Scalable array transceivers with wide frequency tuning range are attractive for next-generationradios. Key challenges for such radios include generation of LO signals with widefrequency tuning range, scalable synchronization between multiple array unit cells andtolerance to in-band and out-of-band interferers. This thesis presents approaches toaddress these challenges in commercial CMOS technologies.The...
Over the past century, life expectancy in the United States has dramatically increased leading to an increasingly aging population with people reaching, and spending more years in ‘old age’. While this unprecedented shift in population demographics represents great strides for humanity, it is not without cost. One consequence of longer...
The complex, dynamic nature of microbial communities in both natural and engineered environments complicates the work of scientists and engineers who wish to channel microbial interactions for societal good. The successful management of these communities towards engineering goals is dependent on developing predictive linkages between community structure and functional outputs....
Society faces many complex management problems, particularly in the area of shared public resources such as ecosystems. Existing decision making processes are often guided by personal experience and political ideology rather than state-of-the-art scientific understanding. This dissertation envisions a future in which multiple stakeholders are provided with computational tools for...
The International Atomic Energy Agency (IAEA) is the leading organization for monitoring nuclear facilities worldwide, and the Agency’s methods are constantly developing and improving in an effort to more effectively safeguard nuclear material. As such, the IAEA addresses near and long term risks in order to advance the capabilities of...
The thesis focuses on activity recognition from sensor data, which has spurred a great deal of interest due to its impact on health care and security. Previous work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort...
In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...