Magnetic Resonance Imaging (MRI) is widely used in medical diagnosis due to its excellent ability to image soft tissues. However, its scanning process suffers from issues such as long imaging times and slow reconstruction speeds, which limit its clinical efficiency. To address these limitations, we propose a novel accelerated MRI reconstruction method, termed CRAVS-Net. This method combines complex residual modeling with multihead attention mechanisms to effectively integrate amplitude and phase information from MRI images. Additionally, a multistage residual structure and a channel attention fusion module are introduced to enhance the modeling depth of multi-channel features and the utilization of cross-scale contextual information. Experiments on the NYU knee dataset demonstrate that CRAVSNet outperforms existing state-of-the-art approaches in terms of SSIM and PSNR, and exhibits robust reconstruction performance and strong generalization ability.

3D object detection is essential in autonomous driving, providing vital information about moving objects and obstacles. Detecting objects in distant regions with only a few LiDAR points is still a challenge, and numerous strategies have been developed to address point cloud sparsity through densification. This paper presents a joint completion and detection framework that improves the detection feature in sparse areas while maintaining costs unchanged. Specifically, we proposeTransBridge, a novel transformer-based up-sampling block that fuses the features from the detection and completion networks. The detection network can benefit from acquiring implicit completion features derived from the completion network. Additionally, we design theDynamic-Static Reconstruction(DSRecon) module to produce dense LiDAR data for the completion network, meeting the requirement for dense point cloud ground truth. Furthermore, we employ the transformer mechanism to establish connections between channels and spatial relations, resulting in a high-resolution feature map used for completion purposes. Extensive experiments on the nuScenes and Waymo datasets demonstrate the effectiveness of the proposed framework. The results show that our framework consistently improves end-to-end 3D object detection, with the mean average precision (mAP) ranging from 0.7 to 1.5 across multiple methods, indicating its generalization ability. For the two-stage detection framework, it also boosts the mAP up to 5.78 points.

It is quite laborious and costly to manually label LiDAR point cloud data for training high-quality 3D object detectors. This work proposes a weakly supervised framework which allows learning 3D detection from a few weakly annotated examples. This is achieved by a two-stage architecture design. Stage-1 learns to generate cylindrical object proposals under inaccurate and inexact supervision, obtained by our proposed BEV center-click annotation strategy, where only the horizontal object centers are click-annotated in bird's view scenes. Stage-2 learns to predict cuboids and confidence scores in a coarse-to-fine, cascade manner, under incomplete supervision, i.e., only a small portion of object cuboids are precisely annotated. With KITTI dataset, using only 500 weakly annotated scenes and 534 precisely labeled vehicle instances, our method achieves 86-97 percent the performance of current top-leading, fully supervised detectors (which require 3,712 exhaustively annotated scenes with 15,654 instances). More importantly, with our elaborately designed network architecture, our trained model can be applied as a 3D object annotator, supporting both automatic and active (human-in-the-loop) working modes. The annotations generated by our model can be used to train 3D object detectors, achieving over 95 percent of their original performance (with manually labeled training data). Our experiments also show our model's potential in boosting performance when given more training data. The above designs make our approach highly practical and open-up opportunities for learning 3D detection at reduced annotation cost.

Current popular online multi-object tracking (MOT) solutions apply single object trackers (SOTs) to capture object motions, while often requiring an extra affinity network to associate objects, especially for the occluded ones. This brings extra computational overhead due to repetitive feature extraction for SOT and affinity computation. Meanwhile, the model size of the sophisticated affinity network is usually non-trivial. In this paper, we propose a novel MOT framework that unifies object motion and affinity model into a single network, named UMA, in order to learn a compact feature that is discriminative for both object motion and affinity measure. In particular, UMA integrates single object tracking and metric learning into a unified triplet network by means of multi-task learning. Such design brings advantages of improved computation efficiency, low memory requirement and simplified training procedure. In addition, we equip our model with a task-specific attention module, which is used to boost task-aware feature learning. The proposed UMA can be easily trained end-to-end, and is elegant - requiring only one training stage. Experimental results show that it achieves promising performance on several MOT Challenge benchmarks.

To eliminate crosstalk problem among multiple sonar sensors widely used in mobile robots, a novel frequency-hopping pseudo-random pulse width modulation (FPPWM) sequence is put forward. To ensure sharp autocorrelation characteristic, which is necessary for a sonar sensor to avoid crosstalk based on correlation technique, each FPPWM sequence is designed to include multiple variable-width and fixed-amplitude pulses, where the duration between two neighboring pulses is random and the frequency range is within bandwidth of the ultrasonic transducer. To adapt to the intrinsic physical characteristic of sonar transducers, i.e. the transmitted ultrasonic frequency can not follow that of the suddenly changed triggering sequence, the proposed sequence covers several frequency bands, which makes sure that the frequency difference between two neighbor triggering pulses is small enough for the transducer to follow. On the other hand, different combination of frequency bands has also enhanced the randomness of the proposed sequence. The FPPWM sequence is a digital signal generated by a DSP processor and no D/A converter is needed. To facilitate the correlation processing, the reconstructed echo sequence is used by compressing the real echo sequence. Real experiments using two Polaroid 600 series instrument grade electrostatic transducers validate the suitability of the FPPWM sequence

Crosstalk is one main problem which leads to false distance measurement and reduces work efficiency of ultrasonic range sensor systems in mobile robots. The reason which results in ultrasonic crosstalk is briefly analyzed. To enhance the SNR (signal noise ratio) and work efficiency, different methods used to avoid ultrasonic crosstalk have been put forward in the past decade. Such representative methods are classified into two categories, which are called heuristic soft-processing strategy and hardware based coding strategy in this paper. The advantage and disadvantage of each method are analyzed. Suggestions and comments on designing non-crosstalk ultrasonic range system are proposed at the end