In the OFDM-based digital terrestrial broadcasting systems, impulsive noise is a significant factor affecting communication quality. A prominent method to suppress impulsive noise is to incorporate a memoryless nonlin...
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In the OFDM-based digital terrestrial broadcasting systems, impulsive noise is a significant factor affecting communication quality. A prominent method to suppress impulsive noise is to incorporate a memoryless nonlinearity at the receiver front-end of the OFDM demodulator, in which parameter estimation of memoryless nonlinearity directly impact the effectiveness of impulsive noise suppression. In this paper, we proposes a deep learning-based memoryless nonlinearity approach for impulsive noise suppression. The proposed method can adaptively estimate the parameters of the memoryless nonlinearity in dynamic impulsive noise environments and achieve totically-optimal parameter estimation. To specific, we design a High-Amplitude Priority Downsampling method to extract the key amplitude characteristics from the input signal, which effectively resolves the issue of extracting amplitude features of impulsive noise. Besides, to address the issue of performance degradation due to insufficient training samples, we propose a novel training method that integrates progressive fine-tuning to complete the training only using few samples. Furthermore, we conduct experiments on signal-to-noise ratio (SNR) and bit error rate (BER) of the signal after impulsive noise suppression. The results validate that the parameters estimated by the proposed method can approximate the theoretical optimal values and the proposed method can effectively suppress impulsive noise and outperform the traditional methods in terms of SNR and BER.
In this paper, we review the derivation of the Gauss-Levenberg-Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the alg...
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In this paper, we review the derivation of the Gauss-Levenberg-Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools. image
The voltage amplitude generated by renewable energy sources is often unstable, necessitating the use of power electronic circuits for effective grid integration. Among these, DC-DC converters play a critical role in m...
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The voltage amplitude generated by renewable energy sources is often unstable, necessitating the use of power electronic circuits for effective grid integration. Among these, DC-DC converters play a critical role in maintaining a constant DC link voltage, typically 400 V or 800 V, at the input of inverter circuits that supply power to the load or the grid. The study focuses on the voltage gain behavior of a high-gain dual cascaded DC-DC boost converter designed for PV (photovoltaic) power systems. Using ANSYS Electronics software with its parametric solver, a comprehensive dataset was generated based on key parameters such as input voltage, power switch duty ratio, and switching frequency. The Improved Grey Wolf Optimizer (IGWO) algorithm was employed to estimate mathematical models for this dataset using linear and quadratic equations. The accuracy of the proposed models was validated across six test scenarios, demonstrating superior performance compared to traditional optimization algorithms, including Harmony Search (HS), Particle Swarm Optimization (PSO), Differential Evolution (DE), and the standard Grey Wolf Optimizer (GWO). Experimental validations yielded output voltages of 23.5 V and 36.1 V for input voltages of 4.8 V and 6.2 V, respectively, closely aligning with simulation results of 23.113 V and 36.447 V. The findings, supported by detailed simulations and graphical analyses, highlight the IGWO algorithm's precision and reliability in predicting converter output voltages under variable input conditions. This work advances renewable energy systems integration by enhancing the modeling and performance of cascaded DC-DC boost converters.
This work considers a temporal fractional HIV/AIDS model with fractal dimensions to examine the influence of awareness on the dynamics of HIV/AIDS. It investigates an epidemiological model of the dynamics of HIV/AIDS ...
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This work considers a temporal fractional HIV/AIDS model with fractal dimensions to examine the influence of awareness on the dynamics of HIV/AIDS. It investigates an epidemiological model of the dynamics of HIV/AIDS transmission in India using actual data from 1990 to 2016 to authenticate the proposed model. The Picard-Lindelof approach is employed to demonstrate the uniqueness and existence of the solutions where the stability analysis is done with the disease-free equilibrium point and basic reproduction number R0. The Adams-Bashforth method employs a two-step Lagrange polynomial in the generalised power-law kernel form to obtain the proposed model's numerical solution. Finally, the least square curve fitting method is used to estimate the parametric study of the proposed model with the actual data on HIV cases reported in India from 1990 to 2016.
Wireless power transfer (WPT) systems are widely used in various applications due to their convenience and efficiency. However, the performance of WPT systems can be significantly affected by misalignment and load var...
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Wireless power transfer (WPT) systems are widely used in various applications due to their convenience and efficiency. However, the performance of WPT systems can be significantly affected by misalignment and load variations. This letter proposes a method to estimate mutual inductance (M) and load resistance (RL) in WPT systems using total harmonic distortion (THD) of input current. A numerical method to estimate these parameters has been developed by deriving an analytical relationship between THD, M , and R L . Experimental validation demonstrates that the proposed method provides accurate estimates of M and R L under varying conditions by only using transmitter current, with maximum estimation errors of 5.1% for M and 3.6% for R L when estimated separately by keeping the other parameter constant. The proposed method is extended to simultaneously estimate M and R L with a maximum estimation error of 4.9%. This method can be adopted for real-time monitoring and control strategies, such as frequency control, system identification, and diagnostic applications, in WPT systems.
The geometric process (GP) has been widely utilized as a stochastic monotone model in the fields of probability and statistics. However, its practical application is often limited by certain assumptions. To address th...
