We define an inner product on a vector space of adelic measures over a number field $K$. We find that the norm induced by this inner product governs weak convergence at each place of $K$. The canonical adelic measure associated to a rational map is in this vector space, and...
Iterative algorithms are simple yet efficient in solving large-scale optimization problems in practice. With a surge in the amount of data in past decades, these methods have become increasingly important in many application areas including matrix/tensor recovery, deep learning, data mining, and reinforcement learning. To optimize or improve iterative algorithms,...
In a radiological emergency scenario, the capability to rapidly visualize radiation emitted from debris, contamination, or absorbed in biological samples, while visualizing the non-radioactive (or “conventional” image) features within a field of view, will provide critical information to support optimization of further analysis, sample collection, and decision making. This research...
The Machine Learning (ML) algorithms are increasingly explored in varies of fields including designing and optimizing computer systems. Recent research, such as optimizing memory/cache prefetching by ML training or predicting traffic pattern in throughput processors, also exhibits a promising future of introducing ML into computer system design and optimization. Throughput...
This work gives some theory and efficient design of binary block codes capable of controlling the deletions of the symbol “0” (referred to as 0-deletions) and/or the insertions of the symbol “0” (referred to as 0-insertions). This problem of controlling 0-deletions and/or 0-insertions (referred to as symmetric 0-errors) is shown...
Throughput-oriented processors, such as graphics processing units (GPUs), have been increasingly used to accelerate general purpose computing, including machine learning models that are being utilized in numerous disciplines. Thousands of concurrently running threads in a GPU demand a highly efficient memory subsystem for data supply in GPUs. In this dissertation,...
We study joint nonlinear state estimation with multi-period measurement vectors that are potentially corrupted by sparse gross errors. The identifiability-aware approach is proposed to leverage common characteristics of fundamentally identifiable gross errors to enhance error correction performance. First, we derive a necessary rank condition that the sparsity pattern of any...
The ubiquity of high quality video and proliferation of mobile devices has contributed to an unprecedented rise in video consumption. HTTP, in conjunction with adaptive streaming, has become the de facto mechanism for delivering the vast majority of video as it readily caters to heterogeneous networks and devices. This dissertation...
This research focuses on receiver architectures which enable better spectral eciency
by handling blockers in the same spectral range as the signal. The presence of
such blockers, without the use of blocker cancelling/ ltering techniques leads to gain
compression and hence, consequent performance degradation of receivers leading to
reduced spectrum...
Traditional approaches to streaming H.264 video over a network typically rely on a single method of transport (i.e., reliable or unreliable) and/or use static values for parameters that can have a significant negative impact on the perceptual quality of the received video. This dissertation presents a dynamic method for wireless...