Scene text image super-resolution (STISR) is crucial for improving the readability and recognition accuracy of low-resolution text images. Many previous methods have incorporated text prior information, such as character sequences or recognition features, into super-resolution frameworks. However, existing methods struggle to recover fine-grained text structures, often introducing artifacts or blurry edges due to insufficient high-frequency (HF) modeling and suboptimal use of text priors. Although some recent approaches incorporate wavelet-domain losses into the generator, they typically retain RGB-domain losses during adversarial training, limiting their ability to distinguish authentic text details from artifacts. To address this, we propose TPWGAN, a GAN-based STISR framework that introduces wavelet-domain losses in both the generator and discriminator. The generator is trained with fidelity losses on the HF wavelet subbands to enhance sensitivity to stroke-level variations, while the discriminator processes HF wavelet subbands fused with binary text region masks via a spatial attention mechanism, enabling semantically guided frequency-aware discrimination. Experiments on the TextZoom dataset and several real-world benchmarks show that TPWGAN achieves consistent improvements in visual quality and text recognition, particularly for challenging text instances with distortions or low resolution.

A model of impulsive octonion-valued fuzzy inertial neural networks (OVFINNs) with time-varying delays is established, and fixed-time (FXT) synchronization is investigated by using direct octonion approach. First, a new FXT stability lemma of impulsive systems is presented and the upper bound of settling time is estimated by using the average impulsive interval and comparison principle. Second, two inequalities on fuzzy logic are developed in the field of octonion. Third, novel lemmas are introduced based on octonion-valued norm and sign function to overcome the non associative and non commutative laws of octonion multiplication. Furthermore, two new nonlinear octonion-valued controllers are directly designed to induce the FXT synchronization. Then, according to the improving FXT stability lemma and designed controllers, some novel sufficient conditions are given to ensure FXT synchronization of OVFINNs. Finally, two numerical simulations are given to demonstrate the correctness of the theoretical results and the effectiveness of FXT synchronization in secure communication.

Insomnia is the most common sleep disorder linked with adverse long-term medical and psychiatric outcomes. Automatic sleep staging plays a crucial role in aiding doctors to diagnose insomnia disorder. Only a few studies have been conducted to develop automatic sleep staging methods for insomniacs, and most of them have utilized transfer learning methods, which involve pre-training models on healthy individuals and then fine-tuning them on insomniacs. Unfortunately, significant differences in feature distribution between the two subject groups impede the transfer performance, highlighting the need to effectively integrate the features of healthy subjects and insomniacs. In this paper, we propose a dual-teacher cross-domain knowledge transfer method based on the feature-based knowledge distillation to improve the performance of sleep staging for insomniacs. Specifically, the insomnia teacher directly learns from insomniacs and feeds the corresponding domain-specific features into the student network, while the health domain teacher guide the student network to learn domain-generic features. During the training process, we adopt the OFD (Overhaul of Feature Distillation) method to build the health domain teacher. We conducted the experiments to validate the proposed method, using the Sleep-EDF database as the source domain and the CAP-Database as the target domain. The results demonstrate that our method surpasses advanced techniques, achieving an average sleep staging accuracy of 80.56% on the CAP-Database. Furthermore, our method exhibits promising performance on the private dataset.

Electroencephalography (EEG) is a commonly used clinical approach for the diagnosis of epilepsy which is a life-threatening neurological disorder. Many algorithms have been proposed for the automatic detection of epileptic seizures using traditional machine learning and deep learning. Although deep learning methods have achieved great success in many fields, their performance in EEG analysis and classification is still limited mainly due to the relatively small sizes of available datasets. In this paper, we propose an automatic method for the detection of epileptic seizures based on deep metric learning which is a novel strategy tackling the few-shot problem by mitigating the demand for massive data. First, two one-dimensional convolutional embedding modules are proposed as a deep feature extractor, for single-channel and multichannel EEG signals respectively. Then, a deep metric learning model is detailed along with a stage-wise training strategy. Experiments are conducted on the publicly-available Bonn University dataset which is a benchmark dataset, and the CHB-MIT dataset which is larger and more realistic. Impressive averaged accuracy of 98.60% and specificity of 100% are achieved on the most difficult classification of interictal (subset D) vs ictal (subset E) of the Bonn dataset. On the CHB-MIT dataset, an averaged accuracy of 86.68% and specificity of 93.71% are reached. With the proposed method, automatic and accurate detection of seizures can be performed in real time, and the heavy burden of neurologists can be effectively reduced.

