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2022

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Articles 18001 - 18030 of 18295

Full-Text Articles in Physical Sciences and Mathematics

Application Of Long Short-Term Memory (Lstm) Neural Network Based On Deeplearning For Electricity Energy Consumption Forecasting, Mehmet Bi̇lgi̇li̇, Ni̇yazi̇ Arslan, Ali̇i̇hsan Şekerteki̇n, Abdulkadi̇r Yaşar Jan 2022

Application Of Long Short-Term Memory (Lstm) Neural Network Based On Deeplearning For Electricity Energy Consumption Forecasting, Mehmet Bi̇lgi̇li̇, Ni̇yazi̇ Arslan, Ali̇i̇hsan Şekerteki̇n, Abdulkadi̇r Yaşar

Turkish Journal of Electrical Engineering and Computer Sciences

Electricity is the most substantial energy form that significantly affects the development of modern life, work efficiency, quality of life, production, and competitiveness of the society in the ever-growing global world. In this respect, forecasting accurate electricity energy consumption (EEC) is fairly essential for any country?s energy consumption planning and management regarding its growth. In this study, four time-series methods; long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with subtractive clustering (SC), ANFIS with fuzzy cmeans (FCM), and ANFIS with grid partition (GP) were implemented for the short-term one-day ahead EEC prediction. Root mean square error (RMSE), …

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Evaluating The English-Turkish Parallel Treebank For Machine Translation, Onur Görgün, Olcay Taner Yildiz Jan 2022

Evaluating The English-Turkish Parallel Treebank For Machine Translation, Onur Görgün, Olcay Taner Yildiz

Turkish Journal of Electrical Engineering and Computer Sciences

This study extends our initial efforts in building an English-Turkish parallel treebank corpus for statistical machine translation tasks. We manually generated parallel trees for about 17K sentences selected from the Penn Treebank corpus. English sentences vary in length: 15 to 50 tokens including punctuation. We constrained the translation of trees by (i) reordering of leaf nodes based on suffixation rules in Turkish, and (ii) gloss replacement. We aim to mimic human annotator?s behavior in real translation task. In order to fill the morphological and syntactic gap between languages, we do morphological annotation and disambiguation. We also apply our heuristics by …

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A Simple Dual-Band Quasi-Yagi Antenna With Defected Ground Structures, Göksel Turan, Hayretti̇n Odabaşi Jan 2022

A Simple Dual-Band Quasi-Yagi Antenna With Defected Ground Structures, Göksel Turan, Hayretti̇n Odabaşi

Turkish Journal of Electrical Engineering and Computer Sciences

In this article, a dual-band compact quasi-Yagi antenna with defected ground structure (DGS) is proposed. The proposed antenna has a simple feeding mechanism consists of a microstrip and transmission line. Half of the driver and director elements are printed on the opposite side of the substrate to ensure good coupling between the antenna elements and achieve a stable radiation pattern. The ground plane is modified with one rectangular slot below the microstrip line to form dual-band operation. Also rectangular slots placed on the sides of the ground plane to improve the matching. The proposed antenna works at $f_{1}=3.35$ and $f_{2}=6.15$ …

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Shape Investigations Of Structures Formed By The Self-Assembly Of Aromaticamino Acids Using The Density-Based Spatial Clustering Of Applications With Noise Algorithm, Mehmet Gökhan Habi̇boğlu, Helen W. Hernandez, Şahi̇n Uyaver Jan 2022

Shape Investigations Of Structures Formed By The Self-Assembly Of Aromaticamino Acids Using The Density-Based Spatial Clustering Of Applications With Noise Algorithm, Mehmet Gökhan Habi̇boğlu, Helen W. Hernandez, Şahi̇n Uyaver

Turkish Journal of Electrical Engineering and Computer Sciences

Tyrosine, tryptophan, and phenylalanine are important aromatic amino acids for human health. If they are not properly metabolized, severe rare mental or metabolic diseases can emerge, many of which are not researched enough due to economic priorities. In our previous simulations, all three of these amino acids are discovered to be self-organizing and to have complex aggregations at different temperatures. Two of these essential stable formations are observed during our simulations: tubular-like and spherical-like structures. In this study, we develop and implement a clustering analyzing algorithm using density-based spatial clustering of applications with noise (DBSCAN) to measure the shapes of …

