Concurrent and complex aerial missions with multiple targets exceed the capabilities of a single cooperative formation of high-speed flight vehicles (HSFVs). To address this challenge, this article decomposes a fleet of HSFVs (subject to multiple compounding factors, including unknown aerodynamic disturbances, unmodeled or parametric uncertainties, actuator faults, and potential intervehicle collisions) into several subgroups and develops a recurrent neural network (RNN) online learning-based prescribed-time safe and robust cooperative group formation control protocol under dynamic event-triggered communication. A distributed prescribed-time event-triggered estimator (DP-TE-TE) is first developed to drive all HSFVs to acquire the convex hull information (i.e., input, velocity, and position) spanned by multiple virtual leader vehicles (VLVs) before grouping or the input, velocity, and position information of their respective single VLV within the group after grouping. Then, based on the constraint-following theory, the safety distance inequality between any potentially colliding pair of HSFVs, along with the first-order differential equation involving the formation position tracking error, is converted into collision motion constraints and prescribed-time trajectory tracking constraints, respectively. To enhance the flight control performance of the swarm, an RNN is constructed for each HSFV to learn the unknown nonlinear function induced by multiple compounding factors, thereby providing online compensation for the subsequent control design. Finally, by integrating the constraint-following errors derived from collision motion constraints and prescribed-time trajectory tracking constraints, the RNN compensation term, and the estimated information, the prescribed-time safe and robust cooperative group formation control scheme (P-TSRCGFCS) is proposed. In the simulation examples, the effectiveness of the proposed algorithms is verified by dividing 12 HSFVs and three VLVs into three subgroups to perform the desired cooperative group formation task.

Hand-based multimodal biometrics have attracted significant attention due to their high security and performance. However, existing methods fail to adequately decouple various hand biometric traits, limiting the extraction of unique features. Moreover, effective feature extraction for multiple hand traits remains a challenge. To address these issues, we propose a novel method for the precise decoupling of hand multimodal features called 'Normalized-Full-Palmar-Hand' and construct an authentication system based on this method. First, we propose HSANet, which accurately segments various hand regions with diverse backgrounds based on low-level details and high-level semantic information. Next, we establish two hand multimodal biometric databases with HSANet: SCUT Normalized-Full-Palmar-Hand Database Version 1 (SCUT_NFPH_v1) and Version 2 (SCUT_NFPH_v2). These databases include full hand images, semantic masks, and images of various hand biometric traits obtained from the same individual at the same scale, totaling 157,500 images. Third, we propose the Full Palmar Hand Authentication Network framework (FPHandNet) to extract unique features of multiple hand biometric traits. Finally, extensive experimental results, performed via the publicly available CASIA, IITD, COEP databases, and our proposed databases, validate the effectiveness of our methods.

Contactless palm vein has attracted significant attention for its high security, stability, and user-friendliness. However, current contactless palm vein recognition predominantly relies on databases collected from platforms with spatial and temporal constrained design, which inadequately reflect relaxed palm vein imaging circumstances. This paper proposes a novel manner called on-the-fly palm vein that frees the user’s palm from spatial and temporal constraints, enabling palm vein recognition under unconstrained and weak-cooperative conditions. Firstly, Designing efficient and user-friendly palm vein imaging and authentication via two dynamic palm motions is proposed, resulting in an on-the-fly palm vein recognition platform. Next, a large-scale and challenging palm vein database, SCUT Palm Vein Database Version 1 (SCUT_PV_v1), is constructed. It is the first palm vein database with images collected under unconstrained and weak-cooperative conditions, encompassing a wider range of palm pose variations, grayscale variations, and lower-quality images. Finally, a lightweight and efficient Adaptive Margin Palm Vein Authentication Network (AMPVNet) is proposed as a baseline for the SCUT_PV_v1, where a vein pattern-specific convolutional neural network (CNN) is designed to extract features and a tailored online data augmentation method, combining Random Perspective Transformation (RPT) with Random Grayscale Adjustment (RGA), is proposed to enrich the diversify of out-of-plane palm pose and grayscale variations. Extensive experimental results demonstrate the effectiveness of our proposed methods. As the first work for palm vein recognition under unconstrained and weak-cooperation conditions, the AMPVNet achieves a promising accuracy and computation result while maintaining robustness to palm pose and grayscale variations. The SCUT_PV_ v1 database will be public at https://github.com/SCUT-BIP-Lab/SCUT_PV_v1.