The passive charge compensation (PCC) technique was introduced for switched capacitor (SC) circuit to increase the slew rate and enhance the linearity performance, as PCC techniques are used on the Delta-Sigma modulator (DSM) in ADC circuitry. The PCC technique of the project was applied to the design of a SC...
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...
It is common practice in the unsupervised anomaly detection literature to create experimental benchmarks by sampling from existing supervised learning datasets. We seek to improve this practice by identifying four dimensions important to real-world anomaly detection applications --- point difficulty, clusteredness of anomalies, relevance of features, and relative frequency of...
Various natural language processing (NLP) tasks necessitate deep models that are fast, efficient, and small based on their ultimate application at the edge or elsewhere. While significant investigation has furthered the efficiency and reduced the size of these models, reducing their downstream latency without significant trade-offs remains a difficult task....
Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing...
Advances in deep learning based image processing have led to their adoption for a wide range of applications, and in tow with these developments is a dramatic increase in the availability of high quality datasets. With this comes the need to accelerate and scale deep learning applications in order to...
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,...
Smart home devices are becoming increasingly popular and by 2021, it is estimated to have 80 million devices in the households of the U.S. The privacy and security threats involved with devices, as a result, are also scaling up in recent years. Smart home cameras have been hacked, private conversations...
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...
Is it possible to determine whether a signal violates a formula in Signal Temporal Logic (STL), if the monitor only has access to a low-resolution version of the signal? We answer this question affirmatively by demonstrating that temporal logic has a multiresolution structure, which parallels the multiresolution structure of signals....
The uncontrolled growth in domains such as surveillance systems, health care services, and finance produce a large amount of data and contain potentially sensitive data that can become public if they are not appropriately sanitized.
Motivated by this issue, we introduce a privacy filter (PF), a novel non-negative matrix factorization...
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...
There is a significant amount of research analyzing the effect of race, gender, and other common demographical data on student interest and performance in computer science. However, there is relatively little research concerning less common demographic populations, such as introverts, artistic students, and visual learners. This study investigates if these...
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...
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...
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...
Significance: Movement intent decoding algorithms can interpret human bioelectrical signals to control prosthetic limbs with many degrees of freedom (DOFs). This work involves decoding volitional movement intent from surface electromyogram (sEMG) signals to control prosthetic arms. To train these algorithms, patients flex their muscles to “follow” a movement prompt, and...
Movement intent decoders, which interpret volitional movement intent from human bioelectric signals, can be incorporated into modern neuroprostheses to offer people living with limb loss or paralysis the potential to regain their lost motor control. Machine learning methods have become the research standard for continuous decoders with high degrees of...
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...
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...
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...
Question Answering in natural language processing has achieved significant progress in recent years. Yet, training and testing set methodology to evaluate the language models has proved inadequate. Adversarial examples aid us in finding loopholes inside these models and provide insights into their inner workings. In this work, an evaluation based...
The tools and infrastructure used in tech, including Open Source Software (OSS), can embed “inclusivity bugs”—features that disproportionately disadvantage particular groups of contributors. To see whether OSS developers have existing practices to ward off such bugs, we surveyed 266 OSS developers. Our results show that a majority (77%) of developers...
Students’ success is one of the foremost objectives in higher education, and their self-efficacy plays a prominent role in students’ achieving their full potentials. It is especially important in STEM fields, which often suffer from higher attrition rates. Therefore, it is important to understand students’ self-efficacy levels at an early...
The Internet of Things (IoT) paradigm brought an ever-increasing dependence on low-power devices to collect sensor data and transmit that information to the cloud, placing greater demand on connectivity and lifespan. In response, rapid worldwide innovation demonstrates the trade-offs in processing, communication, and energy consumption with diverse approaches to low-power...
Smart home devices, such as voice assistants, smart lights, and smart video doorbells have become a part of end users' daily lives. Many of these devices combine their features with other services and smart devices to create a simple and efficient user experience. This is partly because of the contribution...
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,...
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...
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 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...
Magnetic materials can be used in modern soft robotics as a method for external stimulus actuation and motion control. By combining aspects of biology and mechanics, devices are fabricated to create a structure capable of complex movement. Applications that these devices are subject to can be broken down into four...
In the ever-evolving field of computer science (CS) education, the significance of teachers and their backgrounds have often been overshadowed by the predominant focus on students. Teachers in the K-12 often lack the necessary expertise and have limited support provided by existing CS-based curricula. While research on CS education effectiveness...
Modern datacenters are constructed with multirooted tree topologies and support multiple service queues per switch port. They support a wide variety of applications and services with stringent performance needs and conflicting requirements. To meet these requirements, recent works focus on load balancing or ECN marking approaches. Though existing load-balancing approaches...
