Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system （SPRS）. This system, equipped with a combination of various non-invasive sensors, can mon- itor an elderly user＇s sleep behavior. It accumulates the de- tecting data from a pressure sensor matrix and ultra wide band （UWB） tags. Based on these two types of complemen- tary sensing data, SPRS can assess the user＇s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users＇ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.

Prediction of job characteristics for intelligent resource allocation in HPC systems:a survey and future directions

Nowadays,high-performance computing(HPC)clusters are increasingly popular.Large volumes of job logs recording many years of operation traces have been accumulated.In the same time,the HPC cloud makes it possible to access HPC services remotely.For executing applications,both HPC end-users and cloud users need to request specific resources for different workloads by themselves.As users are usually not familiar with the hardware details and software layers,as well as the performance behavior of the underlying HPC systems.It is hard for them to select optimal resource configurations in terms of performance,cost,and energy efficiency.Hence,how to provide on-demand services with intelligent resource allocation is a critical issue in the HPC community.Prediction of job characteristics plays a key role for intelligent resource allocation.This paper presents a survey of the existing work and future directions for prediction of job characteristics for intelligent resource allocation in HPC systems.We first review the existing techniques in obtaining performance and energy consumption data of jobs.Then we survey the techniques for single-objective oriented predictions on runtime,queue time,power and energy consumption,cost and optimal resource configuration for input jobs,as well as multi-objective oriented predictions.We conclude after discussing future trends,research challenges and possible solutions towards intelligent resource allocation in HPC systems.

HIGH FIDELITY VIRTUALIZATION OF CYBER-PHYSICAL SYSTEMS

Cyber-physical systems(CPS)tightly integrate cyber and physical components and transcend discrete and continuous domains.It is greatly desired that the synergy between cyber and physical components of CPS is explored even before the complete system is put together.Virtualization has potential to play a significant role in exploring such synergy.In this paper,we propose a CPS virtualization approach based on the integration of virtual machine and physical component emulator.It enables real software,virtual hardware,and virtual physical components to execute in a holistic virtual execution environment.We have implemented this approach using QEMU as the virtual machine and Matlab/Simulink as the physical component emulator,respectively.To achieve high-fidelity between the real system and its virtualization,we have developed a strategy for synchronizing the virtual machine and the physical component emulator.To evaluate our approach,we have successfully applied it to real-world control systems.Experiments results have shown that our approach achieves high-fidelity in capturing dynamic behaviors of the entire system.This approach is promising in enabling early development of cyber components of CPS and early exploration of the synergy of cyber and physical components.