Although face recognition has achieved great success due to deep learning, many factors may affect the quality of faces in the wild, such as pose changes, age variations, and light changes, which can seriously affect the performance of face recognition. In this work, an effective approach called Efficient Lightweight Attention Networks (ELANet) is proposed to address the challenge brought by the impacts of poses and ages on face recognition performance. First, similar local patches are particularly important when the geometry and appearance of a face change drastically. To alleviate this challenge, spatial attention is used to capture important locally similar patches and channel attention is employed to focus on features with different levels of importance. Furthermore, Efficient Fusion Attention (EFA) module is designed to achieve better performance, which can alleviate the computational effort required by fusing spatial and channel attention. Second, multi-scale features learning is necessary because pose or large expression changes can cause similar recognition regions to appear at different scales. For this purpose, pyramid multi-scale module is presented, which constructs a series of features at different scales via pooling operations. Third, to unite low-level local detail information with high-level semantic information, the features of different layers are fused by Adaptively Spatial Feature Fusion (ASFF) instead of simply utilizing addition or concatenation. Compared to recent lightweight networks, the ELANet improved performance by 1.83% and 2.17% on the CPLFW and VGG2_FP datasets, respectively, and by 0.92% on the CALFW dataset. The ELANet addresses the challenge regarding the impacts of poses and ages on face recognition performance with few parameters and computational effort and is suitable for embedded and mobile devices.

Face recognition has achieved great success due to the development of deep convolutional neural networks (DCNNs) and loss functions based on margin. However, complex DCNNs bring a large number of parameters as well as computational effort, which pose a significant challenge to resource-constrained embedded devices. Meanwhile, the popular margin-based loss functions all introduce only one type of margin and cannot further introduce a larger margin to achieve tighter classification boundary. In contrast to the common approach, we believe that additive and multiplicative margins should be used jointly to introduce larger margins from the margin perspective. Therefore, we propose a new margin-based loss function called UnifiedFace. First, we introduce an additive margin in the target angle activation function. Second, we add a multiplicative margin in the non-target angle. UnifiedFace introduces both additive and multiplicative margins, allowing for the introduction of large margins to achieve more compact intra-class variance and closer separated inter-class variance. In addition, we specifically design efficient face recognition models called GhostFaceNet for resource-constrained embedded devices. Experimental results demonstrate that UnifiedFace achieves state-of-the-art performance or performance competed with popular methods in training datasets of different sizes. UnifiedFace achieves optimal performance in models of varying complexity. Moreover, competitive results are achieved in the large-scale test set IJBB/C, especially the state-of-the-art performance achieved in TAR ($FAR=1e-6$). GhostFaceNet can significantly improve operational efficiency without significantly degrading recognition performance, making it ideal for embedded devices with limited resources.

For the state estimation problem of a multi-source localization nonlinear system with unknown and bounded noise, a distributed sequential ellipsoidal intersection fusion estimation algorithm based on the dual set-membership filtering method is proposed to ensure the reliability of the localization system. First, noise with unknown and bounded characteristics is modeled by using bounded ellipsoidal regions. At the same time, local estimators are designed at the sensor link nodes to filter out the noise interference in the localization system. The local estimator is designed using the dual set-membership filtering algorithm. It uses the dual principle to find the minimizing ellipsoid that can contain the nonlinear function by solving the optimization problem with semi-infinite constraints, and a first-order conditional gradient algorithm is used to solve the optimization problem with a low computational complexity. Meanwhile, the communication confusion among multiple sensors causes the problem of unknown correlation. The obtained estimates of local filters are fused at the fusion center by designing a distributed sequential ellipsoid intersection fusion estimation algorithm to obtain more accurate fusion localization results with lower computational cost. Finally, the stability and reliability of the proposed distributed fusion algorithm are verified by designing a simulation example of a multi-source nonlinear system.

Semantic segmentation is a crucial task in computer vision with broad applications in autonomous driving, intelligent surveillance, drone vision, and other fields. The current high-precision segmentation models generally suffer from large parameter sizes, high computational complexity, and substantial memory consumption, which limits their efficient deployment in embedded systems and resource-constrained environments. In addition, traditional methods exhibit significant limitations in handling multi-scale targets and object boundaries, particularly during deep feature extraction, where the loss of shallow spatial information often results in blurred boundaries and reduced segmentation accuracy. To address these challenges, we propose EfficientSegNet, a lightweight and efficient semantic segmentation network. This network features an innovative architecture that integrates the Cascade-Attention Dense Field (CADF) module and the Dynamic Weighting Feature Fusion (DWF) module, effectively reducing computational resource requirements while balancing global semantic information and local detail recovery. Experimental results demonstrate that EfficientSegNet achieves an excellent balance between segmentation accuracy and computational efficiency on multiple public datasets, providing robust support for real-time segmentation tasks and applications on resource-constrained devices.

This paper investigates the problem of distributed ellipsoidal intersection (DEI) fusion estimation for linear time-varying multi-sensor complex systems with unknown input disturbances and measurement data transmission delays. For the problem with external unknown input disturbance signals, a non-informative prior distribution is used to model the problem. A set of independent random variables obeying Bernoulli distribution is also used to describe the situation of measurement data transmission delay caused by network channel congestion, and appropriate buffer areas are added at the link nodes to retrieve the delayed transmission data values. For multi-sensor systems with complex situations, a minimum mean square error (MMSE) local estimator is designed in a Bayesian framework based on the maximum a posteriori (MAP) estimation criterion. In order to deal with the unknown correlations among the local estimators and to select the fusion estimator with lower computational complexity, the fusion estimator is designed using ellipsoidal intersection (EI) fusion technique, and the consistency of the estimator is demonstrated. In this paper, the difference between DEI fusion and distributed covariance intersection (DCI) fusion and centralized fusion estimation is analyzed by a numerical example, and the superiority of the DEI fusion method is demonstrated.

There are not many high-precision, portable digital compass solutions available right now that can enhance combined navigation systems’ overall functionality. Additionally, there is a dearth of writing about these products. This is why a tunnel magnetoresistance (TMR) sensor-based high-precision portable digital compass system is designed. First, the least-squares method is used to compensate for compass inaccuracy once the ellipsoid fitting method has corrected manufacturing and installation errors in the digital compass system. Second, the digital compass’s direction angle data is utilized to offset the combined navigation system’s mistake. The final objective is to create a high-performing portable TMR digital compass system that will enhance the accuracy and stability of the combined navigation system (abbreviated as CNS). According to the experimental results, the digital compass’s azimuth accuracy was 4.1824° before error compensation and 0.4580° after it was applied. The combined navigation system’s path is now more accurate overall and is closer to the reference route than it was before the digital compass was added. Furthermore, compared to the combined navigation route without the digital compass, the combined navigation route with the digital compass included is more stable while traveling through the tunnel. It is evident that the digital compass system’s design can raise the integrated navigation system’s accuracy and stability. The integrated navigation system’s overall performance may be somewhat enhanced by this approach.