Recently, camera-radar fusion-based 3D object detection methods in bird's eye view (BEV) have gained attention due to the complementary characteristics and cost-effectiveness of these sensors. Previous approaches using forward projection struggle with sparse BEV feature generation, while those employing backward projection overlook depth ambiguity, leading to false positives. In this paper, to address the aforementioned limitations, we propose a novel camera-radar fusion-based 3D object detection and segmentation model named CRAB (Camera-Radar fusion for reducing depth Ambiguity in Backward projection-based view transformation), using a backward projection that leverages radar to mitigate depth ambiguity. During the view transformation, CRAB aggregates perspective view image context features into BEV queries. It improves depth distinction among queries along the same ray by combining the dense but unreliable depth distribution from images with the sparse yet precise depth information from radar occupancy. We further introduce spatial cross-attention with a feature map containing radar context information to enhance the comprehension of the 3D scene. When evaluated on the nuScenes open dataset, our proposed approach achieves a state-of-the-art performance among backward projection-based camera-radar fusion methods with 62.4% NDS and 54.0% mAP in 3D object detection.

Miniature, battery-free implants promise transformative bio-electronic therapies by enabling minimally invasive implantation procedures, reducing risk, and extending device lifetime. Among all wireless power and data transfer (WPDT) modalities, magnetoelectrics (ME) has emerged as a particularly promising solution for millimeter-scale implants, boasting lower tissue attenuation and higher power transfer efficiency over conventional inductive and ultrasonic methods. However, as an acoustic resonator, ME devices face an inherent tradeoff between Q-factor and bandwidth, limiting their ability to simultaneously achieve high-speed communication and efficient wireless power transfer (WPT). To fundamentally circumvent the challenge, this paper presents dual-resonance ME WPDT that exploits the unique multimode resonances of ME transducers to realize WPT and communication at distinct frequencies. Based on this dual-resonance principle, we demonstrate reconfigurable active and passive schemes for different biomedical applications, with a proof-of-concept system including a miniature implant and an external transceiver. The active scheme achieves 60 kbps at operational distances of 6 cm with 2.5 mW implant power, while the passive backscatter offers 20 kbps continuous streaming at 4 cm with negligible power, demonstrating the first reported non-interrupted ME WPDT system and more than twice the data rate of previous ME backscatter methods. Both schemes further support on-off keying (OOK) and binary phase shift keying (BPSK) modulations, providing additional flexibility to tailor communication needs between power efficiency and robustness. The complete prototype system was validated through comprehensive in-vitro experiments and in-vivo EMG streaming in a rodent model.