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Brain–computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI. [ABSTRACT FROM AUTHOR]

This paper presents a new theory and model of the teaching of decoding, reading and writing. The first part of the paper reviews a selection of influential models of learning to read and write that to varying degrees have been used as the basis for approaches to teaching, including the Simple View of Reading. As well as noting some strengths of the models in relation to children's learning, limitations are identified in terms of their applicability as models of teaching. The second part of the paper presents seven components that are central to teaching reading and writing derived from social, cultural and cognitive research and theory. Explanations for the relevance of the components are offered, and seminal and more recent research that underpin them summarised. The final part of the paper introduces a new theory and model of teaching, The Double Helix of Reading and Writing. It is argued that this model provides a rationale for a balanced approach to teaching, and an alternative to synthetic phonics. [ABSTRACT FROM AUTHOR]

This paper attempts to highlight the decoding capabilities of MDP convolutional codes over the erasure channel by defining them as discrete linear dynamical systems, with which the controllability property and the observability characteristics of linear system theory can be applied, in particular those of output observability, easily described using matrix language. Those are viewed against the decoding capabilities of MDS block codes over the same channel. Not only is the time complexity better but the decoding capabilities are also increased with this approach because convolutional codes are more flexible in handling variable-length data streams than block codes, where they are fixed-length and less adaptable to varying data lengths without padding or other adjustments. [ABSTRACT FROM AUTHOR]

In general, the problem of building optimal convolutional codes under a certain criteria is hard, especially when size field restrictions are applied. In this paper, we confront the challenge of constructing an optimal 2D convolutional code when communicating over an erasure channel. We propose a general construction method for these codes. Specifically, we provide an optimal construction where the decoding method presented in the bibliography is considered. [ABSTRACT FROM AUTHOR]

Image dehazing is an important direction of low-level visual tasks, and its quality and efficiency directly affect the quality of high-level visual tasks. Therefore, how to quickly and efficiently process hazy images with different thicknesses of fog has become the focus of research. This paper presents a multi-feature fusion embedded image dehazing network based on transmission guidance. Firstly, we propose a transmission graph-guided feature fusion enhanced coding network, which can combine different weight information and show better flexibility for different dehazing information. At the same time, in order to keep more detailed information in the reconstructed image, we propose a decoder network embedded with Mix module, which can not only keep shallow information, but also allow the network to learn the weights of different depth information spontaneously and re-fit the dehazing features. The comparative experiments on RESIDE and Haze4K datasets verify the efficiency and high quality of our algorithm. A series of ablation experiments show that Multi-weight attention feature fusion module (WA) module and Mix module can effectively improve the model performance. The code is released in https://doi.org/10.5281/zenodo.10836919. [ABSTRACT FROM AUTHOR]

DNA-based storage has been a promising technique of data storage, due to its high density and long duration. During synthesizing and sequencing of DNA storage, edit errors including insertions, deletions and substitutions are introduced inevitably. An effective way to reduce the error probability is to limit the content of G and C in DNA sequences to around 50%, which is called GC-balanced. To deal with edit errors, DNA sequences are also expected to have error-correcting capabilities. In this paper, GC globally balanced and GC locally balanced error-correcting codes are explicitly constructed, respectively. Inspired by repetition codes, the proposed codes are able to correct multiple edit errors. Furthermore, an efficient decoding algorithm applied for both codes is derived when only one kind of edit error occur. [ABSTRACT FROM AUTHOR]