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Internet changed how the people live and access the information that they need. Thanks to the accessibility and the benefits that it brings into the lives, new research areas are emerging. One of the areas is Internet of Things (IoT) which connects countless of devices to the Internet. Increasing usage in IoT tremendously increases the count of con...
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... and assign separate roles to the components. All components defined here can be implemented in any language as long as they satisfy the requirements. Time-sensitiveness requires implementation of several components which are compatible with each other. These components should enable a reliable, stable in-time response and take correct actions. Fig. 5 shows the required software components inside the Core Node to fulfil the requirements. Below, these components are ...
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The Internet has changed the way people access the information they need, and indeed how they live. Whether it is individuals reading emails or watching videos, or factories utilising automated fabrication devices, the access and processing of data is totally different. Thanks to the accessibility and the benefits that it brings into the lives, new...
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With the rise in cutting-edge artificial intelligence technology, 5G networks and the Internet of Things (IoT), enormous amounts of data are produced at the source and are required to be processed in the cloud. A large amount of power and bandwidth is required to transfer the data from the source to the cloud. Consequently, healthcare applications demanding seamless real time data processing and analysis must be able to compute the data closer to the source, that is, the edge. Edge computing devices are the hardware components that harness the application of edge computing thereby reducing the latency of data transfer and processing. Therefore this chapter proposes the hardware architecture for edge computing devices considering the power constraints and provides a survey of efficient embedded cores that can be used in edge computing. The main idea behind this chapter is to present an overview of state-of-the-art techniques and approaches involved in designing low power microcontroller units for edge computing devices which can be used in smart healthcare applications. To further build on the idea and explore its applications, examples of edge computing devices are also presented. In addition, applications of edge computing for smart healthcare systems, challenges in implementation, the impact of 5G, IoT, and advantages of edge computing in smart healthcare applications are also discussed.
Brain-Computer Interface (BCI) also referred to as Brain-Machine Interface (BMI) is a buzzword in the world of neuroscience, translating the human brain's thoughts into a chip. These devices may be surgically implanted or placed externally. Such components allow the user to control the actuators or sense the input data through bilateral communication to achieve the task. Most of the current applications focus on neural prosthetics, artificial limbs, cochlear implants, and assistive devices for people affected by neurological disorders such as Alzheimer’s, Parkinson’s and more. Initially, It was developed to assist persons with neurological disorders. Due to the evolution of non-invasive imaging components, BCI is being extended for public communication like the brain- brain interfacing. The implementation of BCI on neuromorphic hardware components would further improve the computational complexity, execution speed, energy efficiency, and robustness against local failure. Machine learning and deep learning algorithms are contributing to computer vision, speech recognition, game control, autonomous vehicle systems, disease classification/prediction, and many more. Though BCI has improved the lifestyle of the end-users, the responsiveness of those devices is not alike natural elements. Hence, to create an effective pathway from a brain to the external world through mapping, augmenting, assisting and troubleshooting, many computational intelligence methods have been proposed. In this chapter, the translation of brain waves into features and further classified to control any applications in an open/closed environment with a secure mechanism along with adaptive learning algorithms will be discussed.
