3D object detection has achieved significant progress in outdoor LiDAR point clouds, however, the inherent irregularity and varying sparsity distribution of point occupancy present a key challenge. Existing transformer-based 3D detectors often treat all tokens within the attention window as equally important, regardless of varying sparsity, which not only fails to address the disparities between the varying beam densities but also results in increased memory and computational costs. In this work, we propose an adaptive structure-aware cascaded transformer (ASCFormer) that dynamically captures density-insensitive multiscale structure features to model long-range dependencies via cascaded learning. Our ASCFormer detector includes an adaptive structure-aware token learning module that embeds voxel-level foreground probability and grid-level local density into the grid tokens to enhance structural perception capability. Moreover, we integrate these factors to compute significance scores, which are then utilized in inverse transform sampling to select a subset of multiscale tokens with varying receptive field sizes. To improve the training convergence of the window-based transformer in 3D voxel space, we employ cascaded learning via cross-stage attention to enhance the feature representation capability and refine the localization precision of 3D bounding boxes. This design of structure-aware reweighting effectively enhances the cascade paradigm, making to more adaptable to the varying sparsity distribution of point clouds. Extensive experiments on the KITTI and Waymo Open datasets demonstrate that the proposed ASCFormer detector achieves exceptional performance compared with state-of-the-art 3D object detection methods. The source code is publicly available at https://github.com/Xinglong-Li1/ASCFormer.

Human activity recognition (HAR) based on radar has garnered wide interest due to its privacy-friendly and lighting-independent nature. Despite advancements, challenges like response speed and real-world applicability persist. To address these issues, data from different orientations utilizing frequency-modulated continuous-wave (FMCW) radar was collected to better reflect real-world conditions. In terms of data preprocessing, a sliding time-window nonlocal means filter is proposed to effectively remove the noise while retaining the micro-Doppler features. Subsequently, a multiattention enhanced fusion network (MAEF-Net) is proposed for HAR. MAEF-Net, an efficient and lightweight architecture, integrates multiple attention mechanisms to enhance robustness in omnidirectional HAR. It achieves high-accuracy omnidirectional activity recognition by simultaneously focusing on local and global features. Experimental results showcase MAEF-Net achieving 98.3% accuracy in testing, surpassing existing methods, thereby demonstrating its effectiveness in real-world scenarios.

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24 GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14 mmHg and -0.20 ±5.50 mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.

Boosting variational inference (BVI) approximates an intractable probability density by iteratively building up a mixture of simple component distributions one at a time, using techniques from sparse convex optimization to provide both computational scalability and approximation error guarantees. But the guarantees have strong conditions that do not often hold in practice, resulting in degenerate component optimization problems; and we show that the ad-hoc regularization used to prevent degeneracy in practice can cause BVI to fail in unintuitive ways. We thus develop universal boosting variational inference (UBVI), a BVI scheme that exploits the simple geometry of probability densities under the Hellinger metric to prevent the degeneracy of other gradient-based BVI methods, avoid difficult joint optimizations of both component and weight, and simplify fully-corrective weight optimizations. We show that for any target density and any mixture component family, the output of UBVI converges to the best possible approximation in the mixture family, even when the mixture family is misspecified. We develop a scalable implementation based on exponential family mixture components and standard stochastic optimization techniques. Finally, we discuss statistical benefits of the Hellinger distance as a variational objective through bounds on posterior probability, moment, and importance sampling errors. Experiments on multiple datasets and models show that UBVI provides reliable, accurate posterior approximations.