Ph.D Theses

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10 years of news and resources for members of the IEEE Signal Processing Society

Ph.D Theses

Optical fiber communication systems provide the infrastructure for high-capacity data transfer. The field has shown dramatic increases in transmission capacity, and yet the demand for capacity also grows significantly. We are in a situation in which systems must continually grow in capacity to keep pace with the demand, thereby necessitating continual innovation and technical advances.

More than one million fetal deaths occur in the United States every year. Monitoring the long-term heart rate variability provides a great amount of information about the fetal health condition which requires continuous monitoring of the fetal heart rate.

Applications of semidefinite optimization in signal processing are often derived from the Kalman-Yakubovich-Popov lemma and its extensions, which give sum-of-squares theorems of nonnegative trigonometric polynomials and generalized polynomials.

Large-scale networks are becoming more prevalent, with applications in healthcare systems, financial networks, social networks, and traffic systems. The detection of normal and abnormal behaviors (signals) in these systems presents a challenging problem.

This work investigates two different digital signal processing (DSP) approaches that rely on signal-derived timing: continuous-time (CT) DSP and variable-rate DSP. Both approaches enable designs of energy-efficient signal processing systems by relating their operation rates to the input activity.

This study investigates various aspects of multi-speaker interference and its impact on speaker recognition. Single-channel multi-speaker speech signals (aka co-channel speech) comprise a significant portion of speech processing data. Examples of co-channel signals are recordings from multiple speakers in meetings, conversations, debates, etc.

Improving the modeling and processing of nonstationary signals remains an important yet challenging problem. In the past, the most effective approach for processing these signals has been statistical modeling.

Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things (IoT). As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by system architects and design engineers, and present a new set of design trade-offs.

In recent years, the complexity of designing embedded signal processing systems for wireless communications has increased significantly based on the need to support increasing levels of operational flexibility and adaptivity, while also supporting increasing data rates and bandwidths.

This dissertation focuses on statistical signal processing theory and its applications to radar, complex-valued signal processing and model selection.

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