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,...
With the advent of Artificial Intelligence (AI) in every sphere of life in today's day and age, it has become increasingly important for non-AI experts to be able to comprehend the underlying logic of how AI systems work, assess them and find faults in these systems, particularly when they are...
Tensor field topology is of importance to research areas of medicine, science, and engineering. Degenerate curves are one of the crucial topological features that provide valuable insights for tensor field visualization. In this thesis, we study the atomic bifurcations of degenerate curves in 3D linear second-order symmetric tensor fields, and...
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...
This study compares three approaches in the design of an autonomous machine listening agent that predicts harbor porpoise ultrasonic echolocation clicks in diverse noise environments. Considering the temporal variations of noisy coastal ocean soundscapes which the harbor porpoises inhabit, we propose a leave-one-day-out (LODO) cross-validation strategy in the training of...
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...
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...
Top-performing approaches to embodied AI tasks like point-goal navigation often rely on training agents via reinforcement learning over tens of millions (or even billions) of experiential steps – learning neural agents that map directly from visual observations to actions. In this work, we question whether these extreme training durations are...
Voltage fault injection is a technique to disrupt power supply, such that the data or instruction flow in a microcontroller can be modified. Recently, a new class of voltage glitches was introduced termed arbitrary wave voltage glitches. Despite its demonstrated success in practical studies it comes with additional challenges, such...
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...
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...
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...
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...
Materials with a strong spin Hall effect and Rashba-Edelstein effect have the potential to improve the efficiency of solid-state magnetic memory technologies and other magnetic logic devices. Heavy metals have been identified as having these properties, including the beta crystal phase of tungsten. In this work, we determine the strength...
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...
Relational binary operators, such as join, are arguably the most costly and frequently used operations in relational data systems. In many join algorithms, the majority of the process time is spent on scanning and attempting to join the parts of the relations that do not satisfy the join condition and...
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...
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...
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...
We consider three problems on simplicial complexes: the Optimal Bounded Chain Problem, the Optimal Homologous Chain Problem, and 2-Dim-Bounded-Surface. The Optimal Bounded Chain Problem asks to find the minimum weight d-chain in a simplicial complex K bounded by a given (d−1)-chain, if such a d-chain exists. The Optimal Homologous Chain...
The electrical grid of the Western Interconnection is vulnerable to earthquake damage, especially large-magnitude megathrust events, due to the unique seismic profile of the region. However, the size of the Western Interconnection makes it difficult to model seismic failure in electrical substations to the level of detail necessary to improve...
The Fréchet distance is a measure of similarity between curves or surfaces. The Fréchet distance between two polygons can be computed in polynomial time, but it is much harder to compute the Fréchet distance between surfaces. We present the first (1+ε)-approximation algorithm and the first exact algorithm for computing the...
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,...
NAND flash based solid state drives (SSDs) require out-of-place updating due to the characteristics of flash memories. In addition, due to the mismatched granularity between read/write and erase operations, a cleaning policy involving garbage collection and wear leveling has to perform data migration incurring high overhead. Another challenge is that...
Correctness and efficiency are important properties of programs. However, to support maintenance and debugging, the programs should also be understandable. Program explanations also play a vital role in educational settings, enhancing the understanding of programs among students.
Proof trees provide a sound basis for generating dynamic explanations of programs. But...
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...
RF-based signal identification and classification has received growing attention during recent years due to its potential use in many application domains. Of particular interest is Automatic Modulation Classification (AMC), which has been useful in addressing various spectrum related challenges such as signal jamming, policy enforcement, and spectrum sharing. Adopting AMC...
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...
The abilities of plant biologists to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation and mainly to collect this data on a high-throughput scale at low cost. Deep learning-based methods have demonstrated unprecedented potential to automate...
Autonomous vehicles bring great societal benefits but also potential impact and disruption to road safety, traffic congestion, and driving behaviors. One important technology that is indispensable to the success of such systems is vehicular networks. Vehicular networks provide the backbone for ensuring communication and connectivity among vehicles, all crucial to...
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...
Ocean Drifters are a low-cost and easy-to-use platform that are commonly used for studying ocean currents. With added interest in studying the ocean, emphasis has been taken to make it more accessible. A pilot course focused on introducing the topic of ocean sensing was announced. The HC 407 course will...
Microfluidic devices have enabled lab-on-a-chip (LOC) systems that allow for complex sample analysis and preparation in a compact form factor. One technology that may benefit from a microfluidic approach and further miniaturization is flow cytometry. Flow cytometry is an analysis technique for enumerating and characterizing populations of cells; this is...
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...
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...