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Automatically Classifying Familiar Web Users From Eye-Tracking Data:A Machine Learning Approach, Meli̇h Öder, Şükrü Eraslan, Yeli̇z Yesi̇lada Jan 2022

Automatically Classifying Familiar Web Users From Eye-Tracking Data:A Machine Learning Approach, Meli̇h Öder, Şükrü Eraslan, Yeli̇z Yesi̇lada

Turkish Journal of Electrical Engineering and Computer Sciences

Eye-tracking studies typically collect enormous amount of data encoding rich information about user behaviours and characteristics on the web. Eye-tracking data has been proved to be useful for usability and accessibility testing and for developing adaptive systems. The main objective of our work is to mine eye-tracking data with machine learning algorithms to automatically detect users' characteristics. In this paper, we focus on exploring different machine learning algorithms to automatically classify whether users are familiar or not with a web page. We present our work with an eye-tracking data of 81 participants on six web pages. Our results show that …

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Stability Regions In Time Delayed Two-Area Lfc System Enhanced By Evs, Ausnain Naveed, Şahi̇n Sönmez, Saffet Ayasun Jan 2022

Stability Regions In Time Delayed Two-Area Lfc System Enhanced By Evs, Ausnain Naveed, Şahi̇n Sönmez, Saffet Ayasun

Turkish Journal of Electrical Engineering and Computer Sciences

With the extensive usage of open communication networks, time delays have become a great concern in load frequency control (LFC) systems since such inevitable large delays weaken the controller performance and even may lead to instabilities. Electric vehicles (EVs) have a potential tool in the frequency regulation. The integration of a large number of EVs via an aggregator amplifies the adverse effects of time delays on the stability and controller design of LFC systems. This paper investigates the impacts of the EVs aggregator with communication time delay on the stability. Primarily, a graphical method characterizing stability boundary locus is implemented. …

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Eye Movement And Pupil Measures: A Review, Bhanuka Mahanama, Yasith Jayawardana, Sundararaman Rengarajan, Gavindya Jayawardena, Leanne Chukoskie, Joseph Snider, Sampath Jayarathna Jan 2022

Eye Movement And Pupil Measures: A Review, Bhanuka Mahanama, Yasith Jayawardana, Sundararaman Rengarajan, Gavindya Jayawardena, Leanne Chukoskie, Joseph Snider, Sampath Jayarathna

Computer Science Faculty Publications

Our subjective visual experiences involve complex interaction between our eyes, our brain, and the surrounding world. It gives us the sense of sight, color, stereopsis, distance, pattern recognition, motor coordination, and more. The increasing ubiquity of gaze-aware technology brings with it the ability to track gaze and pupil measures with varying degrees of fidelity. With this in mind, a review that considers the various gaze measures becomes increasingly relevant, especially considering our ability to make sense of these signals given different spatio-temporal sampling capacities. In this paper, we selectively review prior work on eye movements and pupil measures. We first …

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Introducing A Real-Time Advanced Eye Movements Analysis Pipeline, Gavindya Jayawardana Jan 2022

Introducing A Real-Time Advanced Eye Movements Analysis Pipeline, Gavindya Jayawardana

Computer Science Faculty Publications

Real-Time Advanced Eye Movements Analysis Pipeline (RAEMAP) is an advanced pipeline to analyze traditional positional gaze measurements as well as advanced eye gaze measurements. The proposed implementation of RAEMAP includes real-time analysis of fixations, saccades, gaze transition entropy, and low/high index of pupillary activity. RAEMAP will also provide visualizations of fixations, fixations on AOIs, heatmaps, and dynamic AOI generation in real-time. This paper outlines the proposed architecture of RAEMAP.

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Multi-User Eye-Tracking, Bhanuka Mahanama Jan 2022

Multi-User Eye-Tracking, Bhanuka Mahanama

Computer Science Faculty Publications

The human gaze characteristics provide informative cues on human behavior during various activities. Using traditional eye trackers, assessing gaze characteristics in the wild requires a dedicated device per participant and therefore is not feasible for large-scale experiments. In this study, we propose a commodity hardware-based multi-user eye-tracking system. We leverage the recent advancements in Deep Neural Networks and large-scale datasets for implementing our system. Our preliminary studies provide promising results for multi-user eye-tracking on commodity hardware, providing a cost-effective solution for large-scale studies.

