Amit Kumar Singh

Piotr Dziurzanski, Amit Kumar Singh, Leandro S. Indrusiak et al.

In this paper, using of automotive use cases as benchmarks for real-time system design has been proposed. The use cases are described in a format supported by AMALTHEA platform, which is a model based open source development environment for automotive multi-core systems. An example of a simple Electronic Control Unit has been analysed and presented with enough details to reconstruct this system in any format. For researchers willing to use AMALTHEA file format directly, an appropriate parser has been developed and offered. An example of applying this parser and benchmark for optimising makespan while not violating the timing constraints by allocating functionality to different Network on Chip resource is demonstrated.

Somdip Dey, Amit Kumar Singh, Xiaohang Wang et al.

Given the constant rise in utilizing embedded devices in daily life, side channels remain a challenge to information flow control and security in such systems. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing elements are observed over time in order to deduce security flaws. In our proposed methodology, DATE: Defense Against TEmperature side-channel attacks, we propose a novel approach of reducing spatial and temporal thermal gradient, which makes the system more secure against temperature side-channel attacks, and at the same time increases the reliability of the device in terms of lifespan. In this paper, we have also introduced a new metric, Thermal-Security-in-Multi-Processors (TSMP), which is capable of quantifying the security against temperature side-channel attacks on computing systems, and DATE is evaluated to be 139.24% more secure at the most for certain applications than the state-of-the-art, while reducing thermal cycle by 67.42% at the most.

Albert Haque, Michelle Guo, Alexandre Alahi et al.

One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection. If we can intelligently track healthcare staff, patients, and visitors, we can better understand the sources of such infections. We envision a smart hospital capable of increasing operational efficiency and improving patient care with less spending. In this paper, we propose a non-intrusive vision-based system for tracking people's activity in hospitals. We evaluate our method for the problem of measuring hand hygiene compliance. Empirically, our method outperforms existing solutions such as proximity-based techniques and covert in-person observational studies. We present intuitive, qualitative results that analyze human movement patterns and conduct spatial analytics which convey our method's interpretability. This work is a step towards a computer-vision based smart hospital and demonstrates promising results for reducing hospital acquired infections.