Minimally-invasive surgery (MIS) and robot-assisted minimally invasive (RAMIS) surgery offer well-documented benefits to patients such as reduced post-operative pain and shorter hospital stays.However, the automation of MIS and RAMIS through the use of AI has been slow due to difficulties in data acquisition and curation, partially caused by the ethical considerations of training, testing and deploying AI models in medical environments.We introduce \texttt{SARAMIS}, the first large-scale dataset of anatomically derived 3D rendering assets of the human abdominal anatomy.Using previously existing, open-source CT datasets of the human anatomy, we derive novel 3D meshes, tetrahedral volumes, textures and diffuse maps for over 104 different anatomical targets in the human body, representing the largest, open-source dataset of 3D rendering assets for synthetic simulation of vision tasks in MIS+RAMIS, increasing the availability of openly available 3D meshes in the literature by three orders of magnitude.We supplement our dataset with a series of GPU-enabled rendering environments, which can be used to generate datasets for realistic MIS/RAMIS tasks.Finally, we present an example of the use of \texttt{SARAMIS} assets for an autonomous navigation task in colonoscopy from CT abdomen-pelvis scans for the first time in the literature.\texttt{SARAMIS} is publically made available at https://github.com/NMontanaBrown/saramis/, with assets released under a CC-BY-NC-SA license.

To properly determine the severity of an emergency and provide a quick response, information has to be gathered, confirmed, and analyzed. Artificial intelligence assisted UAV can provide a unique perspective and review extensive information for an incident. However, their limited computing capacity and battery duration raise the challenge to compute-intensive mobile applications. In addition, because they serve civilian purposes, the security of UAV communication systems has become critical. To enhance first responders' safety and effectiveness, we develop an end-to-end UAV-assisted emergency management system with secure wireless communication links, cloud-based deep learning cognitive algorithms, and easy-to-use mobile user interface. We implement our system onto a hardware platform including Infineon Larix drone, Raspberry Pi and camera, and GPU server. We evaluate the tradeoff between detection accuracy and processing time, and show that our system achieves soft real-time end-to-end latency for data transmission, processing, and reception.