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Completing Single-Cell Dna Methylome Profiles Via Transfer Learning Together With Kl-Divergence, Sanjeeva Dodlapati, Zongliang Jiang, Jiangwen Sun Jan 2022

Completing Single-Cell Dna Methylome Profiles Via Transfer Learning Together With Kl-Divergence, Sanjeeva Dodlapati, Zongliang Jiang, Jiangwen Sun

Computer Science Faculty Publications

The high level of sparsity in methylome profiles obtained using whole-genome bisulfite sequencing in the case of low biological material amount limits its value in the study of systems in which large samples are difficult to assemble, such as mammalian preimplantation embryonic development. The recently developed computational methods for addressing the sparsity by imputing missing have their limits when the required minimum data coverage or profiles of the same tissue in other modalities are not available. In this study, we explored the use of transfer learning together with Kullback-Leibler (KL) divergence to train predictive models for completing methylome profiles with …

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Visual Descriptor Extraction From Patent Figure Captions: A Case Study Of Data Efficiency Between Bilstm And Transformer, Xin Wei, Jian Wu, Kehinde Ajayi, Diane Oyen Jan 2022

Visual Descriptor Extraction From Patent Figure Captions: A Case Study Of Data Efficiency Between Bilstm And Transformer, Xin Wei, Jian Wu, Kehinde Ajayi, Diane Oyen

Computer Science Faculty Publications

Technical drawings used for illustrating designs are ubiquitous in patent documents, especially design patents. Different from natural images, these drawings are usually made using black strokes with little color information, making it challenging for models trained on natural images to recognize objects. To facilitate indexing and searching, we propose an effective and efficient visual descriptor model that extracts object names and aspects from patent captions to annotate benchmark patent figure datasets. We compared two state-of-the-art named entity recognition (NER) models and found that with a limited number of annotated samples, the BiLSTM-CRF model outperforms the Transformer model by a significant …

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Customer Gaze Estimation In Retail Using Deep Learning, Shashimal Senarath, Primesh Pathirana, Dulani Meedeniya, Sampath Jayarathna Jan 2022

Customer Gaze Estimation In Retail Using Deep Learning, Shashimal Senarath, Primesh Pathirana, Dulani Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

At present, intelligent computing applications are widely used in different domains, including retail stores. The analysis of customer behaviour has become crucial for the benefit of both customers and retailers. In this regard, the concept of remote gaze estimation using deep learning has shown promising results in analyzing customer behaviour in retail due to its scalability, robustness, low cost, and uninterrupted nature. This study presents a three-stage, three-attention-based deep convolutional neural network for remote gaze estimation in retail using image data. In the first stage, we design a mechanism to estimate the 3D gaze of the subject using image data …

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D-Lib Magazine Pioneered Web-Based Scholarly Communication, Michael L. Nelson, Herbert Van De Sompel Jan 2022

D-Lib Magazine Pioneered Web-Based Scholarly Communication, Michael L. Nelson, Herbert Van De Sompel

Computer Science Faculty Publications

The web began with a vision of, as stated by Tim Berners-Lee in 1991, “that much academic information should be freely available to anyone”. For many years, the development of the web and the development of digital libraries and other scholarly communications infrastructure proceeded in tandem. A milestone occurred in July, 1995, when the first issue of D-Lib Magazine was published as an online, HTML-only, open access magazine, serving as the focal point for the then emerging digital library research community. In 2017 it ceased publication, in part due to the maturity of the community it served as well as …

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Machine Learning-Based Event Generator For Electron-Proton Scattering, Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A.N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. Mcclellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, L. Velasco Jan 2022

Machine Learning-Based Event Generator For Electron-Proton Scattering, Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A.N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. Mcclellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, L. Velasco

Computer Science Faculty Publications

We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to construct a vertex-level event generator free of theoretical assumptions about femtometer scale physics. Our framework includes a GAN-based detector folding as a fast-surrogate model that mimics detector simulators. The framework is tested and validated on simulated inclusive deep-inelastic scattering data along with existing parametrizations for detector simulation, with uncertainty quantification based on a statistical bootstrapping technique. Our results provide for the first time a realistic proof of concept to mitigate theory bias in inferring vertex-level event …

