The ferromagnetic resonance (FMR) phenomenon serves as a sensitive probe of the effective internal fields in a magnetic material. FMR spectroscopy has consequently become a well-established technique, extensively employed in assessing material properties such as magnetic anisotropy, Landé g-factor, damping parameter, and the magnetoelastic constants of magnetic materials. Determining these...
This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...
Distributed version control allows developers to manage software evolution among distributed development teams. But it does not eliminate all consistency and concurrency issues, and instead introduces additional complexity when merging code. And resolving merge conflicts is nontrivial when automated merging fails. In such cases, developers are forced to inspect the...
Low-power receivers (RX) with 100$\mu W$-scale power consumption can enable several power/energy-constrained IoT applications. However, achieving sensitivity, interferer tolerance and wide operating range with low power presents a challenge for existing architectures, particularly those constrained to highly integrated solutions without high-Q off-chip components. Existing solutions rely heavily on high quality...
Autonomous robotic agents are on their way to becoming in-home personal assistants, construction assistants, and warehouse workers. The degree of autonomy of such systems is reflected by the manner in which we specify goals to them; the abstraction of low-level commands to high-level goals goes hand-in-hand with increased autonomy. In...
Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
Rapid and sensitive detection of stress hormones, such as cortisol and dehydroepiandrosterone (DHEA), can benefit the diagnosis of diseases related to adrenal gland disorders, post-traumatic stress disorders, chronic fatigue syndrome, and more. Stress hormones fluctuate in a circadian rhythm, the highest in the early morning and the lowest at night;...
Motivation: Many robots such as legged robots and prosthetic hands/arms are designed to interact with uncertain and time-varying environments to accomplish their tasks.
Observations on humans and animals during their daily tasks suggest that they adapt their leg compliance while traversing with different velocities on different grounds and adjust their...
A secret sharing scheme allows a dealer to distribute a secret with a set of parties, such that only a certain subset of parties can collaborate and learn the shared secret. Traditional secret sharing schemes have been used as building blocks in various subdomains of cryptography. Recently, two new extensions...
One of the pervasive problems arising in our modern, digital world surrounds data breaches where an adversary, through zero-day exploitations, phishing, or old-fashioned social engineering attacks, gains access to a service’s data stores. Our society increasingly relies on these cloud-based services for everything from our taxes to personal communication. As...
Additive manufacturing has become a promising method for the fabrication of inexpensive, green, flexible sensors and electronics. Printed electronics on low- temperature substrates are very appealing for the flexible hybrid electronics market for their use in disposable and biocompatible electronic applications and in areas like packaging, wearables, and consumer electronics....
While digital inclusivity researchers and software practitioners have been trying to address exclusion biases in Windows, Icons, Menus, and Pointers (WIMP) user interfaces (UIs) for a long time, little has been done to investigate if and how inclusive software design and its methods that have been devised for WIMP UIs...
Over time, Open Source Software (OSS) has become indispensable in the creation and upkeep of software products, serving as the fundamental building block for widely used solutions in our daily lives, including applications that enable communication, entertainment, and productivity. A sustainable OSS ecosystem is one that attracts and retains a...
In an increasingly computation-driven world, algorithms and mathematical models significantly impact decision making across various fields. To foster trust and understanding, it is crucial to provide users with clear and concise explanations of the reasoning behind the results produced by computational tools, especially when recommendations appear counterintuitive. Legal frameworks in...
This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
Secure Computation is a powerful tool that enables a set of parties to jointly compute any function over their private inputs, without a trusted third party. Private Set Intersection is a specific case of two-party Secure Computation, where Alice (with private set X) and Bob (with private set Y) specifically...
Generating abundant, renewable energy from Earth’s oceans is an attractive option for meeting increasing energy demand. Marine renewable energy also comes with the variability of renewable sources, which impact the reliability and power quality of the electrical grid. On a transmission-level, this dissertation looks at ensuring reliability of the power...
The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...
Machine learning applied to computer architecture has rapidly transitioned from a theoretical novelty to being a driving force behind design, control, and simulation in practically all components. These machine-learning-based methodologies are further notable for their scalability to increasingly complex design challenges, which has allowed these methodologies to surpass the prior...
As the fifth generation of mobile communication systems (5G) is being deployed, massive multiple-input multiple-output (MIMO) serves as one of its enabling technologies where reliable and ultra low latency communications are achieved while being power and spectrum efficient. This thesis studies the security aspect of massive MIMO system from the...
