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We demonstrate an application of finding target persons on a surveillance video. Each visually detected participant is tagged with a smartphone ID and the target person with the query ID is highlighted. This work is motivated by the fact that establishing associations between subjects observed in camera images and messages transmitted from their wireless devices can enable fast and reliable tagging. This is particularly helpful when target pedestrians need to be found on public surveillance footage, without the reliance on facial recognition. The underlying system uses a multi-modal approach that leverages WiFi Fine Timing Measurements (FTM) and inertial sensor (IMU) data to associate each visually detected individual with a corresponding smartphone identifier. These smartphone measurements are combined strategically with RGB-D information from the camera, to learn affinity matrices using a multi-modal deep learning network. 

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin. 

Spectroscopic single-molecule localization microscopy (sSMLM) simultaneously provides spatial localization and spectral information of individual single-molecules emission, offering multicolor super-resolution imaging of multiple molecules in a single sample with the nanoscopic resolution. However, this technique is limited by the requirements of acquiring a large number of frames to reconstruct a super-resolution image. In addition, multicolor sSMLM imaging suffers from spectral cross-talk while using multiple dyes with relatively broad spectral bands that produce cross-color contamination. Here, we present a computational strategy to accelerate multicolor sSMLM imaging. Our method uses deep convolution neural networks to reconstruct high-density multicolor super-resolution images from low-density, contaminated multicolor images rendered using sSMLM datasets with much fewer frames, without compromising spatial resolution. High-quality, super-resolution images are reconstructed using up to 8-fold fewer frames than usually needed. Thus, our technique generates multicolor super-resolution images within a much shorter time, without any changes in the existing sSMLM hardware system. Two-color and three-color sSMLM experimental results demonstrate superior reconstructions of tubulin/mitochondria, peroxisome/mitochondria, and tubulin/mitochondria/peroxisome in fixed COS-7 and U2-OS cells with a significant reduction in acquisition time.