Towards energy-autonomous wake-up receiver using visible light communication

The use of Visible Light Communication (VLC) in wake-up communication systems is a potential energy-efficient and low-cost solution for wireless communication of consumer electronics. In this paper, we go one step further and propose the use of visible light both for wake-up communication and energy harvesting purposes, with the final objective of an energy-autonomous wake-up receiver module. We first present the details and the design criteria of this novel system. We then present the results of evaluation of design criteria such as solar panel and capacitor type choices. To evaluate the performance of the developed wake-up system with energy-autonomous receiver system, we perform realistic indoor scenario tests, analyzing the effect of varying distances, angles, and light intensities as well as the effect of presence of interfering lights.Peer ReviewedPostprint (author's final draft

Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts

Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues that continuously relying on human experts to handle difficult model classifications leads to a strong increase in human effort, which strains limited resources. To address this issue, we propose a hybrid system that creates artificial experts that learn to classify data instances from unknown classes previously reviewed by human experts. Our hybrid system assesses which artificial expert is suitable for classifying an instance from an unknown class and automatically assigns it. Over time, this reduces human effort and increases the efficiency of the system. Our experiments demonstrate that our approach outperforms traditional HITL systems for several benchmarks on image classification

Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts

Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues that continuously relying on human experts to handle difficult model classifications leads to a strong increase in human effort, which strains limited resources. To address this issue, we propose a hybrid system that creates artificial experts that learn to classify data instances from unknown classes previously reviewed by human experts. Our hybrid system assesses which artificial expert is suitable for classifying an instance from an unknown class and automatically assigns it. Over time, this reduces human effort and increases the efficiency of the system. Our experiments demonstrate that our approach outperforms traditional HITL systems for several benchmarks on image classification.Comment: Accepted at International Conference on Wirtschaftsinformatik, 202

Conceptualizing Digital Resilience for AI-based Information Systems

The increasing volume of external shocks, such as the financial crisis or the COVID-19 pandemic, emphasizes the need for businesses to increase their resilience, i.e., a system\u27s ability to reduce the impact or recover from such shocks quickly. In the past, information systems were often viewed as a source of vulnerability and complexity. In contrast, we conceptualize digital resilience—a form of resilience that is enabled through information systems by taking an integrated approach. In this work, we develop a framework for the design of digital resilience in AI-based information systems. Based on this framework, we conduct and evaluate an illustrative study addressing demand shocks in the airline industry. We, thereby, show the technical feasibility of concept drift detection and discuss why digital resilience must be designed holistically