Reinforcement learning based multi core scheduling (RLBMCS) for real time systems

Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system

Harnessing Ambient Energy for IoT: Improvements in Path Tools for Drive Collecting Structures

Ability of energy harvesting systems that make use of Internet of Things (IoT) offer sustainable and independent power sources for diverse applications. In order to power IoT devices, these systems make it possible to capture ambient energy from the environment, such as solar, wind, vibrations, and thermal gradients, and transform it into usable electrical energy. We provide growths in circuit technology for IoT-based energy harvesting devices in this research.With a focus on MPPT algorithms that exploit energy extraction efficiency since various sources, advancements in power management circuits are investigated. Energy harvesting devices can now function well even in low-energy environments thanks to the development of ultra-low-power circuits, which is discussed.Super capacitors and rechargeable batteries, two types of energy storage, are assessed for their potential for energy buffering and dependable power delivery to Internet of Things (IoT) devices. Dynamic charging algorithms and capacity estimate methods are two examples of more sophisticated battery management approaches that are also looked at.Voltage regulation is a crucial component of energy harvesting systems that guarantees a steady and reliable power supply to Internet of Things (IoT) devices. Low-dropout regulators (LDOs) and energy-efficient voltage converters, among other recent advancements in voltage regulation circuits, are described. Additionally, the integration of energy harvesting systems with IoT devices is covered, highlighting the benefits and obstacles in creating IoT applications that are energyaware. To reduce power consumption in IoT networks, the significance of energy-efficient communication protocols and adaptive data processing algorithms is emphasised

An IOT framework for detecting cardiac arrhythmias in real-time using deep learning resnet model

A cardiac arrhythmia poses a serious health risk to patients and can have serious consequences for their health. A clinical assessment of arrhythmia disorders could save a person's life. The Internet of Things (IoT) will revolutionize the healthcare sector by continuously monitoring cardiac arrhythmia diseases remotely and minimally invasively. We propose a frame-work that will facilitate the development of a practical diagnostic tool for the identification of cardiac arrhythmias in real-time in this work. An Electrocardiogram (ECG) signal is processed using the Pan Tompkins QRS (Quantum Resonance System) detection method in order to extract the dynamic properties of the signal. The inter beat (RR) intervals are derived from an ECG signal in order to determine the characteristics of heart rate variability. The electrocardiogram is primarily used to identify irregular heartbeats (cardiac arrhythmias). Therefore, in our study, we evaluated other factors such as the heartbeat of the individual. As part of our IoT deployment, we are storing and analyzing data collected by the Pulse Sensor on the ThingSpeak IoT platform. The designed circuit's real-time collection of heartbeat and beats per minute values was uploaded to Thingspeak. Over the course of more than a week, we collected a variety of heart data. We propose Multi Channel Residual Network (MCHResNet) a deep-learning based solution that relies on multi-channel convolutions to detect both spatial and frequency features from electrocardiograms to facilitate the classification process. Based on the well-known Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia (MIT-BIH-AR) database, we evaluate the proposed framework against MCH ResNet. Our IoT-based framework has been shown to be effective based on the results reported in this paper