Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm

The maximum complex correntropy criterion (MCCC) has been extended to complex domain for dealing with complex-valued data in the presence of impulsive noise. Compared with the correntropy based loss, a kernel risk-sensitive loss (KRSL) defined in kernel space has demonstrated a superior performance surface in the complex domain. However, there is no report regarding the recursive KRSL algorithm in the complex domain. Therefore, in this paper we propose a recursive complex KRSL algorithm called the recursive minimum complex kernel risk-sensitive loss (RMCKRSL). In addition, we analyze its stability and obtain the theoretical value of the excess mean square error (EMSE), which are both supported by simulations. Simulation results verify that the proposed RMCKRSL out-performs the MCCC, generalized MCCC (GMCCC), and traditional recursive least squares (RLS).

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A Robust Diffusion Minimum Kernel Risk-Sensitive Loss Algorithm over Multitask Sensor Networks

Sensors ◽

10.3390/s19102339 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2339 ◽

Cited By ~ 6

Author(s):

Xinyu Li ◽

Qing Shi ◽

Shuangyi Xiao ◽

Shukai Duan ◽

Feng Chen

Keyword(s):

Sensor Networks ◽

Gaussian Noise ◽

Impulsive Noise ◽

Estimation Algorithm ◽

Distributed Estimation ◽

Superior Performance ◽

Mean Square ◽

Unknown Parameters ◽

Risk Sensitive ◽

The Impact

Distributed estimation over sensor networks has attracted much attention due to its various applications. The mean-square error (MSE) criterion is one of the most popular cost functions used in distributed estimation, which achieves its optimality only under Gaussian noise. However, impulsive noise also widely exists in real-world sensor networks. Thus, the distributed estimation algorithm based on the minimum kernel risk-sensitive loss (MKRSL) criterion is proposed in this paper to deal with non-Gaussian noise, particularly for impulsive noise. Furthermore, multiple tasks estimation problems in sensor networks are considered. Differing from a conventional single-task, the unknown parameters (tasks) can be different for different nodes in the multitask problem. Another important issue we focus on is the impact of the task similarity among nodes on multitask estimation performance. Besides, the performance of mean and mean square are analyzed theoretically. Simulation results verify a superior performance of the proposed algorithm compared with other related algorithms.

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A Modified Recursive Regularization Factor Calculation for Sparse RLS Algorithm with l1-Norm

Mathematics ◽

10.3390/math9131580 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1580

Author(s):

Junseok Lim ◽

Keunhwa Lee ◽

Seokjin Lee

Keyword(s):

Computational Complexity ◽

Impulse Response ◽

Recursive Least Squares ◽

Mean Square ◽

L1 Norm ◽

Actual System ◽

Rls Algorithm ◽

Simulation Results ◽

Norm Penalty ◽

True System

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l1-norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity.

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A Robust Adaptive Filter for a Complex Hammerstein System

Entropy ◽

10.3390/e21020162 ◽

2019 ◽

Vol 21 (2) ◽

pp. 162 ◽

Cited By ~ 1

Author(s):

Guobing Qian ◽

Dan Luo ◽

Shiyuan Wang

Keyword(s):

Adaptive Filter ◽

Adaptive Filters ◽

Impulsive Noise ◽

Complex Domain ◽

Mean Square ◽

Least Mean Square ◽

Hammerstein System ◽

Robust Adaptive ◽

The Stability ◽

Maximum Correntropy Criterion

The Hammerstein adaptive filter using maximum correntropy criterion (MCC) has been shown to be more robust to outliers than the ones using the traditional mean square error (MSE) criterion. As there is no report on the robust Hammerstein adaptive filters in the complex domain, in this paper, we develop the robust Hammerstein adaptive filter under MCC to the complex domain, and propose the Hammerstein maximum complex correntropy criterion (HMCCC) algorithm. Thus, the new Hammerstein adaptive filter can be used to directly handle the complex-valued data. Additionally, we analyze the stability and steady-state mean square performance of HMCCC. Simulations illustrate that the proposed HMCCC algorithm is convergent in the impulsive noise environment, and achieves a higher accuracy and faster convergence speed than the Hammerstein complex least mean square (HCLMS) algorithm.

