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The normalized subband adaptive filtering algorithm provides fast convergence rate for colored input signals as compared to the normalized least mean square algorithm, but it suffers from a poor convergence issue in the α -stable noise. In light of this, the normalized subband p-norm (NSPN) algorithm, which is based on the least mean p-power error (MPE) criterion, is proposed in this study. This technique is not only robust against impulsive noise samples, but it also maintains a fast convergence rate when colored input signals are used. In addition to this, we develop both the steady-state and the transient models of the NSPN algorithm and provide some insights. Then, in order to solve the problem of making a choice regarding the order p in the NSPN algorithm, we design an autonomous system and come up with the NSPN algorithm with a variable p-norm (VP-NSPN). In addition, we offer the TFC-based VP-NSPN algorithm with a fast convergence rate and low steady-state misadjustment simultaneously by making use of the tap-weights feedback-based convex combination (TFC) scheme. In conclusion, simulation results on system identification and acoustic echo cancellation are undertaken in order to validate the superiority of the proposed algorithms and validate the usefulness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Frequency-domain adaptive filter (FDAF) algorithms have been widely used in many fields in virtue of its fast convergence and low computational complexity. However, FDAF algorithms with fixed step-size cannot balance convergence rate and steady-state misadjustment. In this paper, we propose the switching step-size based FDAF (SSS-FDAF) algorithm that selects the optimal step-size at each iteration for the FDAF update by comparing the mean-square deviation (MSD) trends with different step-sizes, to obtain fast convergence and low steady-state misadjustment at the same time. Furthermore, a novel reset strategy is designed for guaranteeing the tracking capability of the proposed algorithm. Computer simulations of colored signals and real-world signals have demonstrated the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The projection order of the affine projection algorithm (APA) affects not only the convergence performance but also the steady-state behavior. Therefore, for solving the fixed projection order of the conventional APA, we develop a new scheme of variable projection order (VPO) that enables the evolution from the maximum projection order to the minimum one by resorting to the adaptation of the mixing factor, thereby yielding a novel APA with VPO. Moreover, for dealing with sparse systems with fast convergence, we extend the proposed VPO scheme to proportionate APA and develop a VPO-based memory proportionate APA. Simulation results in the scenarios of system identification and acoustic echo cancellation have shown that the proposed algorithms have a faster convergence rate and lower steady-state error as compared to their original counterparts and the existing VPO algorithms. [ABSTRACT FROM AUTHOR]

DNA 4 mC plays a crucial role in the genetic expression process of organisms. However, existing deep learning algorithms have shortcomings in the ability to represent DNA sequence features. In this paper, we propose a 4 mC site identification algorithm, DNABert-4mC, based on a fusion of the pruned pre-training DNABert-Pruning model and artificial feature encoding to identify 4 mC sites. The algorithm prunes and compresses the DNABert model, resulting in the pruned pre-training model DNABert-Pruning. This model reduces the number of parameters and removes redundancy from output features, yielding more precise feature representations while upholding accuracy.Simultaneously, the algorithm constructs an artificial feature encoding module to assist the DNABert-Pruning model in feature representation, effectively supplementing the information that is missing from the pre-trained features. The algorithm also introduces the AFF-4mC fusion strategy, which combines artificial feature encoding with the DNABert-Pruning model, to improve the feature representation capability of DNA sequences in multi-semantic spaces and better extract 4 mC sites and the distribution of nucleotide importance within the sequence. In experiments on six independent test sets, the DNABert-4mC algorithm achieved an average AUC value of 93.81%, outperforming seven other advanced algorithms with improvements of 2.05%, 5.02%, 11.32%, 5.90%, 12.02%, 2.42% and 2.34%, respectively. [Display omitted] • Train a new pruning pretraining model on DNA sequences to extract feature information. • The introduction of manually assisted features has expanded the search space for sequence feature. • Attention fusion strategy better promotes the fusion output of features from different dimensions. • The complementary nature of machine features and manual features better fits the target features. [ABSTRACT FROM AUTHOR]

Edge computing is a key technology in computational imaging, where the algorithms determine reconstruction quality and reconstruction speed. Recovering spectral information from ill-conditioned data of coded aperture snapshots is a novel but challenging solution in spectral imaging for dynamic scenes. Further study is necessary to enhance the edge computing power of computational spectral programs. Deep unfolding networks have made considerable headway in this direction, but the existing methods still have two drawbacks: (1) The 3D spectral cube exhibits long-range dependency and non-local self-similarity in both spatial and spectral dimensions, yet learn the global features of spatial and spectral is difficult, which remains to be investigated. (2) The global features degenerate as the number of iterations increases. To solve the above problems, this paper proposes a Momentum Accelerated Unfolding Network with Spectral–Spatial prior (MAUNSS). Specifically, a denoising module based on a spectral–spatial transformer is designed, which uses both sparse spectral features to recover global information and dense spatial features to enhance detail texture. Within such a framework, cross-stage feature transmission channels transmitting features across spectral and spatial transformers at different stages are built to avoid feature degeneration. In order to increase the training speed and save the time and cost associated with the iterative approach, we improve it by means of the momentum acceleration module. Supplemented by a re-projection loss, a technique introduced from 3D measurement, the accuracy is further improved. To the best of our knowledge, the proposed method is an attempt to use momentum to expedite the convergence of unfolding networks. Extensive experiments demonstrate that our proposed method outperforms previous state-of-the-art methods by more than 1.94 dB. • MAUN with learnable hyper-parameters and Momentum methods ensures better accuracy. • A reprojection loss is proposed to improve the reconstruction accuracy of HIS. • MAUNSS exceeds the previous SOTA methods while requiring less memory costs. [ABSTRACT FROM AUTHOR]