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Toward A Real-Time Index Of Pupillary Activity As An Indicator Of Cognitive Load, Gavindya Jayawardena, Yasith Jayawardana, Sampath Jayarathna, Jonas Högström, Thomas Papa, Deepak Akkil, Andrew T. Duchowski, Vsevolod Peysakhovich, Izabela Krejtz, Nina Gehrer, Krzysztof Krejtz Jan 2022

Toward A Real-Time Index Of Pupillary Activity As An Indicator Of Cognitive Load, Gavindya Jayawardena, Yasith Jayawardana, Sampath Jayarathna, Jonas Högström, Thomas Papa, Deepak Akkil, Andrew T. Duchowski, Vsevolod Peysakhovich, Izabela Krejtz, Nina Gehrer, Krzysztof Krejtz

Computer Science Faculty Publications

The Low/High Index of Pupillary Activity (LHIPA), an eye-tracked measure of pupil diameter oscillation, is redesigned and implemented to function in real-time. The novel Real-time IPA (RIPA) is shown to discriminate cognitive load in re-streamed data from earlier experiments. Rationale for the RIPA is tied to the functioning of the human autonomic nervous system yielding a hybrid measure based on the ratio of Low/High frequencies of pupil oscillation. The paper's contribution is drawn from provision of documentation of the calculation of the RIPA. As with the LHIPA, it is possible for researchers to apply this metric to their own experiments …

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Ready Raider One: Exploring The Misuse Of Cloud Gaming Services, Guannan Liu, Daiping Liu, Shuai Hao, Xing Gao, Kun Sun, Haining Wang Jan 2022

Ready Raider One: Exploring The Misuse Of Cloud Gaming Services, Guannan Liu, Daiping Liu, Shuai Hao, Xing Gao, Kun Sun, Haining Wang

Computer Science Faculty Publications

Cloud gaming has become an emerging computing paradigm in recent years, allowing computer games to offload complex graphics and logic computation to the cloud. To deliver a smooth and high-quality gaming experience, cloud gaming services have invested abundant computing resources in the cloud, including adequate CPUs, top-tier GPUs, and high-bandwidth Internet connections. Unfortunately, the abundant computing resources offered by cloud gaming are vulnerable to misuse and exploitation for malicious purposes. In this paper, we present an in-depth study on security vulnerabilities in cloud gaming services. Specifically, we reveal that adversaries can purposely inject malicious programs/URLs into the cloud gaming services …

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Scholarly Big Data Quality Assessment: A Case Study Of Document Linking And Conflation With S2orc, Jian Wu, Ryan Hiltabrand, Dominik Soós, C. Lee Giles Jan 2022

Scholarly Big Data Quality Assessment: A Case Study Of Document Linking And Conflation With S2orc, Jian Wu, Ryan Hiltabrand, Dominik Soós, C. Lee Giles

Computer Science Faculty Publications

Recently, the Allen Institute for Artificial Intelligence released the Semantic Scholar Open Research Corpus (S2ORC), one of the largest open-access scholarly big datasets with more than 130 million scholarly paper records. S2ORC contains a significant portion of automatically generated metadata. The metadata quality could impact downstream tasks such as citation analysis, citation prediction, and link analysis. In this project, we assess the document linking quality and estimate the document conflation rate for the S2ORC dataset. Using semi-automatically curated ground truth corpora, we estimated that the overall document linking quality is high, with 92.6% of documents correctly linking to six major …

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Edge Fueling And Neutral Density Studies Of The Alcator C-Mod Tokamak Using The Solps-Iter Code, Richard M. Reksoatmodjo Jan 2022

Edge Fueling And Neutral Density Studies Of The Alcator C-Mod Tokamak Using The Solps-Iter Code, Richard M. Reksoatmodjo

Dissertations, Theses, and Masters Projects

Understanding edge neutral dynamics in high-field tokamaks has strong consequencesfor both fueling and plasma profile predictions. We validate the ability of SOLPS-ITER, a 2D fluid plasma/kinetic Monte Carlo neutral code, to accurately model the upstream neutral density profiles of L-mode, I-mode, and H-mode discharges in the Alcator CMod tokamak, for which Lyman-alpha emission measurements were available. We achieve simulated Lyman-alpha emission and neutral density profiles that are within one standard deviation of empirically inferred profiles for all three discharges, via iterative tuning of the perpendicular transport coefficient profiles alone, providing confidence in the conclusion that while further physics (drifts, impurities, …