Radio frequency (RF) sensing arises as a promising option for enabling the internet of things (IoT) applications that transform our life into a world of smart homes, smart cities, and smart industries. The innovation of IoT reveals the benefits of RF sensing across cost, pervasiveness, unobtrusiveness, and privacy. However, challenges...
Global Positioning Systems have allowed for precise timing of power system measurements over wide areas. This newly found capability has the potential to provide much greater insight into the operation of the power system and its response to contingencies, but few analytical techniques currently exist that provide enough robustness and...
RNAs play important roles in multiple cellular processes, and many of their functions rely on folding to specific structures. To maintain their functions, secondary structures of RNA homologs are conserved across evolution. These conserved structures provide critical targets for diagnostics and therapeutics. Thus, there is a need for developing fast...
Ring Amplifier serves as a great candidate both for precision amplification and fast integration in the discrete time system. It can be utilized in high-performance analog-to-digital converters (ADCs). In high-speed ADC utilizing pipelined architectures with residue amplification, Successive-Approximation Register (SAR) ADCs as the sub-ADC and power efficient Ring Amplifier based...
Simultaneous speech translation (SimulST) is widely useful in many cross-lingual communication scenarios, including multinational conferences and international traveling. Since text-based simultaneous machine translation (SimulMT) has achieved great success in recent years. The conventional cascaded approach for SimulST uses a pipeline of streaming ASR followed by simultaneous MT but suffers from...
Secure two-party computation (2PC) is the task of performing arbitrary calculations on secret inputs provided by two parties, while maintaining secrecy if at least one party is honest. 2PC has been applied to privacy-preserving record linkage and machine learning, in areas such as medicine where maintaining privacy is crucial. One...
Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
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,...
Many home users nowadays use various smart devices to improve the efficiency and convenience of their home environments. Trigger-action platforms such as “If-This-Then-That” (IFTTT) enable end users to connect different smart devices and services using simple apps to control these devices and automate the tasks (e.g., if the camera detects...
Programming is integrated across the workflow of multiple domains where end-user programmers, those who need to program as a means to an end, regularly need to code. In the modern setting of collaborative development, end-user programmers have to interpret the intentions behind existing code to contribute and build solutions to...
With continuing improvements in performance and capability, GPU processing has gained significant and growing interest across science and industry. With this interest, research has increasingly focused upon methods of processing algorithms with stochastic, non-uniform branching while maintaining low divergence. Central among these methods is thread-data remapping (TDR), whereby data is...
Papers proposing novel machine learning algorithms tend to present the algorithm or technique in question in the best possible light. The standard practice is generally for authors to emphasize their proposed algorithms' performance in the precise setting where it is maximally impressive, often by only fully evaluating their best known...
Machine Translation, the task of automatically translating between human languages has been studied for decades. This task is used to be solved by count-based statistical models, e.g. Phrase-based Statistical Machine Translation (PBSMT), which solves the translation problem by separately training a statistical language model and a translation model. Recently, Neural...
We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
We do not know how to align a very intelligent AI agent's behavior with human interests. I investigate whether—absent a full solution to this AI alignment problem—we can build smart {\ai} agents which have limited impact on the world, and which do not autonomously seek power. In this thesis, I...
As one of the most popular data types, the point cloud is widely used in various appli- cations, including computer vision, computer graphics and robotics. The capability to directly measure 3D point clouds is invaluable in those applications as depth information could remove a lot of the segmentation ambiguities in...
Continuous Improvement (CI) of academic computing programs is a main requirement of accreditation. Academic computing programs must have a well-documented CI plan in order to be granted accreditation. Based on the existing literature, we developed a comprehensive CI (or 360-CI) model consisting of 8 components: course, curriculum, administration, faculty, research,...
Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...
This work – in which three peer-reviewed academic papers are presented – addresses the ap-plication of Bayesian Reinforcement Learning to the control of a class of ocean wave energy conversion systems. The first paper presents a comparison of a Reinforcement-Learning (RL) based wave energy converter controller against standard Reactive Damping...
Multiple wave energy converter (WEC) archetypes exist with varying power take-off (PTO) designs in the attempt to maximize ocean energy harnessed and converted into useful energy. The pendulum PTO is popular for its simple, yet robust functionality due to its internally located components and simple operation. Additionally, this PTO does...
In computer science, learning abstract fundamental programming concepts requiring students to understand memory management can be very difficult and lead to misunderstandings that carry on into advanced topics. This is especially true in data structures with abstract data types. Understanding how novice students think and reason about data structures is...
