating where the proposed model stands in terms of performance and architecture size compared to state-of-the-art models. The proposed model is evaluated on four benchmark datasets: Celeb-DF (v2), DeepFake Detection Challenge Preview, FaceForensics++ and Wilddeepfake. Experimental results show that the proposed adaptive Gabor filters reduce the model size by 64.9% compared to adaptive weighted filters without performance reduction.

Design Technique for Multilayered Filters Composed of LC Resonators

In multilayer filter design, both the parasitic effects and the lack of fine-tunings may result in the error of component values, which has great influences on performance. In this paper, a design technique for multilayer filter composed of LC resonators is proposed. With this technique, influences of the error of component values may be reduced and the design procedure may be simplified. An elliptic filter composed of four LC resonators is used to demonstrate the proposed technique. Three experimental prototypes with different component values were fabricated for comparisons. Very good agreements between measured responses and designed responses can be observed. The measured parasitic resonances are higher than the third harmonic frequency. These facts show the effectiveness of the proposed technique.

Design of signal conditioning circuit at the output end of signal source based on AD9954

The signal source based on DDS technology has the characteristics of high precision and high resolution, and is widely used in precision testing, instrumentation and other fields. However, interference signals such as high frequency harmonics and noise will inevitably appear in the output signal of the DDS chip, and even the sinusoidal signal generated by the DDS will be completely covered. This article takes ADI’s 14-bit high-precision DDS chip AD9954 as an example. I/V conversion and elliptic filter circuits are added to the output of the chip to obtain a pure sinusoidal signal. The rationality of the design is verified by multisim simulation software. This circuit provides strong support for the design and implementation of high-precision signal sources based on DDS.

Additive manufactured spherical resonator V‐band elliptical waveguide filter

AbstractThe design of an additively manufactured V‐band bandpass filter is proposed using spherical resonators with elliptical waveguides feeds. Compared with the traditional rectangular form, the curvilinear architecture seeks to mitigate corner radius effects in both the electromagnetic performance and additive manufacturing process. The filter and calibration components required for measurement are printed as single structures and then electroless silver plated using a closed‐loop bidirectional pump‐driven fluidic system. Simulated and measured results are provided.

Design of variable elliptical filters with direct tunability in 2D domain

Design of variable elliptical filters with direct tunability in 2D domain

Performance Analysis and Comparison of narrowband noise passing through filter types (Elliptic) and (Butterworth)

This paper introduces the properties of an elliptical filter and Butterworth filter characteristics when narrowband noise passes through them, which has the same behavior in both, traffic and stop range. The amount of ripple in each band can be adjusted independently as it has a faster gain transition between the pass and stop band, so a compression has been made between elliptical and Butterworth filters response to narrowband noise in order to find a new ways to increase their immunity to noise. MATLAB Simulink Software has been used as a simulations environment.

Classification of EEG Signals using Nonlinear Features and Preprocessing Techniques

In this paper we study the effect of nonlinear preprocessing techniques in the classification of electroencephalogram (EEG) signals. These methods are used for classifying the EEG signals captured from epileptic seizure activity and brain tumor category. For the first category, preprocessing is carried out using elliptical filters, and statistical features such as Shannon entropy, mean, standard deviation, skewness and band power. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used for the classification. For the brain tumor EEG signals, empirical mode decomposition is used as a pre-processing technique along with standard statistical features for the classification of normal and abnormal EEG signals. For epileptic signals we have achieved an average accuracy of 94% for a three-class classification and for brain tumor signals we have achieved a classification accuracy of 98% considering it as a two class problem.

Three Waveguide Coupled Sagnac Loop Reflectors for Advanced Spectral Engineering

We theoretically investigate advanced multi-functional integrated photonic filters formed by three waveguide coupled Sagnac loop reflectors (3WC-SLRs). By tailoring the coherent mode interference, the spectral response of the 3WC-SLR resonators is engineered to achieve different filter functions with good performance parameters. These include optical analogues of Fano resonances that yield ultrahigh spectral extinction ratios (ERs) and slope rates, resonance mode splitting with high ERs and low free spectral ranges, and classical Butterworth, Bessel, Chebyshev, and elliptic filters. A detailed analysis of the impact of the structural parameters and fabrication tolerances is provided to facilitate device design and optimization. The requirements for practical applications are also considered. Our results theoretically verify the effectiveness of using 3WC-SLR resonators as multi-functional integrated photonic filters for flexible spectral engineering for a range of applications such as optical filtering, switching, and sensing.

Improving the intensity-contrast image of a noisy digital hologram by convolution of Chebyshev type 2 and elliptic filters.

In this paper, a new, to the best of our knowledge, technique convolves the windowed Fourier filtering (WFF) of the Fresnel transform with the transfer functions of both Chebyshev type 2 and elliptic filters to enhance the intensity-contrast image of a noisy digital hologram. The recorded digital hologram is reconstructed by the Fresnel approach, the reconstructed intensity-contrast image is transformed by WFF, and the obtained spectrum is convolved in frequency domain with the transfer functions of Chebyshev type 2 and elliptic filters. The result of convolution is transformed by inverse WFF to produce a speckle-free image with a sharp roll-off and no ripples in both pass- and stop-bands. The experimental results with a die in the presence and absence of a rotating ground glass diffuser are shown and demonstrate that the resolution can be effectively enhanced with simple setup and procedure. The proposed technique can improve the capabilities of digital holography in three-dimensional (3D) microscopy.