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Exploring The Effects Of Microplastics On Marine Biota, Meredith Evans Seeley Jan 2022

Exploring The Effects Of Microplastics On Marine Biota, Meredith Evans Seeley

Dissertations, Theses, and Masters Projects

There is mounting evidence that microplastics are a persistent and increasing hazard for aquatic organisms. The effects of microplastics on organisms and ecosystems are complex, however, and may be linked to a wide variety of particle characteristics including size, shape, polymer, additive chemistry, and degree of weathering. Assessing risk is complicated by the fact that many known effects of microplastics are sublethal, and that plastics have been postulated to interact with other stressors, such as pathogens. The work presented here expands our understanding of these complex effects. First, the impacts of microplastics on sedimentary microbial ecosystems and biogeochemical carbon and …

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Quantum Sensing For Low-Light Imaging, Savannah Cuozzo Jan 2022

Quantum Sensing For Low-Light Imaging, Savannah Cuozzo

Dissertations, Theses, and Masters Projects

In high-precision optical measurements, noise due to quantum fluctuations in the amplitude and phase of the probing field becomes the limiting factor in detection sensitivity. While this quantum noise is fundamental and not a result of detection, it is possible to engineer a quantum state that has reduced noise in either amplitude or phase (at the cost of increasing noise in the other) called a quadrature-squeezed state. In this dissertation, we study the use of quadrature-squeezed vacuum states for low-light imaging and develop a quantum detection method to measure the spatial dependence of the quantum noise using a camera instead …

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Deep Learning From Space: Methods & Applications In High-Resolution Satellite Imagery Analysis, Ethan Brewer Jan 2022

Deep Learning From Space: Methods & Applications In High-Resolution Satellite Imagery Analysis, Ethan Brewer

Dissertations, Theses, and Masters Projects

Satellite imagery analysis using deep learning methods, specifically convolutional neural networks (CNNs), has grown in popularity since 2012, with uses extending into the estimation of population, wealth, poverty, conflict, migration, education, and infrastructure, among other applications. This dissertation contributes to this body of literature in three parts. First, I explore the use of deep learning to overcome the sparsity, or complete lack, of accurate information regarding existing road infrastructure across much of the world. Using a novel labeled dataset generated by a custom-coded Android application, I show that a transfer learning approach can estimate road quality based on high-resolution satellite …

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Investigation Of Stripes, Spin Density Waves And Superconductivity In The Ground State Of The Two-Dimensional Hubbard Model, Hao Xu Jan 2022

Investigation Of Stripes, Spin Density Waves And Superconductivity In The Ground State Of The Two-Dimensional Hubbard Model, Hao Xu

Dissertations, Theses, and Masters Projects

The Hubbard model is a "paradigmatic" model in the realm of condensed matter physics. Recently a work with various state-or-art methods established the ground state stripe order near 1/8 doping and strong on-site interaction. Therefore, in this thesis, we determine the spin and charge order of ground state of 2D doped Hubbard model in its simplest form (with only on site repulsion and nearest-neighbor hoping) with various doping and small to medium interaction. At half-filling, the ground state is known to be an antiferromagnetic Mott insulator. Doping Mott insulators is believed to be relevant to the superconductivity observed in cuprates. …

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Enabling Practical Evaluation Of Privacy Of Commodity-Iot, Sunil Manandhar Jan 2022

Enabling Practical Evaluation Of Privacy Of Commodity-Iot, Sunil Manandhar

Dissertations, Theses, and Masters Projects

There has been a massive shift towards the use of IoT products in recent years. While companies have come a long way in making these devices and services easily accessible to the consumers, very little is known about the privacy issues pertaining to these devices. In this dissertation, we focus on evaluating privacy pertaining to commodity-IoT devices by studying device usage behavior of consumers and privacy disclosure practices of IoT vendors. Our analyses consider deep intricacies tied to commodity-IoT domain, revealing insightful findings that help with building automated tools for a large scale analysis. We first present the design and …

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Importance Of Muddy Bed Aggregate Processes In Cohesive Sediment Dynamics Associated With Sediment Management Projects, David Perkey Jan 2022