Wind turbines serve an increasing proportion of total energy generation, with expanded onshore and offshore installations proceeding worldwide. Continued construction, expansion, and operation of wind energy installations must be managed in conjunction with effects on local and migratory wildlife, specifically bird and bat species that may be affected by wind...
Despite near unanimous opinion on the consequences of climate change by scientific community, the rate at which carbon is emitted into the atmosphere continues to increase. The need for a clean and sustainable source of energy is therefore one of humankind's most urgent challenges. Solar energy is the most abundant...
Modern sensors are complex systems comprising multiple sub-systems such as transducers, analog and mixed-signal interface circuits, digital processing circuits, and packaging. Over the last few decades, innovations in these sub-systems combined with their increased integration in complementary metal-oxide semiconductor (CMOS) processes have led to the rapid growth in sensors for...
There has been tremendous growth in using data analytic and machine learning algorithms to make critical decisions, such as in the national power grid, healthcare operations, and autonomous vehicles. Employing data analytic for decision-making allows cyber attackers to manipulate the decisions of these algorithms through data falsification. Hence, the trustworthiness...
Data centers (DCs) have been witnessing unprecedented growth in size, number and complexity in recent years. They consist of tens of thousands of servers interconnected by fast network switches, hosting and enabling numerous applications with various traffic characteristics and requirements. As a result, DC networks have been presented with several...
Nowadays, wireless communication systems use high-order quadrature amplitude modulation (QAM) together with orthogonal frequency-division multiplexing (OFDM) to increase the link capacity and robustness. These signals always have large peak-to-average power ratio (PAPR) and require power amplifiers (PAs) achieve both high efficiency and linearity simultaneously. For the efficiency, the complementary metal...
Part I: Plasmonic color filters can be manufactured at lower cost since they can be fabricated in single lithographic process step as compared to Fabry-Perot based filters. In addition, they have narrow passband making resolving sharp features in sample spectrum possible. Due to these benefits, in this thesis, Plasmonic color...
Atomic layer deposition (ALD) is an enabling technique for many new micro- and nanoscale technologies. The self-limiting surface chemical reactions by which ALD fundamentally operates give rise to uniquely high precision (atomic) control over deposited film thickness, uniformity over large areas, and conformality over complex and extreme topographies. One such...
The ability to extract uncertainties from predictions is crucial for the adoption of deep learning systems to safety-critical applications. Uncertainty estimates can be used as a failure signal, which is necessary for automating complex tasks where safety is a concern. Furthermore, current deep learning systems do not provide uncertainty estimates,...
In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
This work addresses the application of Nonlinear Model Predictive Control (NMPC) to a class of ocean wave energy conversion systems in which the cost functional is not in a standard quadratic form, and the WEC model includes the nonlinear effects, such as the hydrodynamic viscous drag. The NMPC implementation is...
Ph.D. candidate Qi Wei's thesis consists of two projects: Chemotherapy Project: a study based on the research paper "Predicting chemotherapy response of various cancer types using a variational auto-encoder approach" submitted to the bioRxiv preprint archive and accepted by the BMC bioinformatics; and Wound Monitor Project: implementing and assessing analytics...
Many large-scale data analysis applications involve data that can vary over both time and space. Often the primary goal of analyzing spatiotemporal data is identifying trends, movements, and sudden changes with respect to time, location, or both. This can include a variety of applications in economics (housing prices, unemployment, job...
RNAs play important roles in the central dogma of molecular biology, and are involved in multiple biology processes such as chromatin modification, transcriptional interference and translation initiation. The functions of RNAs, especially non-coding RNAs, are highly related to its secondary structures, therefore computational methods for RNA structure prediction are of...
Developers frequently change the type of a program element and update all its references for performance, security, concurrency, library migration, or better maintainability. Despite type changes being a common program transformation, it is the least automated and the least studied. Manually performing type changes is tedious since the programmers have...
Dense electrical recording of biosignals has been developed to provide spatial resolution and precise temporal information for health monitoring, diagnostics, and clinical research. However, more electrodes require more wires, and wiring density quickly becomes a limiting factor. To break this bottleneck, we proposed a frequency-division multiplexing (FDM) based architecture for...
Deep learning is becoming the latest trend in sensitive applications, such as healthcare, criminal justice, and finance. As these new applications emerge, adversaries are circumventing them.
Further, there have been concerns about the possibility of bias and discrimination in predictive applications.
In order to address these issues, we propose an...
