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In this paper, we consider the automotive orthogonal frequency division modulation-radar in millimeter wave band. To avoid interference between different radar systems, resources need to be split and then used by different radar systems. This thus degrades the radar performance as compared to the radar system having full resources (FRs). To mitigate this issue, we develop a deep learning-based range-Doppler (R-D) map reconstruction approach along with a time-frequency resource allocation scheme. In the reconstruction approach, we propose a deep learning-based convolutional neural network to reconstruct the R-D map such that the reconstructed R-D map can be close to the R-D map under FRs. In the resource allocation scheme, we propose a block-wise interleaved method that can facilitate the proposed reconstruction approach. Simulation results show that our proposed approach can effectively mitigate the performance degradation of radar systems when resources are shared among users.

This paper presents a multisection bendable actuator with soft ring-buckle-type arm pair in each section (Fig. 1). The actuator is made of ionic polymer metal composite (IPMC) with our newly developed fabrication process, possessing better toughness and force output compared to conventional IPMC actuators. Novel flip lock connectors and parylene thin-film cable are developed and further integrated with the IPMC actuator to effectively provide squeezing pressure and electrical signals. A polyacrylamide hydrogel/parylene tube is created and assembled with the developed IPMC actuator as an active esophagus mimic for functional testing. The soft tube manipulated to generate a peristalsis-like movement for adjusting the bolus transport by electrical control of the IPMC arm pairs with different phase is demonstrated.

This work proposes an approach using the lowcost Kinect sensor as an assisting device that not only uses a contactless device to capture images information but cooperates with the application of developing field goal shooting posture detection algorithms for basketball players. The algorithms classify the field goal shooting postures of players into three stages as pre-shot, mid-shot, and post-shot routines each time for analyses. The Kinect first sets the posture recognizing conditions through the data provided by professional players and analyzes the postures affecting the highest and lowest field goal shooting percentage the most and least data from the statistical results.