It is proved in a reference (Fan, Lin, IEEE TIT, vol.67, pp.5016-5025) that the self-dual (LCD respectively) dihedral codes over a finite fieldFwith$|F|=q$are asymptotically good ifqis even (odd respectively). In this paper, we investigate the algebraic structures and the asymptotic properties of consta-dihedral codes overF, and show that: ifqis even or$4\,|\,(q-1)$, then the self-dual consta-dihedral codes are asymptotically good; otherwise, the LCD consta-dihedral codes are asymptotically good. And, with the help of a technique developed in this paper, some errors in the reference mentioned above are corrected.

Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) signals offer high information transfer rates and non-invasive brain-to-device connectivity, making them highly practical. In recent years, deep learning techniques, particularly convolutional neural network (CNN) architectures, have gained prominence in EEG (e.g., SSVEP) decoding because of their nonlinear modeling capabilities and autonomy from manual feature extraction. However, most studies using CNNs employ temporal signals as the input and cannot directly mine the implicit frequency information, which may cause crucial frequency details to be lost and challenges in decoding. By contrast, the prevailing supervised recognition algorithms rely on a lengthy calibration phase to enhance algorithm performance, which could impede the popularization of SSVEP based BCIs. To address these problems, this study proposes the Time-Frequency Attention Network (TFA-Net), a novel CNN model tailored for SSVEP signal decoding without the calibration phase. Additionally, we introduce the Frequency Attention and Channel Recombination modules to enhance ability of TFA-Net to infer finer frequency-wise attention and extract features efficiently from SSVEP in the time-frequency domain. Classification results on a public dataset demonstrated that the proposed TFA-Net outperforms all the compared models, achieving an accuracy of 79.00% $\pm$ 0.27% and information transfer rate of 138.82 $\pm$ 0.78 bits/min with a 1-s data length. TFA-Net represents a novel approach to SSVEP identification as well as time-frequency signal analysis, offering a calibration-free solution that enhances the generalizability and practicality of SSVEP based BCIs.

In the context of long-range infrared detection of small targets, complex battlefield environments with strong background effects, adverse weather conditions, and intense light interference pose significant challenges. These factors contribute to a low signal-to-noise ratio and limited target information in infrared imagery. To address these challenges, a feature enhancement network called target-focused enhancement network (TENet) is proposed with two key innovations: the dense long-distance constraint (DLDC) module and the autoaugmented copy-paste bounding-box (ACB) strategy. The DLDC module incorporates a self-attention mechanism to provide the model with a global understanding of the relationship between small targets and the backgrounds. By integrating a multiscale structure and using dense connections, this module effectively transfers global information to the deep layers, thus enhancing the semantic features. On the other hand, the ACB strategy focuses on data enhancement, particularly increasing the target representation. This approach addresses the challenge of distributional bias between small targets and background using context information for target segmentation, mapping augment strategies to 2-D space, and using an adaptive paste method to fuse the target with the background. The DLDC module and the ACB strategy complement each other in terms of features and data, leading to a significant improvement in model performance. Experiments on infrared datasets with complex backgrounds demonstrate that the proposed network achieves superior performance in detecting dim and small targets.

Let F be a field with cardinality$p^{\ell } $and$0\neq \lambda \in F$, and$0\le h\lt \ell $. Extending Euclidean and Hermitian inner products, Fan and Zhang introduced Galois$p^{h}$-inner product (DCC, vol.84, pp.473–492). In this paper, we characterize the structure of 2-quasi$\lambda $-constacyclic codes over F; and exhibit necessary and sufficient conditions for 2-quasi$\lambda $-constacyclic codes being Galois$p^{h}$-self-dual. With the help of a technique developed in this paper, we prove that, when$\ell $is even, the Hermitian self-dual 2-quasi$\lambda $-constacyclic codes are asymptotically good if and only if$\lambda ^{1+p^{\ell /2}}\!=1$. And, when$p^{\ell } \,{\cancel {\equiv }}\,3~({\mathrm { mod}}~4)$, the Euclidean self-dual 2-quasi$\lambda $-constacyclic codes are asymptotically good if and only if$\lambda ^{2}=1$.

