In recent years, uncrewed aerial vehicles (UAVs) have become increasingly prevalent for collecting environmental data from various wireless sensors. However, existing research on employing UAVs to collect data from wireless sensors has often ignored the heterogeneous requirements of sensors. In this paper, we investigate joint deployment, user association, and power allocation for data collection in the UAV-assisted wireless sensor network to accommodate the heterogeneous requirements of sensors, where a novel satisfaction function is designed for three types of sensors, including sensors with delay requirements, sensors with energy consumption requirements, and sensors with both delay and energy consumption requirements. Leveraging the satisfaction function, we formulate the optimization problem aimed at jointly optimizing the positions of UAVs, the association between sensors and UAVs, and the power allocation of sensors to maximize overall satisfaction of sensors. In order to effectively address the considered problem, we decompose it into two subproblems, i.e., joint UAV deployment and user association subproblem, and transmission power allocation subproblem. An enhanced human evolutionary algorithm is developed to tackle the joint UAV deployment and user association subproblem, and the Lagrange dual method and gradient descent method are employed to solve the transmission power allocation subproblem. The suboptimal solution is achieved by iteratively addressing the two subproblems until convergence of the proposed enhanced Lagrange and gradient descent-based human evolutionary optimization algorithm is attained. Extensive simulations demonstrate the effectiveness of the proposed algorithm in enhancing overall satisfaction of sensors, underscoring its significant advantages in managing heterogeneous network environments.

Electroencephalography (EEG) is vital for brain-computer interfaces (BCIs) due to its non-invasive approach and high temporal resolution data capabilities, amid challenges such as data scarcity and the need for extensive labeling. Significant inter-individual variability in EEG signals further limits model generalization. Concurrently, the use of self-supervised pre-training, particularly through masked modeling, is gaining traction in time series analysis to mitigate labeling costs. Although this method involves reconstructing masked signal from unmasked series, random masking can disrupt critical temporal variations, complicating effective representation learning. We thus introduce SSL-MEMI, a novel self-supervised contrastive learning framework for masked EEG motor imagery modeling, integrating Domain Adaptive Alignment (DAA) and Multi-View Temporal-spatial Attention module (MTSA) to effectively handle EEG variability. This framework utilizes manifold-based masking to reconstruct original sequences from masked series, thereby enhancing classification accuracy. When tested on the BCI Competition IV and High Gamma datasets, SSL-MEMI outperforms existing methods, achieving top accuracies and demonstrating superior domain adaptation through reduced Global ${\mathcal{A}}$-distance scores. This study advances EEG classification and indicates broader applications for self-supervised learning in biomedical signal processing. The source code is available at https://github.com/KunKun-Zhang/SSL-MEMI.git.

Fine-grained leaf image retrieval (FGLIR) is a new unsupervised pattern recognition task in content-based image retrieval (CBIR). It aims to distinguish varieties/cultivars of leaf images within a certain plant species and is more challenging than general leaf image retrieval task due to the inherently subtle differences across different cultivars. In this study, we for the first time investigate the possible way to mine the spatial structure and contextual information from the activation of the convolutional layers of CNN networks for FGLIR. For achieving this goal, we design a novel geometrical structure, named Triplet Patch-Pairs Composite Structure (TPCS), consisting of three symmetric patch pairs segmented from the leaf images in different orientations. We extract CNN feature map for each patch in TPCS and measure the difference between the feature maps of the patch pair for constructing local deep self-similarity descriptor. By varying the size of the TPCS, we can yield multi-scale deep self-similarity descriptors. The final aggregated local deep self-similarity descriptors, named Structural Deep Patch Representation (SDePR), not only encode the spatial structure and contextual information of leaf images in deep feature domain, but also are invariant to the geometrical transformations. The extensive experiments of applying our SDePR method to the public challenging FGLIR tasks show that our method outperforms the state-of-the-art handcrafted visual features and deep retrieval models.

Decentralized learning (DL) has recently employed local updates to reduce the communication cost for general non-convex optimization problems. Specifically, local updates require each node to perform multiple update steps on the parameters of the local model before communicating with others. However, most existing methods could be highly sensitive to data heterogeneity (i.e., non-iid data distribution) and adversely affected by the stochastic gradient noise. In this paper, we propose DSE-MVR to address these problems. Specifically, DSE-MVR introduces a dual-slow estimation strategy that utilizes the gradient tracking technique to estimate the global accumulated update direction for handling the data heterogeneity problem; also for stochastic noise, the method uses the mini-batch momentum-based variance-reduction technique. We theoretically prove that DSE-MVR can achieve optimal convergence results for general non-convex optimization in both iid and non-iid data distribution settings. In particular, the leading terms in the convergence rates derived by DSE-MVR are independent of the stochastic noise for large-batches or large partial average intervals (i.e., the number of local update steps). Further, we put forward DSE-SGD and theoretically justify the importance of the dual-slow estimation strategy in the data heterogeneity setting. Finally, we conduct extensive experiments to show the superiority of DSE-MVR against other state-of-the-art approaches. We provide our code here: https://anonymous.4open.science/r/DSE-MVR-32B8/.

Accurate and effective segmentation of breast masses plays an important role in the early stages of breast cancer treatment. However, the irregular shape of mass, the blurred mass boundary and speckle noise in breast ultrasound (BUS) images make automatic segmentation still challenging. In this paper, we propose a classification task assist (CTA) module for boosting the performance of the commonly used deep segmentation models on BUS images. This module can be easily inserted into representative segmentation models to focus on a learning task of BUS image-level classification. The incorporation of this classification learning task enable the segmentation model achieve additional supervision information to enhance the extraction of semantic context information and the localization of segmentation object. We added the CTA module to four state-of-the-art segmentation models, including codec and non-codec structures, and the experimental results show that the insertion of the CTA module into the original model results in the lowest improvement of 1.48% and the highest improvement of 4.29% in the intersection over union (IoU) metric.

Millimeter-wave (mmWave) communication is one of the key technologies to drive future high-capacity communication in the sixth generation (6G) network. However, the easily blocking characteristics of mmWave communication links hinder the reliable transmission in mmWave communication. To improve the reliability of mmWave transmission, we investigate the joint coordinated multi-beam selection and power control scheme that maximizes average sum rate of UEs. To efficiently solve this problem, we recast it as a potential game and propose a best response based approach to find the Nash equilibrium (NE). Simulation results have shown that the proposed solution has better average sum rate, and can achieve more reliable mmWave communications.