Random Fourier Feature Based Deep Learning for Wireless Communications

This paper provides analytical results on fixed kernel width based RFF based DL (RFF-DL). The derived analysis and the presented case-studies indicate the RFF-DL's robustness to kernel-width initializations, and offers improved convergence in the low-data regime.

Multi-Objective Grasshopper Optimization Based MPPT and VSC Control of Grid-Tied PV-Battery System

This article presents the control of a three-phase three-wire (3P-3W) dual-stage grid-tied PV-battery storage system using a multi-objective grass-hopper optimization (MOGHO) algorithm. The voltage source converter (VSC) control of the presented system is implemented with adaptive kernel width sixth-order maximum correntropy criteria (AKWSOMCC) and maximum power point tracking (MPPT) control is accomplished using the variable step-size incremental conductance (VSS-InC) technique. The proposed VSC control offers lower mean square error and better accuracy, convergence rate and speed as compared to peer adaptive algorithms, i.e., least mean square (LMS), least mean fourth (LMF), maximum correntropy criteria (MCC), etc. The adaptive Gaussian kernel width is a function of the error signal, which changes to accommodate and filter Gaussian and non-Gaussian noise signals in each iteration. The VSS-InC based MPPT is provided with a MOGHO based modulation factor for better and faster tracking of the maximum power point during changing solar irradiation. Similarly, an optimized gain conventional PI controller regulates the DC bus to improve the power quality, and DC link stability during dynamic conditions. The optimized DC-link generates an accurate loss component of current, which further improves the VSC capability of fundamental load current component extraction. The VSC is designed to perform multi-functional operations, i.e., harmonics elimination, reactive power compensation, load balancing and power balancing at point of common coupling during diverse dynamic conditions. The MOSHO based VSS-InC, and DC bus performance is compared to particle swarm optimization (PSO) and genetic algorithm (GA). The proposed system operates satisfactorily as per IEEE519 standards in the MATLAB simulation environment.

Random Fourier Feature Based Deep Learning for Wireless Communications

This paper provides analytical results on fixed kernel width based RFF based DL (RFF-DL). The derived analysis and the presented case-studies indicate the RFF-DL's robustness to kernel-width initializations, and offers improved convergence in the low-data regime.

Genome-Wide Association Study of Kernel Traits in Aegilops tauschii

Aegilops tauschii is the diploid progenitor of the D subgenome of hexaploid wheat (Triticum aestivum L.). Here, the phenotypic data of kernel length (KL), kernel width (KW), kernel volume (KV), kernel surface area (KSA), kernel width to length ratio (KWL), and hundred-kernel weight (HKW) for 223 A. tauschii accessions were gathered across three continuous years. Based on population structure analysis, 223 A. tauschii were divided into two subpopulations, namely T-group (mainly included A. tauschii ssp. tauschii accessions) and S-group (mainly included A. tauschii ssp. strangulata). Classifications based on cluster analysis were highly consistent with the population structure results. Meanwhile, the extent of linkage disequilibrium decay distance (r2 = 0.5) was about 110 kb and 290 kb for T-group and S-group, respectively. Furthermore, a genome-wide association analysis was performed on these kernel traits using 6,723 single nucleotide polymorphism (SNP) markers. Sixty-six significant markers, distributed on all seven chromosomes, were identified using a mixed linear model explaining 4.82–13.36% of the phenotypic variations. Among them, 15, 28, 22, 14, 21, and 13 SNPs were identified for KL, KW, KV, KSA, KWL, and HKW, respectively. Moreover, six candidate genes that may control kernel traits were identified (AET2Gv20774800, AET4Gv20799000, AET5Gv20005900, AET5Gv20084100, AET7Gv20644900, and AET5Gv21111700). The transfer of beneficial genes from A. tauschii to wheat using marker-assisted selection will broaden the wheat D subgenome improve the efficiency of breeding.

An adaptive kernel width convex combination method for maximum correntropy criterion

Recently, the maximum correntropy criterion (MCC) has been successfully applied in numerous applications regarding nonGaussian data processing. MCC employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the adaptive filtering. However, determining the optimal value for such parameter is not always a trivial task. Within this context, this paper proposes a novel method called adaptive convex combination maximum correntropy criterion (ACCMCC), which combines an adaptive kernel algorithm with convex combination techniques. ACCMCC takes advantage from a convex combination of two adaptive MCC-based filters, whose kernel widths are adjusted iteratively as a function of the minimum error value obtained in a predefined estimation window. Results obtained in impulsive noise environment have shown that the proposed approach achieves equivalent convergence rates but with increased accuracy and robustness when compared with other similar algorithms reported in literature.