Steady-state visual evoked potential (SSVEP) is a commonly used brain-computer interface (BCI) paradigm. The performance of cross-subject SSVEP classification has a strong impact on SSVEP-BCI. This study designed a cross subject generalization SSVEP classification model based on an improved transformer structure that uses domain generalization (DG). The global receptive field of multi-head self-attention is used to learn the global generalized SSVEP temporal information across subjects. This is combined with a parallel local convolution module, designed to avoid oversmoothing the oscillation characteristics of temporal SSVEP data and better fit the feature. Moreover, to improve the cross-subject calibration-free SSVEP classification performance, an DG method named StableNet is combined with the proposed convolutional transformer structure to form the DG-Conformer method, which can eliminate spurious correlations between SSVEP discriminative information and background noise to improve cross-subject generalization. Experiments on two public datasets, Benchmark and BETA, demonstrated the outstanding performance of the proposed DG-Conformer compared with other calibration-free methods, FBCCA, tt-CCA, Compact-CNN, FB-tCNN, and SSVEPNet. Additionally, DG-Conformer outperforms the classic calibration-required algorithms eCCA, eTRCA and eSSCOR when calibration is used. An incomplete partial stimulus calibration scheme was also explored on the Benchmark dataset, and it was demonstrated to be a potential solution for further high-performance personalized SSVEP-BCI with quick calibration.

Brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) have a broad application prospect owing to their multiple command output and high performance. Each harmonic component of SSVEP individually contains unique features, which can be utilized to enhance the recognition performance of SSVEP-based BCIs. However, the existing subband analysis methods for SSVEP, including those based on filter banks and existing mode decomposition methods, have limitations in extracting and utilizing independent harmonic components. This study proposes a sinusoidal signal assisted multivariate variational mode decomposition (SA-MVMD) algorithm that allows the constraint of the center frequencies and narrowband filtering structures of the intrinsic mode functions (IMFs) based on the prior frequency knowledge of the signal. It preserves the target information of the signal during decomposition while avoiding mode mixing and incorrect decomposition, thereby enabling the effective extraction of each independent harmonic component of SSVEP. Building on this, a SA-MVMD based task-related component analysis (SA-MVMD-TRCA) method is further proposed to fully utilize the features within the overall SSVEP as well as its independent harmonics, thereby enhancing the recognition performance. Testing on the public SSVEP Benchmark dataset demonstrates that the proposed method significantly outperforms the filter bank-based control methods. This study confirms the effectiveness of SA-MVMD and the potential of this approach, which analyzes and utilizes each independent harmonic of SSVEP, providing new strategies and perspectives for performance enhancement in SSVEP-based BCIs.

Eye tracking technology has become increasingly important in scientific research and practical applications. In the field of eye tracking research, analysis of eye movement data is crucial, particularly for classifying raw eye movement data into eye movement events. Current classification methods exhibit considerable variation in adaptability across different participants, and it is necessary to address the issues of class imbalance and data scarcity in eye movement classification. In the current study, we introduce a novel eye movement classification method based on cascade forest (EMCCF), which comprises two modules: 1) a feature extraction module that employs a multi-scale time window method to extract features from raw eye movement data; 2) a classification module that innovatively employs a layered ensemble architecture, integrating the cascade forest structure with ensemble learning principles, specifically for eye movement classification. Consequently, EMCCF not only enhanced the accuracy and efficiency of eye movement classification but also represents an advancement in applying ensemble learning techniques within this domain. Furthermore, experimental results indicated that EMCCF outperformed existing deep learning-based classification models in several metrics and demonstrated robust performance across different datasets and participants.

How to encode as many targets as possible with limited frequency resources is a grave problem that restricts the application of steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In the current study, we propose a novel block-distributed joint temporal-frequency-phase modulation method for a virtual speller based on SSVEP-based BCI. A 48-target speller keyboard array is virtually divided into eight blocks and each block contains six targets. The coding cycle consists of two sessions: in the first session, each block flashes at different frequencies while all the targets in the same block flicker at the same frequency; in the second session, all the targets in the same block flash at different frequencies. Using this method, 48 targets can be coded with only eight frequencies, which greatly reduces the frequency resources required, and average accuracies of 86.81 $\pm$ 9.41% and 91.36 $\pm$ 6.41% were obtained for both the offline and online experiments. This study provides a new coding approach for a large number of targets with a small number of frequencies, which can further expand the application potential of SSVEP-based BCI.