Metal grating based plasmonic filters are widely researched for their unique properties of field enhancement and localization of light beyond diffraction limit. However, the plasmonic grating filters reported in literature mostly have broadband outputs making them unsuitable for spectroscopy. In this project, we have designed, fabricated, and characterized an array...
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...
Machine common sense remains a broad, potentially unbounded problem in AI. Our focus is to move toward AI systems that can develop common-sense reasoning similar to humans to detect anomalies. In particular, we study the problem of detecting the violation of expectations when object appearance or motion dynamics change from...
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...
Renewable energy technology continues to grow in popularity as countries aim to reduce greenhouse gas emissions. Land based and offshore wind turbines are one option for expanding renewable energy sources. However, as wind energy adoption increases, so does the need for enhanced monitoring of wind turbines’ potential effect on local...
Ocean Wave Energy Converters (WECs) are of interest around the globe as global economies begin to shift their interest to renewable forms of energy. However, the devices are costly to construct–a quality that can be relieved through proper modeling, control, and implementation. This paper presents the numerical and hybrid simulation...
Compositional data is a type of data where the features are non-negative and always sum to a constant. This type of data is frequently encountered in many fields such as microbiology, geology, economics and natural language processing. Compositional data has unique statistical properties that makes it difficult to apply standard...
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...
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 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...
Private matching (PM) is a key cryptographic primitive in secure computation that allows several parties to jointly compute some functions depending on their private inputs. Indeed, this primitive has many practical applications. For instance, in online advertising, two companies may wish to find their common customers for a joint marketing...
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...
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...
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...
Current methods for visualizing forest rely on geospatial and remote sensed data. Such data can be used to create visualization and to perform simulations. Currently, however, these visualizations are often limited to 2D or abstract representations. These methods can be effective for large scale data visualization and low accuracy needs....
We present student perceptions of a new first-year engineering programming class that was designed by informed research practices. While the College of Engineering at Oregon State University saw a lot of major switching in the first year, there were not many students switching into computer science (CS). This could have...
This Thesis aims to determine whether we can improve the accuracy, resolution, and speed of calculations for common power system problems using simple computational models that scale well to machine learning and high performance computing solutions. The second chapter of this Thesis implements more precise aging and degradation models for...
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...
Object detection models are being widely used in many applications, such as autonomous driving, construction management, and cancer detection. Evaluating the performance of the object detection model is more complicated than other computer vision models such as image classification models. Most of the images have several objects to be detected,...
Social media platforms use many techniques to engage users' attention with their platforms, including notifications, popups, and gamification elements. The impact of social media on physical and mental health has been studied, but limited publicly available research exists on how social media users can be helped to disengage from these...
The Gulf of Alaska (GOA) is home to the most productive fisheries in the world. In 2019, 2.2 million metric tons of fish were shipped from Alaska to destinations all over the world (NOAA Fisheries, 2019). From 2014-2016 and, more recently, in 2019 the largest heatwaves in recorded history caused...
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...
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...
AMD SEV allows for the creation of fully encrypted virtual machines. This allows cloud computing tenants’ data to be secret to the cloud computing provider. However, it has been shown that the encryption scheme used by AMD can easily be broken. The attacker can create a copy of the virtual...
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...
Traditional localization techniques rely on triangulation or trilateration, where in a set of three or more stationary known locations is used to estimate a “client” position. For inertial navigation, these techniques can estimate client positions merely using the measured data from tri-axial accelerometers and gyroscopes. However, the use of double...
In standard training regimes, one assumes that the classes presented to a model constitute all of the classes that the model will encounter when it is deployed. In real deployment scenarios, however, a model can sometimes encounter situations or objects that it has never seen. When these scenarios are safety-critical,...
Developers spend a considerable amount of time comprehending code and building accurate mental models of the code. Understanding the relationships between software features within IDEs is difficult, with information split across different visual hierarchies making navigation cumbersome. Canvas-based IDEs mitigate some of the navigation costs by allowing relevant information to...
Clos-based network topologies have been deployed in production data center networks to provide multiple path alternatives between the pairs of network hosts. Production data centers operate under varying traffic dynamics and topological asymmetry. Therefore, a good load balancing scheme must adapt to network conditions and dynamics in real-time and intelligently...
Recently, Explicit Congestion Notification (ECN) has been leveraged by most Datacenter Network (DCN) protocols for congestion control to achieve high throughput and low latency. However, the majority of these approaches assume that each switch port has one queue while current industry trends towards having multiple queues per switch port. To...
Current research into path planning for Unmanned Aerial Vehicles (UAVs) has placed a major focus on accomplishing the goals of various agents while avoiding collisions between each other. However, there has been little focus on whether the planned trajectories are fair for each agent.
This work provides an answer to...