A Dictionary Optimization Method for Reconstruction of ECG Signals after Compressed Sensing

Redundancy cancellation of compressed measurements by QRS complex alignment

The demand for long-term continuous care has led healthcare experts to focus on development challenges. On-chip energy consumption as a key challenge can be addressed by data reduction techniques. In this paper, the pseudo periodic nature of ElectroCardioGram(ECG) signals has been used to completely remove redundancy from frames. Compressing aligned QRS complexes by Compressed Sensing (CS), result in highly redundant measurement vectors. By removing this redundancy, a high cluster of near zero samples is gained. The efficiency of the proposed algorithm is assessed using the standard MIT-BIH database. The results indicate that by aligning ECG frames, the proposed technique can achieve superior reconstruction quality compared to state-of-the-art techniques for all compression ratios. This study proves that by aligning ECG frames with a 0.05% unaligned frame rate(R-peak detection error), more compression could be gained for PRD > 5% when 5-bit non-uniform quantizer is used. Furthermore, analysis done on power consumption of the proposed technique, indicates that a very good recovery performance can be gained by only consuming 4.9μW more energy per frame compared to traditional CS.

An Efficient On-Demand Hardware Replacement Platform for Metamorphic Functional Processing in Edge-Centric IoT Applications

The paradigm of Internet-of-things (IoT) systems is changing from a cloud-based system to an edge-based system. These changes were able to solve the delay caused by the rapid concentration of data in the communication network, the delay caused by the lack of server computing capacity, and the security issues that occur in the data communication process. However, edge-based IoT systems performance was insufficient to process large numbers of data due to limited power supply, fixed hardware functions, and limited hardware resources. To improve their performance, application-specific hardware can be installed in edge devices, but performance cannot be improved except for specific applications due to a fixed function of an application-specific hardware. This paper introduces an edge-centric metamorphic IoT (mIoT) platform that can use various hardware modules through on-demand partial reconfiguration, despite the limited hardware resources of edge devices. In addition, this paper introduces an RISC-V based metamorphic IoT processor (mIoTP) with reconfigurable peripheral modules. We experimented to prove that the proposed structure can reduce the server access of edges and can be applied to a large-scale IoT system. Experiments were conducted in a single-edge environment and a large-scale environment combining one physical edge and 99 virtual edges. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration prediction algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to our previous study in which the prediction process was executed at the server. Furthermore, we confirmed that there is no additional reconfiguration time overhead even for the large IoT systems.