Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Development of a Safeguards Monitoring System for Special Nuclear Facilities

Two important issues related to nuclear materials safeguards are the continuous monitoring of nuclear processing facilities to verify that undeclared uranium is not processed or enriched and to verify that declared uranium is accounted for. The International Atomic Energy Agency (IAEA) is tasked with ensuring special nuclear facilities are operating as declared and that proper material safeguards have been followed. Traditional safeguards measures have relied on IAEA personnel inspecting each facility and verifying material with authenticated instrumentation. In newer facilities most plant instrumentation data are collected electronically and stored in a central computer. Facilities collect this information for a variety of reasons, most notably for process optimization and monitoring. The field of process monitoring has grown significantly over the past decades, and techniques have been developed to detect and identify changes and to improve reliability and safety. Several of these techniques can also be applied to international and domestic safeguards. This dissertation introduces a safeguards monitoring system developed for both a simulated Uranium blend down facility, and a water-processing facility at the Oak Ridge National Laboratory. For the simulated facility, a safeguards monitoring system is developed using an Auto-Associative Kernel Regression model, and the effects of incorporating facility specific radiation sensors and preprocessing the data are examined. The best safeguards model was able to detect diversions as small as 1.1%. For the ORNL facility, a load cell monitoring system was developed. This monitoring system provides an inspector with an efficient way to identify undeclared activity and to identify atypical facility operation, included diversions as small as 0.1 kg. The system also provides a foundation for an on-line safeguards monitoring approach where inspectors remotely facility data to draw safeguards conclusion, possibly reducing the needed frequency and duration of a traditional inspection

Bio : A Mulrimodal biometric authentication system for person identification and verification

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