Summary Epilepsy seriously damages the physical and mental health of patients. Detection of epileptic EEG signals in different periods can help doctors diagnose the disease. The change of frequency components during epilepsy seizures is obvious, and there may be noises in epilepsy EEG signals. Moreover, epileptic seizures are closely related to the release of neuronal spiking in the brain. In this paper, we propose an approach for epilepsy period classification based on combination feature extraction methods and spiking swarm intelligent optimization classification algorithm. First, combination feature extraction methods take in account both the time‐frequency features and principal component features of epilepsy. The time‐frequency features are obtained by WPT or STFT‐PSD, and noises are removed while extracting principal component features by PCA. Second, spiking swarm intelligent optimization classification algorithm takes advantage of individual cooperation and information interaction with strong robustness. Its simulated neurons are closer to reality, which consider more information and obtain stronger computing power. The experimental results show that the average classification accuracy of the proposed method can reach 98.95% and the highest classification accuracy can reach 100%. Compared with other methods, the proposed method has the best classification performance.

In machine learning and data mining applications, clustering is a critical task for knowledge discovery that attract attentions from large quantities of researchers. Generally, with the help of label information, supervised learning methods have more flexible structure and better result than unsupervised learning. However, supervised learning is infeasible for clustering task. In this paper, to fill the gap between clustering and supervised learning, the proposed clustering methods introduce the exemplars discriminative information into a supervised learning. To build the effective objective function, a strategy for reducing intracluster distance and increasing intercluster distance is introduced to form a unified optimization objective function. With initially setting the clustering centers, the data that near the centers are selected as exemplars to indicate the ground truth of different classes. Discriminative learning is then introduced to learn the partition hyperplane and classify all the data into different classes. New clustering centers are calculated for selecting new exemplars alternately. Using the proposed algorithms, the unsupervised K -means clustering problem is effectively solved from the perceptive of optimization. Feature mapping is also introduced to improve the performance by reducing the intercluster distance. A novel framework for exploring discriminative information from unsupervised data is provided. The proposed algorithms outperform the state-of-the-art approaches on a wide range of benchmark datasets in terms of accuracy.

Target searching, i.e. fast locating target objects in images or videos, has attracted much attention in computer vision. A comprehensive understanding of factors influencing human visual searching is essential to design target searching algorithms for computer vision systems. In this paper, we propose a combined model to generate scan paths for computer vision to follow to search targets in images. The model explores and integrates three factors influencing human vision searching, top-down target information, spatial context and bottom-up visual saliency, respectively. The effectiveness of the combined model is evaluated by comparing the generated scan paths with human vision fixation sequences to locate targets in the same images. The evaluation strategy is also used to learn the optimal weighting coefficients of the factors through linear search. In the meanwhile, the performances of every single one of the factors and their arbitrary combinations are examined. Through plenty of experiments, we prove that the top-down target information is the most important factor influencing the accuracy of target searching. The effects from the bottom-up visual saliency are limited. Any combinations of the three factors have better performances than each single component factor. The scan paths obtained by the proposed model are optimal, since they are most similar to the human vision fixation sequences.

This paper presents an improved neural architecture for gaze movement control in target searching. Compared with the four-layer neural structure proposed in [14], a new movement coding neuron layer is inserted between the third layer and the fourth layer in previous structure for finer gaze motion estimation and control. The disadvantage of the previous structure is that all the large responding neurons in the third layer were involved in gaze motion synthesis by transmitting weighted responses to the movement control neurons in the fourth layer. However, these large responding neurons may produce different groups of movement estimation. To discriminate and group these neurons' movement estimation in terms of grouped connection weights form them to the movement control neurons in the fourth layer is necessary. Adding a new neuron layer between the third layer and the fourth lay is the measure that we solve this problem. Comparing experiments on target locating showed that the new architecture made the significant improvement.

Pulse-coupled neuron networks (PCNN) can be efficiently applied to image segmentation. However, the performance of segmentation depends on the suitable PCNN parameters, which are obtained by manual experiment, and the effect of the segmentation needs to be improved for images with noise. In this paper, dynamic mechanism based PCNN(DMPCNN) is brought forward to simulate the integrate-and-fire mechanism, and it is applied to segment images with noise effectively. Parameter selection is based on dynamic mechanism. Experimental results for image segmentation show its validity and robustness.