Inductance Simulators and Their Application to the 4th Order Elliptic Lowpass Ladder Filter Using CMOS VD-DIBAs

This paper presents inductance simulators using the voltage differencing differential input buffered amplifier (VD-DIBA) as an active building block. Three types of inductance simulators, including floating lossless inductance, series inductance-resistance, and parallel inductance-resistance simulators, are proposed, in addition to their application to the 4th order elliptic lowpass ladder filter. The simple design procedures of these inductance simulators using a circuit block diagram are also given. The proposed inductance simulators employ two VD-DIBAs and two passive elements. The complementary metal oxide semiconductor (CMOS) VD-DIBA used in this design utilizes the multiple-input metal oxide semiconductor (MOS) transistor technique in order to achieve a compact and simple structure with a minimum count of transistors. Thanks to this technique, the VD-DIBA offers high performances compared to the other CMOS structures presented in the literature. The CMOS VD-DIBAs and their applications are designed and simulated in the Cadence environment using a 0.18 µm CMOS process from Taiwan semiconductor manufacturing company (TSMC). Using a supply voltage of ±0.9 V, the linear operation of VD-DIBA is obtained over a differential input range of −0.5 V to 0.5 V. The lowpass (LP) ladder filter realized with the proposed inductance simulators shows a dynamic range (DR) of 80 dB for a total harmonic distortion (THD) of 2% at 1 kHz and a 1.8 V peak-to-peak output. In addition, the experimental results of the floating inductance simulators and their applications are obtained by using VD-DIBA constructed from the available commercial components LM13700 and AD830. The simulation results are in agreement with the experimental ones, confirming the advantages of the inductance simulators and their application.

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.
 
 
 

Hessian-MRLoG: Hessian information and multi-scale reverse LoG filter for pulmonary nodule detection

Computer-aided detection (CADe) of pulmonary nodules is an effective approach for early detection of lung cancer. However, due to the low contrast of lung computed tomography (CT) images, the interference of blood vessels and classifications, CADe has the problems of low detection rate and high false-positive rate (FPR). To solve these problems, a novel method using Hessian information and multi-scale reverse Laplacian of Gaussian (LoG) (Hessian-MRLoG) is proposed and developed in this work. Also, since the intensity distribution of the LoG operator and the lung nodule in CT images are inconsistent, and their shapes are mismatched, a multi-scale reverse Laplacian of Gaussian (MRLoG) is constructed. In addition, in order to enhance the effectiveness of target detection, the second-order partial derivatives of MRLoG are partially adjusted by introducing an adjustment factor. On this basis, the Hessian-MRLoG model is developed, and a novel elliptic filter is designed. Ultimately, in this study, the method of Hessian-MRLoG filtering is proposed and developed for pulmonary nodule detection. To verify its effectiveness and accuracy, the proposed method was used to analyze the LUNA16 dataset. The experimental results revealed that the proposed method had an accuracy of 93.6% and produced 1.0 false positives per scan (FPs/scan), indicating that the proposed method can improve the detection rate and significantly reduce the FPR. Therefore, the proposed method has the potential for application in the detection, localization and labeling of other lesion areas.

Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids “reinventing the wheel,” but also presents a lightweight solution to otherwise computationally heavy problems.

Voltage-Mode Elliptic Band-Pass Filter Based on Multiple-Input Transconductor

This paper presents a new voltage-mode elliptic band-pass filter based on a multiple-input transconductor (MI-OTA). The MI-OTA’s structure employs the multiple-input MOS transistor technique that simply enables to increase the number of OTA’s inputs without increasing the number of current branches or the differential pairs. The MI-OTA features high linearity over a wide input range with a compact and simple CMOS structure. From the advantage of multiple inputs, it enables to construct the arbitrarily summing and subtracting under the proposed voltage-mode filter design procedure. The filter is designed and simulated in Cadence environment using $0.18~\mu \text{m}$ TSMC CMOS technology. The filter offers 72.9 dB dynamic range for 2 % total harmonic distortion (THD) for sine input signal of 0.5 Vpp @ 1kHz with voltage supply ± 0.9V. The simulation results of the filter are in agreement with the RLC prototype. The experimental results using commercially available IC are also included to confirm the proposed filter that are in good agreement with the simulation results.

Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

Human behavior modeling (HBM) is a challenging classification task for researchers seeking to develop sustainable systems that precisely monitor and record human life-logs. In recent years, several models have been proposed; however, HBM remains an inspiring problem that is only partly solved. This paper proposes a novel framework of human behavior modeling based on wearable inertial sensors; the system framework is composed of data acquisition, feature extraction, optimization and classification stages. First, inertial data is filtered via three different filters, i.e., Chebyshev, Elliptic and Bessel filters. Next, six different features from time and frequency domains are extracted to determine the maximum optimal values. Then, the Probability Based Incremental Learning (PBIL) optimizer and the K-Ary tree hashing classifier are applied to model different human activities. The proposed model is evaluated on two benchmark datasets, namely DALIAC and PAMPA2, and one self-annotated dataset, namely, IM-LifeLog, respectively. For evaluation, we used a leave-one-out cross validation scheme. The experimental results show that our model outperformed existing state-of-the-art methods with accuracy rates of 94.23%, 94.07% and 96.40% over DALIAC, PAMPA2 and IM-LifeLog datasets, respectively. The proposed system can be used in healthcare, physical activity detection, surveillance systems and medical fitness fields.