Transformers have achieved significant success in medical image segmentation, owing to their capability to capture long-range dependencies. Previous studies have employed either pure Transformer or hybrid CNN-Transformer architectures in the encoder module to enhance their ability to extract more complex features. However, these models still exhibit limitations in fine-grained local feature extraction and effectively suppressing irrelevant information. To address this issue, a convolution sparse vector coding-based decoder is proposed, namely the CAScaded multi-layer Convolutional Sparse vector Coding DEcoder (CASCSCDE), which suppresses noise by refining the feature representation to be more sparse and accurate through sparse coding and localized convolution, effectively minimizing less important, noisy components. To demonstrate the effectiveness and versatility of our CASCSCDE, we incorporate our decoder into both pure Transformer and hybrid CNN-Transformer models, such as SwinUNet and TransUNet. Our experiments demonstrate that integrating CASCSCDE into the models significantly enhances segmentation performance. The CASCSCDE opens new ways for constructing decoders based on convolutional sparse vector coding.

Recent work on battery-free soft robotics has demonstrated the use of liquid crystal elastomers (LCE) to build shape-changing materials activated by applied external heat. However, sources of heat must typically be in direct field-of-view of the robot (i.e. NIR, laser, and visual light EM sources or convective heats guns), be tethered to an external power supply (i.e. thermoelectric heating or resistive joule heaters), or require a heavy on-board battery that limits mobility and range. This paper presents a novel battery-free soft-robotics platform that can crawl through confined, enclosed, and hard-to-reach spaces (e.g. packages, machinery, pipes, etc.), hidden from view of heating infrastructure. This is achieved through the co-design of a soft robotics platform and integrated soft conductive traces that enable wireless (microwave) heating through remote stimulation. We achieve fast actuation through a careful choice of materials and the overall mechanical structure of the robot to maximize heating efficiency. Further, the robot is actively tracked through enclosed spaces using a mm Wave radar to direct heat to its location. We provide a detailed evaluation on the robot's heating efficiency, location-tracking accuracy and crawling speed.