Lizhi Sun

2papers

2 Papers

CRFeb 4, 2021
LEAP: TrustZone Based Developer-Friendly TEE for Intelligent Mobile Apps

Lizhi Sun, Shuocheng Wang, Hao Wu et al.

ARM TrustZone is widely deployed on commercial-off-the-shelf mobile devices for secure execution. However, many Apps cannot enjoy this feature because it brings many constraints to App developers. Previous works have been proposed to build a secure execution environment for developers on top of TrustZone. Unfortunately, these works are still not fully-fledged solutions for mobile Apps, especially for emerging intelligent Apps. To this end, we propose LEAP, which is a lightweight developer-friendly TEE solution for mobile Apps. LEAP enables isolated codes to execute in parallel and access peripheral (e.g., mobile GPUs) with ease, flexibly manages system resources upon different workloads, and offers the auto DevOps tool to help developers prepare the codes running on it. We implement the LEAP prototype on the off-the-shelf ARM platform and conduct extensive experiments on it. The experimental results show that Apps can be adapted to run with LEAP easily and efficiently. Compared to the state-of-the-art work along this research line, LEAP can achieve an average 3.57x speedup in supporting intelligent Apps using mobile GPU acceleration.

CVNov 22, 2013
Dictionary-Learning-Based Reconstruction Method for Electron Tomography

Baodong Liu, Hengyong Yu, Scott S. Verbridge et al.

Electron tomography usually suffers from so called missing wedge artifacts caused by limited tilt angle range. An equally sloped tomography (EST) acquisition scheme (which should be called the linogram sampling scheme) was recently applied to achieve 2.4-angstrom resolution. On the other hand, a compressive sensing-inspired reconstruction algorithm, known as adaptive dictionary based statistical iterative reconstruction (ADSIR), has been reported for x-ray computed tomography. In this paper, we evaluate the EST, ADSIR and an ordered-subset simultaneous algebraic reconstruction technique (OS-SART), and compare the ES and equally angled (EA) data acquisition modes. Our results show that OS-SART is comparable to EST, and the ADSIR outperforms EST and OS-SART. Furthermore, the equally sloped projection data acquisition mode has no advantage over the conventional equally angled mode in the context.