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Injection-rolling nozzle in an imprint system with continuous injection direct rolling for ultra-thin microstructure guide light plate

Advances in Mechanical Engineering ◽

10.1177/16878140211011274 ◽

2021 ◽

Vol 13 (4) ◽

pp. 168781402110112

Author(s):

Yan Lou ◽

Kewei Chen ◽

Xiangwei Zhou ◽

Yanfeng Feng

Keyword(s):

Flow Velocity ◽

Superior Performance ◽

Light Transmittance ◽

Velocity Difference ◽

Orthogonal Experiments ◽

Width Direction ◽

Direct Rolling ◽

Simulation Results ◽

Continuous Injection ◽

Better Than

A novel Injection-rolling Nozzle (IRN) in an imprint system with continuous injection direct rolling (CIDR) for ultra-thin microstructure polymer guide light plates was developed to achieve uniform flow velocity and temperature at the width direction of the cavity exit. A novel IRN cavity was designed. There are eight of feature parameters of cavity were optimized by orthogonal experiments and numerical simulation. Results show that the flow velocity at the width direction of the IRN outlet can reach uniformity, which is far better than that of traditional cavity. The smallest flow velocity difference and temperature difference was 0.6 mm/s and 0.24 K, respectively. The superior performance of the IRN was verified through a CIDR experiment. Several 0.35-mm thick, 340-mm wide, and 10-m long microstructural Polymethyl Methacrylate (PMMA) guide light plates were manufactured. The average filling rates of the microgrooves with the aspect ratio 1:3 reached above 93%. The average light transmittance is 88%.

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Fast computation and practical use of amplitudes at non-Fourier frequencies

Computational Statistics ◽

10.1007/s00180-020-01061-4 ◽

2021 ◽

Author(s):

Erhard Reschenhofer ◽

Manveer K. Mangat

Keyword(s):

High Frequency ◽

Superior Performance ◽

High Frequency Data ◽

Mean Square ◽

Fast Computation ◽

Efficient Computation ◽

Frequency Data ◽

Second Stage ◽

Orthogonality Conditions ◽

Memory Parameter

AbstractIn this paper, it is shown that the performance of various frequency-domain estimators of the memory parameter can be boosted by the inclusion of non-Fourier frequencies in addition to the regular Fourier frequencies. A fast two-stage algorithm for the efficient computation of the amplitudes at these additional frequencies is presented. In the first stage, the naïve sine and cosine transforms are computed with a modified version of the Fast Fourier Transform. In the second stage, these transforms are amended by taking the violation of the standard orthogonality conditions into account. A considerable number of auxiliary quantities, which are required in the second stage, do not depend on the data and therefore only need to be computed once. The superior performance (in terms of root-mean-square error) of the estimators based also on non-Fourier frequencies is demonstrated by extensive simulations. Finally, the empirical results obtained by applying these estimators to financial high-frequency data show that significant long-range dependence is present only in the absolute intraday returns but not in the signed intraday returns.

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Zero Watermarking Algorithm for Vector Geographic Data Based on the Number of Neighboring Features

Symmetry ◽

10.3390/sym13020208 ◽

2021 ◽

Vol 13 (2) ◽

pp. 208

Author(s):

Qifei Zhou ◽

Changqing Zhu ◽

Na Ren ◽

Weitong Chen ◽

Weiteng Gong

Keyword(s):

Second Order ◽

Superior Performance ◽

Statistical Characteristics ◽

First Order ◽

Geographic Data ◽

Simulation Results ◽

Topological Characteristics ◽

Zero Watermarking ◽

Critical Challenge

Zero watermarking is an important part of copyright protection of vector geographic data. However, how to improve the robustness of zero watermarking is still a critical challenge, especially in resisting attacks with significant distortion. We proposed a zero watermarking method for vector geographic data based on the number of neighboring features. The method makes full use of spatial characteristics of vector geographic data, including topological characteristics and statistical characteristics. First, the number of first-order neighboring features (NFNF) and the number of second-order neighboring features (NSNF) of every feature in vector geographic data are counted. Then, the watermark bit is determined by the NFNF value, and the watermark index is determined by the NSNF value. Finally, combine the watermark bits and the watermark indices to construct a watermark. Experiments verify the theoretical achievements and good robustness of this method. Simulation results also demonstrate that the normalized coefficient of the method is always kept at 1.00 under the attacks that distort data significantly, which has the superior performance in comparison to other methods.

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Model development and control of an auto-refrigerated polystyrene polymerization reactor

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211025125 ◽

2021 ◽

pp. 014233122110251

Author(s):

Reyhane Mokhtarname ◽

Ali Akbar Safavi ◽

Leonhard Urbas ◽

Fabienne Salimi ◽

Mohammad M Zerafat ◽

...

