Caleb McIrvin

In wireless communications, collaborative spectrum sensing is a process that leverages radio frequency (RF) data from multiple RF sensors to make more informed decisions and lower the overall risk of failure in distributed settings. However, most research in collaborative sensing focuses on homogeneous systems using identical sensors, which would not be the case in a real world wireless setting. Instead, due to differences in physical location, each RF sensor would see different versions of signals propagating in the environment, establishing the need for heterogeneous collaborative spectrum sensing. Hence, this paper explores the implementation of collaborative spectrum sensing across heterogeneous sensors, with sensor fusion occurring in the cloud for optimal decision making. We investigate three different machine learning-based fusion methods and test the fused model's ability to perform modulation classification, with a primary goal of optimizing for network bandwidth in regard to next-generation network applications. Our analysis demonstrates that our fusion process is able to optimize the number of features extracted from the heterogeneous sensors according to their varying performance limitations, simulating adverse conditions in a real-world wireless setting.

Objective: This project focuses on Artificial Intelligence (AI) supported enhancement of descriptive metadata for fashion collections (otherwise known as costume or dress and textile collections) through expanding costume-specific controlled terms. The authors use Natural Language Processing (NLP) techniques along with a human-in-the-loop process to support selection of descriptive terms for inclusion in the controlled terms of a metadata schema. This project seeks to expand upon existing domain-specific schema, Costume Core, by enhancing the schema with a comprehensive set of descriptors. This enhancement will allow for more accurate and detailed descriptions of costume artifacts. This article describes this process and the outcomes of AI approaches for providing this metadata expansion, who this process is for, ethical considerations, and lessons learned.Methods: We approached our problem with an investigation into using word embeddings to aid in supporting the suggesting of new metadata terms. Several word embedding models were applied with the more descriptive one chosen for final use in a human-in-the-loop selection process. This selection process provided domain experts to identify which terms chosen by the model are of relevant value. We then compare what was chosen by the domain experts and what the model produced to get an idea as to the value the model provides in the process of metadata expansion.Results: The metadata expansion process was a success. An AI supported process aided domain experts in choosing relevant terms to include in their metadata schema. Therefore, the results were a methodology for using identified AI models for the problem, an interactive system to aid the domain experts (software system), and how to evaluate the results.Conclusion: The application of AI technologies (word embeddings) provided a successful pipeline for supporting domain experts to expand the metadata schema with additional descriptors. Enhancing the metadata schema with additional descriptors improves its usability for fashion collection managers and allows for a more precise description of the artifacts. As a result, many new terms that were expertly chosen were added to the metadata schema.