Engineering Commons^™

Spectrum Sharing, Latency, And Security In 5g Networks With Application To Iot And Smart Grid, Imtiaz Parvez

FIU Electronic Theses and Dissertations

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The …

From A Locally Competitive Algorithm To Sensory Relevance Models, Walter Woods

Electrical and Computer Engineering PhD Day

This poster addresses the development of a new Machine Learning (ML) mechanism, the Sensory Relevance Model (SRM), as a means of splitting information processing tasks into two sub-tasks with more intuitive properties. Specifically, SRMs are a front-end for other ML techniques, re-mapping the input data to a similar space with significantly more sparsity, achieved through the transformation and suppression of inputs irrelevant to the task. Prior work has attempted to reveal this information for Neural Networks (NNs) either as a post-processing step via saliency maps or through a simple masking of the input achieved with a dot product (so-called ``attention'' …

A Lightweight Classification Algorithm For Human Activity Recognition In Outdoor Spaces, Graham Mccalmont, Huiru Zheng, Haiying Wang, S. I. Mcclean, Matteo Zallio, Damon Berry

Conference Papers

The aim of this paper is to discuss the development of a lightweight classification algorithm for human activity recognition in a defined setting. Current techniques to analyse data such as machine learning are often very resource intensive meaning they can only be implemented on machines or devices that have large amounts of storage or processing power. The lightweight algorithm uses Euclidean distance to measure the difference between two points and predict the class of new records.

The results of the algorithm are largely positive achieving accuracy of 100% when classifying records taken from the same sensor position and accuracy of …