Search

To enhance the performance of the diffusion robust adaptive filtering algorithm, this paper presents a novel loss function framework named the "Tanh cost function framework." By utilizing this framework, we are able to incorporate standard cost functions, devise novel cost functions, and introduce the associated diffusion algorithm. Moreover, the convergence of the algorithm is thoroughly analyzed. Furthermore, the proposed diffusion Tanh algorithm family is simulated in a system identification model to assess its performance, demonstrating superior performance compared to the standard diffusive algorithm. Notably, the DTHLMS within the diffusion tanh algorithm family exhibits superior performance over the mainstream robust diffusion algorithms. [ABSTRACT FROM AUTHOR]

Three-phase unbalanced system can be represented mathematically in terms of positive, zero, and negative sequence components. These components are really useful to provide information regarding power quality disturbances in an unbalanced system. Thus effective estimation of sequence components using appropriate signal processing models helps to detect short-duration disturbances like sag, swell, etc. The use of fractional-order calculus-based signal processing models has given a new dimension to estimate parameters and track non-stationary power quality events. The input matrix and weight vector are generated using trigonometric expansion considering the voltages across all the three phases. The magnitude and phase of the sequence components can be estimated from the optimal weight vector which is recursively updated using the proposed algorithm. In this study, the strength of fractional-order calculus is exploited using least mean square/fourth (LMS/F) to estimate sequence components in an unbalanced three-phase power system. The proposed fractional LMS/F (FLMS/F)-based model is tested using signals generated from MATLAB 2020 and IEEE 1159 PQE database. All the comparisons of results are made using standard performance measures, and also the convergence analysis of the algorithm is presented. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments. [ABSTRACT FROM AUTHOR]

Partial-update (PU) algorithms offer reduced computational complexity to adaptive second-order Volterra filters (SOV) in nonlinear systems while retaining acceptable performance. In this paper, a new selective partial-update technique for the normalized LMS (NLMS) SOV algorithm is proposed, which requires lesser number of comparison operations per iteration than existing methods while providing close performance to the standard M-Max NLMS-SOV algorithm. Convergence properties of the proposed algorithm are enhanced by making the algorithm step-size time varying based on the natural logarithm function. Simulation experiments compare the proposed algorithm with existing PU and variable step-size NLMS-SOV algorithms, which illustrate the advantageous properties of the new algorithm. The proposed algorithm achieves both lower steady-state misalignment and faster convergence speed when compared with the fixed step-size full-update NLMS-SOV algorithm. Simulations also show that comparison operations overhead of the proposed algorithm is reduced significantly compared to that of the standard M-Max NLMS-SOV algorithm. [ABSTRACT FROM AUTHOR]

Bingham proposed a complex fast independent component analysis (c-FastICA) algorithm to approximate the nengentropy of circular sources using nonlinear functions. Novey proposed extending the work of Bingham using information from a pseudo-covariance matrix for noncircular sources, particularly for sub-Gaussian noncircular signals such as binary phase-shift keying signals. Based on this work, in the present paper we propose a new reference-based contrast function by introducing reference signals into the negentropy, upon which an efficient optimization FastICA algorithm is derived for noncircular sources. This new approach is similar to Novey's nc-FastICA algorithm, but differs in that it is much more efficient in terms of the computational speed, which is significantly notable with a large number of samples. In this study, the local stability of our reference-based negentropy is analyzed and the derivation of our new algorithm is described in detail. Simulations conducted to demonstrate the performance and effectiveness of our method are also described. [ABSTRACT FROM AUTHOR]

A novel single-stage variable-gain amplifier (VGA) based on transconductance $$g_{m}$$ -ratio amplifier is analyzed and designed with wider linear-in-dB gain range and improved linearity. The variable-gain amplifier proposed here consists of an exponential control block, a current squarer and an amplifier block with both input and load degeneration. With the help of current squarer which gets square function of the output current from exponential control block, the VGA achieves the maximum linear gain range in single stage. Current squarer is proposed, which is designed with compensation technique to minimize the second-order effect caused by carrier mobility reduction in short channel MOSFET. To avoid the poor linearity performance of the $$g_{{m}}$$ -ratio amplifiers, the distortion is analyzed and the linearity is improved by applying input and load degenerating technique. At the same power consumption, the input 1 dB compression point can be improved by nearly 8.78 dB. Simulation results show that the VGA can provide a gain variation range of 64.09 dB (from $$-35.59$$ to 28.5 dB) with a 3-dB bandwidth from 47 to 640 MHz. The circuit consumes the maximum power 3.5 mW from a 1.8-V supply. [ABSTRACT FROM AUTHOR]