This talk first provides a general introduction to AANs integrated networks based on physical, MAC, and networking layer requirements, followed by some state-of-the-art AANs along with possible applications. 

In this dissertation, two pivotal problems in space-time reconstruction are explored: the first is how to have a wider spatial-temporal bandwidth in the spacetime reconstruction. The second is how to use temporal and physical information to help with space-time reconstruction.

Abstract

With one or more constitutive parameters vanishing, zero-index

The demands for using optical wireless communication (OWC) have increased in recent years due to the bandwidth limitation of the radio frequency (RF) communication link. Ultraviolet-based communication, in particular, has gained attraction due to its robustness against channel misalignment and wavelength-beam blockage.

CMOS (Complementary Metal-Oxide-Semiconductor) technology has long held a preeminent position as the quintessential choice for constructing microprocessors.

This dissertation focuses on the fabrication and characterization of InGaN/GaN multiple quantum well (MQW) micro-light-emitting diodes (micro-LEDs) and photodetectors (PDs) for advanced display and wireless communication systems.

Monocular depth estimation, the task of inferring depth information from a single RGB image, is a fundamental yet challenging problem in computer vision due to its inherently ill-posed nature. This dissertation presents a series of approaches that significantly advance the state-of-the-art in depth estimation.

In addressing the challenges of expanding wireless coverage to unconnected regions, the space-air-ground integrated network (SAGIN) emerges as a transformative paradigm designed to meet the demands for high-rate and high-reliability communications. This dissertation introduces SAGIN, comprising satellite, aerial, and terrestrial components, as a two-hop relay network enhanced by cooperative links between space-air and air-ground segments.

In this talk, we will illustrate the application of SIM in multiple-input multiple-output (MIMO) and multi-user MIMO wireless communications.

Molecular communication (MC) is a promising paradigm for information transmission in complex environments, such as living cells and porous media. While most existing works consider standard diffusion, where the mean square displacement (MSD) of information molecules (IMs) scales linearly with time, this dissertation focuses on sub-diffusive dynamics in crowded and complex environments. The primary objectives of this research are to model, simulate, and analyze the performance of MC systems in sub-diffusive environments.

In this talk, we will provide a critical analysis of the state of the art in MC research and outline the training and research program of SyMoCADS with the objective of inspiring similar programs elsewhere.

This dissertation examines the deployment of Low Earth Orbit (LEO) satellite networks to enhance Internet of Things (IoT) connectivity across extensive geographic regions, including remote and rural areas where terrestrial infrastructure is insufficient. Through a comprehensive study structured into three main areas, this research addresses uplink performance, energy sustainability, and security challenges associated with LEO satellite communications.

In this talk, I will present a vision of future research topics of long-term importance in the area of Satellite Communications and Non-Terrestrial Networks.

The aim of this thesis is to understand and analyze diffusive and thermal effects in multicomponent systems for gas mixtures through the perspective of partial differential equations. Starting from Class--II models of thermodynamics, diffusion equations are derived formally by a Chapman--Enskog expansion and the expansion is justified as a relaxation limit by means of the relative entropy method.

Numerous problems in scientific computing can be formulated as optimization problems of suitable parametric models over parameter spaces. Neural network and deep learning methods provide unique capabilities for building and optimizing such models, especially in high-dimensional settings.

Disordered sound fields are ubiquitous in our daily life – the sound in a room undergoes multiple scattering form such a sound field. These fields are inherently random and traditionally challenging to manipulate. In our research, we demonstrate that reconfigurable acoustic metasurfaces can effectively reshape reverberating sound fields. By actively adjusting the reflective phases, these metasurfaces can 're-sculpt' the acoustic environment. The specific acoustic metasurface we have developed includes 200 units of electronically tunable Helmholtz resonators. Each unit can switch between two phase states, allowing for dynamic control of reflected waves over a wide frequency range from 1000 to 1700 Hz. This tunability is achieved through individual control of each resonator, utilizing a series of feedback-driven optimization algorithms.

Currently, the acquisition of accurate cryogenic electron microscopy data deals with problems with complex and time-consuming processes, low signal-to-noise ratio, and missing wedge, leading to a lack of highly accurate imaging data. Such data would be necessary to develop computational methods/visualizations and essential to train deep learning models that are used to solve inverse problems.

The Dual-function radar communication (DFRC) refers to an integrated system that performs both the functions of a radar and a communication system. It is designed by exploiting the tractability and reusability of both radar and communication systems' components, parameters, and spectrum to achieve an integrated system. This dissertation explores and exploits the flexibility in the transmit beampattern design in MIMO radar systems to implement the transmission of communication symbols.

I will address the concept of multimodal integration, where information from conventional sensors and network sensing modalities can be fused for different purposes.

With the ever-increasing amount of cyberthreats out there, securing IT and OT infrastructures against these threats has become not only desirable, but fundamental. Network Intrusion Detection Systems (NIDS) are key assets for system protection, providing early alerts of network attacks. An important class of NIDS are those based on ML techniques, around which a substantial amount of research is being done these days. Unfortunately, being ML-based, these NIDS can be targeted by adversarial evasion attacks (AEA), which malicious parties try to exploit to perform network attacks without being detected.

The first part of the dissertation presents a study on the convergence properties of Stein Variational Gradient Descent (SVGD), a sampling algorithm with applications in machine learning. The research delves into the theoretical analysis of SVGD in the population limit, focusing on its behavior under various conditions, including the Talagrand’s inequality T1 and the (L0, L1)−smoothness condition. The study also introduces an improved version of SVGD with importance weights, demonstrating its potential to accelerate convergence and enhance stability.

Environmental statistics play a critical role in various interconnected domains, encompassing weather and climate forecasting, air quality monitoring, and sustainable urban planning. However, because of their high inherent unpredictability and nonstationarity, modeling complex spatio-temporal dynamics of environmental processes is challenging. This dissertation develops a set of DNN based methods for large-scale spatial and spatio-temporal processes.

DNA replication is a fundamental cellular process and its precise regulation is crucial for maintaining genomic integrity. Inspired by the mathematical parallelism between DNA replication and the nucleation problem in one-dimensional crystal growth kinetics, we introduce a model that maps whole-genome replication dynamics based on the firing rate profiles of replication origins and fork movement.

Deep Learning and generative Artificial Intelligence has grown rapidly during the past few years due to the advancement of computing powers and parallel distributed training algorithms. As a result, it has been a common practice to use hundreds or thousands of machines to train very large Deep Neural Networks.

This thesis explores advanced statistical models and methods for analyzing multivariate and functional time series data. It focuses on various aspects of statistical analysis, including visualization, robust outlier detection, inference, and forecasting. It addresses challenges in outlier detection for functional data, quantile spectral estimation for multivariate time series, and high-dimensional functional time series forecasting, with applications in environmental, financial, and demographic fields.