scholarly journals A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Aihua Zhang ◽  
Chen Chen ◽  
Hamid Reza Karimi
Keyword(s):  
Analog Circuit ◽  
Feature Space ◽  
Support Vector ◽  
Fast Time ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Circuit Performance ◽  
Evaluation Strategy ◽  
Performance Indexes ◽  
Testing Speed

Focusing on the analog circuit performance evaluation demand of fast time responding online, a novel evaluation strategy based on adaptive Least Squares Support Vector Regression (LSSVR) which employs multikernel RBF is proposed in this paper. The superiority of the multi-kernel RBF has more flexibility to the kernel function online such as the bandwidths tuning. And then the decision parameters of the kernel parameters determine the input signal to map to the feature space deduced that a well plant model by discarding redundant features. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the testing speed together with the evaluation performance, especially the testing speed of the proposed, is superior to that of the traditional LSSVR andε-SVR, which is suitable for promotion online.

scholarly journals Research on Amplifier Performance Evaluation Based onδ-Support Vector Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Xing Huo ◽  
Aihua Zhang ◽  
Hamid Reza Karimi
Keyword(s):  
Performance Evaluation ◽  
Support Vector Regression ◽  
Computer Calculation ◽  
Support Vector ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Evaluation Strategy ◽  
Support Vectors ◽  
Performance Indexes ◽  
Rbf Kernel

Focusing on the amplifier performance evaluation demand, a novel evaluation strategy based onδ-support vector regression (δ-SVR) is proposed in this paper. Lower computer calculation demand is considered firstly. And this is dealt with by the superiority ofδ-SVR which can be significantly improved on the number of support vectors. Moreover, the function ofδ-SVR employs the modified RBF kernel function which is constructed from an original kernel by removing the last coordinate and adding the linear term with the last coordinate. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the need of the number ofδ-SVR support vectors is the lowest among the other two methods LSSVR andε-SVR under obtaining nearly the same evaluation result. And this is also suitable for promotion computational speed.

scholarly journals A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM

2015 ◽  
Vol 22 (2) ◽  
pp. 251-262 ◽  
Author(s):  
Chaolong Zhang ◽  
Yigang He ◽  
Lei Zuo ◽  
Jinping Wang ◽  
Wei He
Keyword(s):  
Analog Circuit ◽  
Support Vector ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Op Amp ◽  
Novel Approach ◽  
Low Pass ◽  
Diagnosis Accuracy ◽  
Incipient Fault

Abstract Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

2020 ◽  
Vol 13 ◽  
Author(s):  
Jianfeng Jiang
Keyword(s):  
Fault Diagnosis ◽  
Fractal Analysis ◽  
Analog Circuit ◽  
Training Data ◽  
Support Vector ◽  
Pass Filter ◽  
Multiple Kernel ◽  
Testing Data ◽  
Circuit Fault Diagnosis ◽  
Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

scholarly journals New Strategy for Analog Circuit Performance Evaluation under Disturbance and Fault Value

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Aihua Zhang ◽  
Yongchao Wang ◽  
Chen Chen ◽  
Hamid Reza Karimi
Keyword(s):  
Performance Evaluation ◽  
Support Vector Regression ◽  
Analog Circuit ◽  
Support Vector ◽  
Training Set ◽  
Circuit Performance ◽  
New Strategy ◽  
Indirect Evaluation ◽  
Novel Strategy ◽  
Fuzzy Learning

Focus on this issue of disturbance and fault value is inevitable in data collection about analog circuit. A novel strategy is developed for analog circuit online performance evaluation based on fuzzy learning and double weighted support vector machine (DWMK-FSVM). First, the double weighted support vector regression machine is employed to be the indirect evaluation means, relied on the college analog electronic technology experiment to evaluate analog circuit. Second, the superiority of fuzzy learning also is addressed to realize active suppression to the fault values and disturbance parameters. Moreover, the multikernel RBF is employed by support vector regression machine to realize more flexibility online such as the bandwidths tuning. Numerical results, supported by the college analog circuit experiments, adopted OTL performance eight indexes, which were obtained via precision instrument evaluation in two years to construct training set and are then to be evaluated online based on DWMK-FSVM. Simulation results presented not only highlight precision of the evaluation strategy derived here but also illustrate its great robustness.

An experimental analog circuit realization of Matsuda’s approximate fractional-order integral operators for industrial electronics

2021 ◽  
Author(s):  
Murat Koseoglu ◽  
Furkan Nur Deniz ◽  
Baris Baykant Alagoz ◽  
Ali Yuce ◽  
Nusret Tan
Keyword(s):  
Fractional Order ◽  
Analog Circuit ◽  
Low Cost ◽  
Electric Circuit ◽  
Partial Fraction ◽  
Circuit Realization ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Performance Improvements ◽  
Low Pass

Abstract Analog circuit realization of fractional order (FO) elements is a significant step for the industrialization of FO control systems because of enabling a low-cost, electric circuit realization by means of standard industrial electronics components. This study demonstrates an effective operational amplifier-based analog circuit realization of approximate FO integral elements for industrial electronics. To this end, approximate transfer function models of FO integral elements, which are calculated by using Matsuda’s approximation method, are decomposed into the sum of low-pass filter forms according to the partial fraction expansion. Each partial fraction term is implemented by using low-pass filters and amplifier circuits, and these circuits are combined with a summing amplifier to compose the approximate FO integral circuits. Widely used low-cost industrial electronics components, which are LF347N opamps, resistor and capacitor components, are used to achieve a discrete, easy-to-build analog realization of the approximate FO integral elements. The performance of designed circuit is compared with performance of Krishna’s FO circuit design and performance improvements are shown. The study presents design, performance validation and experimental verification of this straightforward approximate FO integral realization method.

