Object-oriented wrappers for the Linux kernel

Linux is an open-source operating system, which has increased in its popularity and size since its birth. Various studies have been conducted in literature on the evolution of the Linux kernel, which have shown that there are considerable maintenance problems arising out of the coupling issues in the Linux kernel and this may hamper the evolution of the kernel in future. We propose an object-oriented (OO) wrapper-based approach to Linux kernel to provide OO abstractions to external modules. As the major growth of the size of the Linux kernel is in device drivers, our approach provides substantial benefits in terms of developing the device drivers in C++, although the kernel is in C. Providing reusability and extensibility features to device drivers improves the maintainability of the kernel. The OO wrappers provide several benefits to module developers in terms of understandability, development ease, support for OO modules, etc. The design and implementation of C++ wrappers for Linux kernel and the performance of a device driver re-engineered in C++ are presented in this paper

Towards real-time heartbeat classification : evaluation of nonlinear morphological features and voting method

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%