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.

Cardiac magnetic resonance imaging (CMRI) has been proven to be a valuable source of diagnostic information concerning heart health. One application, myocardial blood flow (MBF) quantification using first-pass contrast-enhanced myocardial perfusion, has aided the detection of coronary artery disease and provides an accurate evaluation of myocardial ischemia, an identifier of coronary artery stenosis.

This work investigates how multipath propagation can be exploited to enhance the accuracy of AoA localization systems. The presented multipath assisted method resembles a fingerprinting approach, matching an AoA measurement vector to a set of reference vectors. 

Location based service (LBS) refers to the applications that depend on a user's location to provide services in various categories including navigation, tracking, advertising, healthcare and billing. With the explosive growing market of mobile phones in recent years, its demand is increasing with new ideas and becoming an irreplaceable part of life.