Virtual reality (VR) has huge potential to enable radically new applications, behind which spherical panoramic video processing is one of the backbone techniques. However, current VR systems reuse the techniques designed for processing conventional planar videos, resulting in significant energy inefficiencies. Our characterizations show that operations that are unique to processing 360° VR content constitute 40% of the total processing energy consumption.

During the development or maintenance of an Android app, the app developer needs to determine the app's security and privacy requirements such as permission requirements. Permission requirements include two folds. First, what permissions (i.e., access to sensitive resources, e.g., location or contact list) the app needs to request. Second, how to explain the reason of permission usages to users. In this paper, we focus on the multiple challenges that developers face when creating permission-usage explanations. We propose a novel framework, CLAP, that mines potential explanations from the descriptions of similar apps. CLAP leverages information retrieval and text summarization techniques to find frequent permission usages. We evaluate CLAP on a large dataset containing 1.4 million Android apps. The evaluation results outperform existing state-of-the-art approaches, showing great promise of CLAP as a tool for assisting developers and permission requirements discovery.

After Android 6.0 introduces the runtime-permission system, many apps provide runtime-permission-group rationales for the users to better understand the permissions requested by the apps. To understand the patterns of rationales and to what extent the rationales can improve the users' understanding of the purposes of requesting permission groups, we conduct a large-scale measurement study on five aspects of runtime rationales. We have five main findings: (1) less than 25% apps under study provide rationales; (2) for permission-group purposes that are difficult to understand, the proportions of apps that provide rationales are even lower; (3) the purposes stated in a significant proportion of rationales are incorrect; (4) a large proportion of customized rationales do not provide more information than the default permission-requesting message of Android; (5) apps that provide rationales are more likely to explain the same permission group's purposes in their descriptions than apps that do not provide rationales. We further discuss important implications from these findings.

A brain-computer interface (BCI) is a communication approach that permits cerebral activity to control computers or external devices. Brain electrical activity recorded with electroencephalography (EEG) is most commonly used for BCI. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is a data-driven time-frequency analysis method that can be applied to nonlinear and nonstationary EEG signals for BCI data processing. However, because white Gaussian noise occupies a broad range of frequencies, some redundant components are introduced. To solve this leakage problem, in this study, we propose using a sinusoidal assisted signal that occupies the same frequency ranges as the original signals to improve MEMD performance. To verify the effectiveness of the proposed sinusoidal signal assisted MEMD (SA-MEMD) method, we compared the decomposition performances of MEMD, NA-MEMD, and the proposed SA-MEMD using synthetic signals and a real-world BCI dataset. The spectral decomposition results indicate that the proposed SA-MEMD can avoid the generation of redundant components and over decomposition, thus, substantially reduce the mode mixing and misalignment that occurs in MEMD and NA-MEMD. Moreover, using SA-MEMD as a signal preprocessing method instead of MEMD or NA-MEMD can significantly improve BCI classification accuracy and reduce calculation time, which indicates that SA-MEMD is a powerful spectral decomposition method for BCI.

Establishing a high-accuracy and training-free brain-computer interface (BCI) system is essential for improving BCI practicality. In this study, we propose for the first time a training-free double-partial least-squares (D-PLS) model for steady-state visual evoked potential (SSVEP) detection that consists of double-layer PLS, a PLS spatial filter, and a PLS feature extractor. Electroencephalographic data from 11 healthy volunteers under four different visual stimulation frequencies were used to test the proposed method. Compared with commonly used spatial filters, minimum energy combination and average maximum contrast combination, the classification accuracies could be improved 2-10% by our proposed PLS spatial filter. Furthermore, our proposed PLS feature extractor achieved better performance than current feature extraction methods, namely power spectral density analysis, canonical correlation analysis, and the use of the least absolute shrinkage and selection operator. The average classification accuracy for our proposed D-PLS model exceeded [Formula: see text] when the signal time window was longer than 3.5 s and reached as high as [Formula: see text] when the time window was 5 s. Moreover, the D-PLS model can be easily set without training data, so it can be used widely in SSVEP-based BCI systems.