scholarly journals Design and Implementation of High Performance PPSK demodulator in Biomedical Implant

2021 ◽  
Author(s):  
Hima Bindu Katikala ◽  
G.Ramana Murthy ◽  
Yatavakilla Amarendra Nath
Keyword(s):  
Low Power ◽  
Hearing Aids ◽  
Data Transmission ◽  
High Performance ◽  
Large Scale ◽  
Analog Circuit ◽  
Supply Voltage ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Abstract The important challenge for the realization of hearing aids is small size, low cost, low power consumption and better performance, etc. Keeping these requirements in view this work concentrates on the VLSI (Very Large Scale Integrated) implementation of analog circuit that mimic the PPSK (Passive Phase Shift Keying) demodulator with low pass filter. This research deals with RF Cochlear implant circuits and their data transmission. A PPSK modulator is used for uplink data transmission in biomedical implants with simultaneous power, data transmission This paper deals about the implementation of PPSK demodulator with related circuits and low pass filter which are used in cochlear implants consumes low power and operates at 14MHz frequency. These circuits are designed using FINFET 20nm technology with 0.4v DC supply voltage. The performance of proposed design over the previous design is operating at low threshold voltage, reduces static leakage currents and often observed greater than 30 times of improvement in speed performance

HISTORY AND PROGRESS OF THE TOW–THOMAS BI-QUADRATIC FILTER PART I: GENERATION AND OP AMP REALIZATIONS

2008 ◽  
Vol 17 (01) ◽  
pp. 33-54 ◽  
Author(s):  
AHMED M. SOLIMAN
Keyword(s):  
Second Order ◽  
Spice Simulation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Circuit Performance ◽  
High Q ◽  
Op Amp ◽  
Compensation Methods ◽  
Low Pass ◽  
History Of

The history of Tow–Thomas second-order filter is reviewed. Two alternative generation methods of the Tow–Thomas filter are discussed. The first is a generation method from the second-order passive RLC filter and the second is from the multiple feedbacks inverting low-pass filter using a single op amp. Several forms of the circuit are briefly reviewed. Passive and active compensation methods to improve the circuit performance for high-Q designs are summarized. Spice simulation results are included.

scholarly journals Readout for simple and precise analog acoustic impact initialization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Roman Sotner ◽  
Ladislav Polak ◽  
Jan Jerabek ◽  
Abhirup Lahiri ◽  
Winai Jaikla
Keyword(s):  
Shock Wave ◽  
Analog Circuit ◽  
Linear Part ◽  
Readout System ◽  
Spectral Character ◽  
Time Duration ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Impulse Duration ◽  
Acoustic Shock Wave

AbstractAn economic concept of acoustic shock wave sensing readout system for simple computer processing is introduced in this work. Its application can be found in precise initialization of the stopwatch from the starter sound, handclap or gun in competitive sport races but also in many other places. The proposed device consists of several low-cost commercially available components and it is powered by a 9 V battery. The proposed device reliably reacts on incoming acoustic shock wave by generation of explicit impulse having controllable duration. It significantly overcomes basic implementations using only a microphone and amplifier (generating parasitic burst instead of defined and distinct impulse) or systems allowing a limited number of adjustable features (gain and/or threshold of the comparator—our concept offers the adjustment of gain, cut-off frequency, threshold level and time duration of active state). In comparison with standard methods, the proposed approach simplifies and makes sensing device less expensive and universal for any powder-based starting gun (without necessity to adapt starting gun). The proposed device, among others, has the following features: impulse duration can be controlled from hundreds of μs up to 2.3 s, the gain range of linear part of processing from 6 to 40 dB and open-collector output compatible with 5 V TTL or 3.3 V CMOS logic. The initialization has been tested in the range from tens of centimeters up to four meters. In order to highlight the important spectral components, the spectral character of the signal can be optimally reduced by a low-pass filter. The quiescent power consumption of the designed simple analog circuit reaches 90 mW. Several use cases, response of the designed system on gunshot signature, talking, hand-clapping and hit on the sensing microphone, are studied and compared to each other. Simulation and experimental results confirm functionality of the realized system.

scholarly journals Identification And Classification Of Brain Tumor Images Using Efficient Classifier

2019 ◽  
Vol 8 (6) ◽  
pp. 3677-3683
Keyword(s):  
Brain Tumor ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Segmentation Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Feature Extraction Method ◽  
Low Pass ◽  
Occurrence Matrix

The main objective of this study is to propose a model for finding brain tumor. Failing to detect the tumor in its prior stage will increases the chance of losing a life. So the identification and treatment for the tumor in its prior stages become vital to save the life of a human being. This work uses the Magnetic Resonance Image (MRI) images to identify and classify the benign and malign type brain tumors. Low pass filter is applied to preprocess the MRI image that removes the unwanted background structures at the same time keeps the important portions sharpened. Watershed segmentation method is used for segmenting the tumor affected area independently. The statistical feature extraction method Gray Level Co-occurrence Matrix (GLCM) is applied to take out the imperative features from the segmented tumor. The feature selection is performed using Recursive Feature Elimination- Particle Swarm Optimization (RFE-PSO) method. Ensemble Support Vector Machine (SVM) is applied to classify the tumors into harmless and harmful from the medical image.

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