Unlike other biosensing, electrochemical biosensor has the advantages of robustness, easy miniaturization, excellent detection limits, and capability in handling small analyte volumes. The detection selectivity of the electrochemical biosensors is mainly supported by bioreceptors. However, these receptors always suffer from the performance variation caused by the environment and the thermal-...
Ecological domains seeking to understand the environment and the behavior of species have received little attention in machine learning (ML), despite the fact that environmental changes have a significant impact on humans as well as ecosystems. Some ecological problems can be formulated similarly to other common ML applications, but there...
In this thesis, I present the variational database management system, a formal framework and its implementation for representing variation in relational databases and managing variational information needs. A variational database is intended to support any kind of variation in a database. Specific kinds of variation in databases have already been...
Switched capacitor (SC) circuits are the main building blocks in many structures such as filters, data converters, sampling circuits and sampled-data amplifiers. The key challenge is to design such circuits which are the prime components of any IoT system with low power consumption without compromising on the performance. In this...
Over the last two decades, satisfiability and satisfiability-modulo theory (SAT/SMT) solvers have grown powerful enough to be general purpose reasoning engines throughout software engineering and computer science. However, most practical use cases of SAT/SMT solvers require not just solving a single SAT/SMT problem, but solving sets of related SAT/SMT problems....
N-ary relationships, which relate N entities where N is not necessarily two, are omnipresent in real life. In this thesis, we develop a visualization technique for N-ary relationships.
First, we propose a visual metaphor that utilizes vertices and polygons to represent entities and N-ary relationships. Based on this visual metaphor,...
This dissertation addresses few-shot object segmentation in images. The goal of segmentation is to label every image pixel with a class of the object occupying that pixel, where the class may represent a semantic object category or instance. In few-shot segmentation, training and test datasets have different classes. Every new...
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...
In weak supervision learning, label information can be provided at different levels of granularity. For example, in multi-instance multi-label learning, samples are organized into bags and labels for each class are provided at the bag level. For small datasets, this approach offers means of reducing the labeling efforts. However, in...
Increasing threats of terrorism and contraband smuggling have led to a growing interest in millimeter wave/ THz security sensors. Recently, systems combining active and passive sensing into one unit have been proposed where the active mode provides information on range and reflectivity, and the passive mode complements the active by...
Converting energy from ocean waves is a challenging area for control theory application because of the nonlinear dynamics in various time scales. Generally, wave energy converter (WEC) control is applied in order to maximize power absorption, in the most common wave conditions, and subject to the devices’ physical constraints. Commonly,...
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 presents a high-resolution Delta-Sigma ADC which combines the use of the pseudo-pseudo-differential noise filtering technique with single-ended ring amplifier based integrators. The pseudo-pseudo-differential noise filtering technique utilizes single-ended circuits while maintaining the even-order rejection found in fully-differential structures which alleviates, in the active analog blocks, the need for...
Sensor system plays an important role in connecting everything including human body to the electrical information systems that we have built and that we are going to build, to make the world more intelligent and efficient. One of the key propulsive forces behind these emerging techniques is CMOS scaling that...
Information about named entities (real-world objects) is usually harvested from different sources and organized as a multiple relational directed graph in Knowledge Bases (KBs). KBs play essential roles in many NLP modules including question answering, fact-checking, search engines, etc. KBs are big but still incomplete: relational information among entities is...
Network flows in Real-Time (RT) systems need to meet stringent end-to-end deadlines in order for such systems to operate safely and reliably. Today, such systems use custom or domain specific network system designs to meet end-to-end deadlines and other constraints of real-time flows. In this work we explore the design...
Emergence of highly accurate Convolutional Neural Networks (CNNs) with the capability to process large datasets, has led to their popularity in many applications, including safety/security-sensitive (e.g. disease recognition, self-driving cars). Despite the high accuracy of convolutional neural networks, they have been found to be susceptible to adversarial noise added to...
In this dissertation, we propose Ideal Thumbnail-Preserving Encryption (Ideal TPE), as a special case of format-preserving encryption, to balance image privacy and usability concerns in a cloud environment. We first introduce a concrete construction for Ideal TPE, that provably leaks nothing about the plaintext (unencrypted) image beyond its thumbnail. We...
There are five main contributions of this dissertation. The first contribution is new closed-form expressions for channel capacity of a new class of channel matrices. The second contribution is the discovery of the structure for optimal binary quantizer and the associated methods for finding an optimal quantizer that maximizes mutual...
We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...
