A Near-Field Super-Resolution Network for Accelerating Antenna Characterization

We present a deep neural network-enabled method to accelerate near-field (NF) antenna measurement. We develop a Near-field Super-resolution Network (NFS-Net) to reconstruct significantly undersampled near-field data as high-resolution data, which considerably reduces the number of sampling points required for NF measurement and thus improves measurement efficiency. The high-resolution near-field data reconstructed by the network is further processed by a near-field-to-far-field (NF2FF) transformation to obtain far-field antenna radiation patterns. Our experiments demonstrate that the NFS-Net exhibits both accuracy and generalizability in restoring high-resolution near-field data from low-resolution input. The NF measurement workflow that combines the NFS-Net and the NF2FF algorithm enables accurate radiation pattern characterization with only 11% of the Nyquist rate samples. Though the experiments in this study are conducted on a planar setup with a uniform grid, the proposed method can serve as a universal strategy to accelerate measurements under different setups and conditions.

DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference

Semantic segmentation for scene understanding is nowadays widely demanded, raising significant challenges for the algorithm efficiency, especially its applications on resource-limited platforms. Current segmentation models are trained and evaluated on massive high-resolution scene images ("data level") and suffer from the expensive computation arising from the required multi-scale aggregation("network level"). In both folds, the computational and energy costs in training and inference are notable due to the often desired large input resolutions and heavy computational burden of segmentation models. To this end, we propose DANCE, general automated DAta-Network Co-optimization for Efficient segmentation model training and inference. Distinct from existing efficient segmentation approaches that focus merely on light-weight network design, DANCE distinguishes itself as an automated simultaneous data-network co-optimization via both input data manipulation and network architecture slimming. Specifically, DANCE integrates automated data slimming which adaptively downsamples/drops input images and controls their corresponding contribution to the training loss guided by the images' spatial complexity. Such a downsampling operation, in addition to slimming down the cost associated with the input size directly, also shrinks the dynamic range of input object and context scales, therefore motivating us to also adaptively slim the network to match the downsampled data. Extensive experiments and ablating studies (on four SOTA segmentation models with three popular segmentation datasets under two training settings) demonstrate that DANCE can achieve "all-win" towards efficient segmentation(reduced training cost, less expensive inference, and better mean Intersection-over-Union (mIoU)).