Keyword(s):

Temperature Control ◽

Model Development ◽

Heat Removal ◽

Superior Performance ◽

Polymerization Process ◽

Vacuum System ◽

Control Scheme ◽

Simulation Results ◽

And Control ◽

Operating Plant

Dynamic model development and control of an existing operating industrial continuous bulk free radical styrene polymerization process are carried out to evaluate the performance of auto-refrigerated CSTRs (continuous stirred tank reactors). One of the most difficult tasks in polymerization processes is to control the high viscosity reactor contents and heat removal. In this study, temperature control of an auto-refrigerated CSTR is carried out using an alternative control scheme which makes use of a vacuum system connected to the condenser and has not been addressed in the literature (i.e. to the best of our knowledge). The developed model is then verified using some experimental data of the real operating plant. To show the heat removal potential of this control scheme, a common control strategy used in some previous studies is also simulated. Simulation results show a faster dynamics and superior performance of the first control scheme which is already implemented in our operating plant. Besides, a nonlinear model predictive control (NMPC) is developed for the polymerization process under study to provide a better temperature control while satisfying the input/output and the heat exchanger capacity constraints on the heat removal. Then, a comparison has been also made with the conventional proportional-integral (PI) controller utilizing some common tuning rules. Some robustness and stability analyses of the control schemes investigated are also provided through some simulations. Simulation results clearly show the superiority of the NMPC strategy from all aspects.

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Adaptive modulation based on steady-state mean square error for underwater acoustic communication

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01956-w ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Zhiyong Liu ◽

Zhoumei Tan ◽

Fan Bai

Keyword(s):

Steady State ◽

Mean Square Error ◽

Adaptive Modulation ◽

Blind Equalization ◽

Training Sequence ◽

Transmission Efficiency ◽

Underwater Acoustic Communication ◽

Mean Square ◽

Modulation Mode ◽

Simulation Results

AbstractTo improve the transmission efficiency and facilitate the realization of the scheme, an adaptive modulation (AM) scheme based on the steady-state mean square error (SMSE) of blind equalization is proposed. In this scheme, the blind equalization is adopted and no training sequence is required. The adaptive modulation is implemented based on the SMSE of blind equalization. The channel state information doesn’t need to be assumed to know. To better realize the adjustment of modulation mode, the polynomial fitting is used to revise the estimated SNR based on the SMSE. In addition, we also adopted the adjustable tap-length blind equalization detector to obtain the SMSE, which can adaptively adjust the tap-length according to the specific underwater channel profile, and thus achieve better SMSE performance. Simulation results validate the feasibility of the proposed approaches. Simulation results also show the advantages of the proposed scheme against existing counterparts.

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Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise

Electronics ◽

10.3390/electronics10020196 ◽

2021 ◽

Vol 10 (2) ◽

pp. 196

Author(s):

Jun Lu ◽

Qunfei Zhang ◽

Wentao Shi ◽

Lingling Zhang ◽

Juan Shi

Keyword(s):

Adaptive Filtering ◽

Interference Cancellation ◽

Impulsive Noise ◽

Weight Vector ◽

Filtering Algorithm ◽

Convergence Performance ◽

Simulation Results ◽

Robust Adaptive ◽

Arctangent Cost Function ◽

Simultaneous Transmission

Self-interference (SI) is usually generated by the simultaneous transmission and reception in the same system, and the variable SI channel and impulsive noise make it difficult to eliminate. Therefore, this paper proposes an adaptive digital SI cancellation algorithm, which is an improved normalized sub-band adaptive filtering (NSAF) algorithm based on the sparsity of the SI channel and the arctangent cost function. The weight vector is hardly updated when the impulsive noise occurs, and the iteration error resulting from impulsive noise is significantly reduced. Another major factor affecting the performance of SI cancellation is the variable SI channel. To solve this problem, the sparsity of the SI channel is estimated with the estimation of the weight vector at each iteration, and it is used to adjust the weight vector. Then, the convergence performance and calculation complexity are analyzed theoretically. Simulation results indicate that the proposed algorithm has better performance than the referenced algorithms.

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Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

Mathematical Problems in Engineering ◽

10.1155/2016/9641608 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Ming Yin ◽

Kai Yu ◽

Zhi Wang

Keyword(s):

Compressive Sensing ◽

Sensor Array ◽

Superior Performance ◽

Wireless Sensor ◽

Prototype System ◽

Reconstruction Method ◽

Compressed Sampling ◽

Data Volume ◽

Simulation Results ◽

Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

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