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The geometric process (GP) has been widely utilized as a stochastic monotone model in the fields of probability and statistics. However, its practical application is often limited by certain assumptions. To address this, [Wu (2018). Doubly geometric process and applications. Journal of the Operational Research Society, 69(1), 66-67] introduced the doubly geometric process (DGP) as an extension of the GP model, relaxing some of its assumptions. Due to its ability to overcome the limitations of the GP model, the DGP has gained significant popularity in recent times. This study focuses on the parameter estimation problem for the DGP when the distribution of the first interarrival time follows a lognormal distribution with parameters delta and tau. We employ the maximum likelihood method to obtain estimates for both the model parameters and the distribution parameters. Additionally, we investigate the asymptotic joint distribution and statistical properties such as asymptotic unbiasedness and consistency of the estimators. Furthermore, we propose a novel test procedure to distinguish between the GP and DGP models. To assess the performance of the estimators and the proposed test procedure, we conduct a simulation study involving various sample sizes and parameter values. Finally, we present an application of the developed methods in fitting data from bladder cancer patients.
In this paper, we study parameter identification for solutions to (possibly non-linear) SDEs driven by additive Rosenblatt process and singularity of the induced laws on the path space. We propose a joint estimator fo...
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In this paper, we study parameter identification for solutions to (possibly non-linear) SDEs driven by additive Rosenblatt process and singularity of the induced laws on the path space. We propose a joint estimator for the drift parameter, diffusion intensity, and Hurst index that can be computed from discrete-time observations with a bounded time horizon and we prove its strong consistency under in-fill asymptotics with a fixed time horizon. As a consequence of this strong consistency, singularity of measures generated by the solutions with different drifts is shown. This results in the invalidity of a Girsanov-type theorem for Rosenblatt processes.
The traditional parameter estimation method based on the matrix framework in multiple-input-multiple-output (MIMO) radar with sparse arrays loses the information of the tensor signal structure, resulting in performanc...
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The traditional parameter estimation method based on the matrix framework in multiple-input-multiple-output (MIMO) radar with sparse arrays loses the information of the tensor signal structure, resulting in performance degradation. Therefore, this article primarily investigates parameter estimation methods for bistatic MIMO radar based on a tensor decomposition framework. The conventional tensor based on self-correlation discards two virtual array elements, leading to a loss of performance and degrees of freedom (DOFs). To address this issue, this article proposes a coarray tensor decomposition framework for direction of departure (DOD) and direction of arrival (DOA) estimation, introducing an approach for constructing a coarray tensor. First, a virtual difference coarray is constructed using two subtensors. Then, a coarray tensor is constructed based on the non-Hermitian structure of the cross-correlation signal matrix. Next, the resulting coarray tensor is reconstructed to achieve optimal source identifiability. Nevertheless, the increase in dimensionality resulting from the reconstructed coarray tensor leads to higher algorithmic complexity. To mitigate this, we perform a real-valued transformation on the reconstructed coarray tensor, which speeds up the execution of the method. Additionally, the proposed method also features the capability to suppress colored noise. Theoretical analysis indicates that the reconstructed coarray tensor has more DOFs than the original coarray tensor. Simulation results show that the proposed method has larger DOFs and good parameter estimation performance.
Uncertain differential equations have been extensively studied and applied to model time-varying system in the past decade. Especially, uncertain differential equations with jumps (UDEJs) were proposed to take into ac...
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Uncertain differential equations have been extensively studied and applied to model time-varying system in the past decade. Especially, uncertain differential equations with jumps (UDEJs) were proposed to take into account occasional emergencies. The prerequisite for applying UDEJs models is to reasonably estimate unknown parameters in these models based on observations. For this purpose, this work first proposes the concept of residual for UDEJs, and calculates the residual based on updated UDEJs. Then, combined with the idea of moment estimation, the moment estimations of unknown parameters in UDEJs are given based on residuals. Experiments on two real data sets, Alibaba stock price and birth rate in mainland China, are built to validate the proposed method.
Integrated sensing and communication (ISAC) is a prospective technique that focuses on enabling simultaneous communication and sensing functions to enhance spectral and energy efficiency in future wireless systems. Pe...
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Integrated sensing and communication (ISAC) is a prospective technique that focuses on enabling simultaneous communication and sensing functions to enhance spectral and energy efficiency in future wireless systems. Perceptive mobile network (PMN) is a typical communication-centric ISAC system that integrates sensing capabilities into cellular networks. In current literature, the estimation of sensing parameters in PMNs has been formulated to a generalized delay-quantized channel model and solved as a block multiple measurement vectors (MMV) reconstruction problem. To further improve the reconstruction performance, this paper estimates the parameters jointly by exploiting the temporal correlation of channels. The sparse signals representing the channel parameters of consecutive symbols are modeled to share partial support, and then a two-stage block MMV orthogonal matching pursuit algorithm is proposed to solve the formulated reconstruction problem. In addition, we further provide the theoretical analysis of the proposed algorithm based on restricted isometry property. Simulation results demonstrate that the proposed algorithm achieves higher accuracy in parameter estimation than the existing block MMV reconstruction algorithms.
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