Spectrum overcrowding, ever increasing demand for high data rate and increased mobility requirements are three major challenges 5G-technology is trying to address. In this thesis I start with a RF front-end technique that deals with blocker interference arising from spectrum overcrowding both across frequency bands and within the same frequency...
In this dissertation, we address action segmentation in videos under limited supervision. The goal of action segmentation is to predict an action class for each frame of a video. The limited supervision means ground truth labels of video frames are not available in training. We focus on three types of...
Impedance measurements are increasingly demanded in modern CMOS sensing systems as impedance is the most common electrical signal obtained from sensors, delivering physical, chemical and biomedical quantity changes. Impedance sensing for wide interested frequency, broad dynamic range, and various sensor interfaces has numerous challenges, especially targeted in CMOS miniaturization with...
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,...
The sensors in real time data processing IoT devices require high resolution and sub-MHz data converters, usually implemented as Incremental ADCs due to the advantages of oversampling technique and low latency. In discrete time incremental (IDT) ADCs, the sampling switch non-linearity, charge injection degrade the resolution, and power hungry OPAMPs...
The need for sustainably powering unobtrusive internet-of-things applications has led to an interest in energy harvesting. Particularly, the proliferation of wireless communication and devices in the 2.4 GHz Industrial Scientificc Medical (ISM) band creates an opportunity to leverage commonly used devices for RF powering. This dissertation presents a low-quiescent-power...
Software systems are becoming an essential part of the lives of both individuals and organizations, and as a consequence, these systems are getting bigger and more complex. Because of this, the tasks of maintaining the quality in these complex software systems are becoming increasingly difficult. Furthermore, these systems are subject...
Software entropy impacts the overall quality of software systems. High entropy hinders developers from understanding the purpose of a piece of code and can cause developers to make sub-optimal changes and introduce bugs. Researchers have used entropy scores to measure the naturalness of code. However, thus far, no one has...
Silicon photonics has become the most promising platform for future large-scale optical interconnect and optical computing systems due to its inherent CMOS compatibility, which brings exclusive advantages in bandwidth density, energy efficiency, and cost effectiveness. Parallel optical interconnects based on photonic integrated circuits (PICs) have the capacity to meet the...
Merge conflicts have long plagued software development. With larger and more dispersed teams comes greater risk of developers working on the same code at the same time. While merge conflicts are known to be painful, their exact impact on software is still largely unknown. Are merge conflicts an isolated problem,...
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...
Ever increasing global internet data traffic has driven up the demand for cutting-edge high-speed wireline communication systems including SerDes PHY for various interfaces, interconnects, data centers servers and switches in optical systems. Operating wireline communications at higher data rates leads to signals suffering from greater channel loss and exponential increase...
Emerging fifth generation (5G) and beyond 5G communication networks are stimulating the design of radio-frequency (RF) and millimeter-wave (mmWave) integrated circuits for wireless transceivers systems. While co-integration of active circuits and diverse passive components becomes feasible at these high operating frequencies, circuit design is faced with significant challenges due to...
The electrical grid is a key component of the Nation's critical infrastructure. Its continuous and reliable operation is of vital importance; any system-wide disruption would have a debilitating impact on crucial services, public health and safety, the economy, and the national security of the United States.
High-impact low-frequency events pose...
Due to the limitation of the radio frequency (RF) spectrum, it is increasingly more difficult to support billions of wireless devices in the age of Internet-of-Things. Consequently, many recent wireless indoor communication systems have been developed using free space optical (FSO) communication technologies that exploit the extremely large light spectrum...
Simultaneous translation, which translates concurrently with the source language speech, is widely used in many scenarios including multilateral organizations. However, it is well known to be one of the most challenging tasks for humans due to the simultaneous perception and production in two languages. On the other hand, simultaneous translation...
Advances in sensor technology are greatly expanding the range of quantities that can be measured while simultaneously reducing the cost. However, deployed sensors drift out of calibration and fail, so every sensor network requires quality control procedures to promptly detect these failures. To address these problems, we propose a two-level...
This dissertation proposes the use of advanced time-varying approaches for modeling the dynamics of the multipath channel in wireless communication networks. These advanced time-varying approaches include linear Kalman innovation models in observable block companion form, and neural network-based models. The e˙ectiveness of these type of models is evaluated through three...
This work presents a novel CCRW receiver that utilizes a window of variable width, for e˙ectively mitigating multipath and ambiguity in both civil and military positioning applica-tions using Global Navigation Satellite Systems (GNSS). This CCRW receiver incorporates a single stroboscopic window, whose width is iteratively reduced until the e˙ect of...