Gesture based IoT light control for smart clothing

In this paper, a smart wireless wristband is proposed. The potential of innovative gesture based interactivity with connected lighting solutions is reviewed. The solution is intended to offer numerous benefits, in terms of ease of use, and enhanced dynamic interactive functionality. A comparative analysis will be carried out between this work and existing solutions. The evolution of lighting and gesture controls will be discussed and an overview of alternative applications will be provided, as part of the critical analysis.

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However, as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.

Evolution of emerging iPS cell-based therapies for age-related macular degeneration (AMD)

Stem cell-based regenerative medicine is poised to revolutionize the way diseases are treated. In recent years, induced pluripotent stem (iPS) cells, a newly stem cell species, has attracted significant attention. This paper seeks to understand the pathways along which emerging clinical research efforts in the field of iPS cells is evolved. In particular, the empirical case of age-related macular degeneration (AMD) is used, which is the world-pioneering clinical application of iPS cells. In line with the literature, this study explores the interrelations between three different pathways, such as biomedical scientific understanding, development of medical technologies, and learning in clinical practice. For this, a techmining approach is used including co-term, co-citation, and direct citation methods. Scientific publications indexed in the Thomson Reuters' Web of Science and Elsevier's Scopus databases form the basis of the study. This research first explores the iPS cell research landscape through the construction of a co-term map, particularly stressing the location and intensity of disease-tackling efforts; then focus on the evolution of scientific knowledge on AMD through co-citation networks and the main path algorithm on direct citations. At the researcher level, the development of four different research groups working on cell therapies for AMD is evaluated through the software CitNetExplorer. By integrating these approaches, the result shows a wider picture of the complexities inherent in the translation of knowledge into revolutionary clinical methods.