Importance Of Muddy Bed Aggregate Processes In Cohesive Sediment Dynamics Associated With Sediment Management Projects, David Perkey

Dissertations, Theses, and Masters Projects

The erosion and transport processes of fine sediment is largely impacted by the aggregation state. Understanding fine sediment transport processes is a key component to managing the nation’s navigation channels, ports, and reservoirs. To improve its ability to apply management strategies related to fine sediments, the USACE has undertaken research that focusses on the aggregation state of fine sediment. Of particular interest is the ability to expand the use of fine-grained sediment in projects that seek to beneficially use dredge material. In this study, a newly developed camera system was used to evaluate the aggregation state of eroded sediment from …

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Physiological Condition And Recruitment Of Mytilus Edulis And Donax Variabilis On Virginia Barrier Islands, Taylor Walker Jan 2022

Physiological Condition And Recruitment Of Mytilus Edulis And Donax Variabilis On Virginia Barrier Islands, Taylor Walker

Dissertations, Theses, and Masters Projects

Climate change has caused gradual changes within marine environments within the last couple decades and is expected to continue to impact these ecosystems. Changes to these ecosystems are anticipated to emerge as adverse effects reach the lowest and highest levels within trophic food webs. For example, these environmental changes may change the abundance and distribution of species within their current geographic range. In extreme cases, climate change has already resulted in range shifts of terrestrial and marine species. A need for bioindicator species has emerged, so that they may be used to indicate when climate change may impact marine communities …

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Communication And Computation Efficient Deep Learning, Zeyi Tao Jan 2022

Communication And Computation Efficient Deep Learning, Zeyi Tao

Dissertations, Theses, and Masters Projects

Recent advances in Artificial Intelligence (AI) are characterized by ever-increasing datasets and rapid growth of model complexity. Many modern machine learning models, especially deep neural networks (DNNs), cannot be efficiently carried out by a single machine. Hence, distributed optimization and inference have been widely adopted to tackle large-scale machine learning problems. Meanwhile, quantum computers that process computational tasks exponentially faster than classical machines offer an alternative solution for resource-intensive deep learning. However, there are two obstacles that hinder us from building large-scale DNNs on the distributed systems and quantum computers. First, when distributed systems scale to many nodes, the training …

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Environmental Justice In The Elizabeth River Watershed: Exploring The Utility Of Environmental Justice Screening Tools, Julianna M. Ramirez Jan 2022

Environmental Justice In The Elizabeth River Watershed: Exploring The Utility Of Environmental Justice Screening Tools, Julianna M. Ramirez

Dissertations, Theses, and Masters Projects

The Environmental Justice (EJ) movement has long highlighted the disproportionate exposure to environmental hazards experienced by Black, Indigenous, People of Color (BIPOC) and low-income communities across the country. Environmental practitioners have recently focused on utilizing EJ screening tools, which combine environmental and social data to visualize vulnerable communities, to begin to address environmental injustice rampant in BIPOC and low-income communities. This project explores EJ theoretical frameworks and the historical context of social oppression and environmental pollution in the Elizabeth River watershed (ERW) of Virginia to: 1) understand the social, political, and economic context behind environmental injustice; and 2) generate goals …

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Sediment Survey: Yr060822, Station 3916, Clay Bank, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs Jan 2022

Sediment Survey: Yr060822, Station 3916, Clay Bank, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs

Data

This dataset consists of sediment properties including grain size distribution, percent moisture, percent organic matter, sediment bed erodibility, as well as (in most cases) x-ray images of the sediment structure. Most samples were taken in support of an Acoustic Doppler Velocimeter (ADV) tripod deployed in nearby location.

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Sediment Survey: Yr070523, Station 3939, Clay Bank, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs Jan 2022

Sediment Survey: Yr070523, Station 3939, Clay Bank, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs

Data

This dataset consists of sediment properties including grain size distribution, percent moisture, percent organic matter, sediment bed erodibility, as well as (in most cases) x-ray images of the sediment structure. Most samples were taken in support of an Acoustic Doppler Velocimeter (ADV) tripod deployed in nearby location.

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Sediment Survey: Yr060925, Station 3918, Gloucester Point, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs Jan 2022

Sediment Survey: Yr060925, Station 3918, Gloucester Point, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs

Data

This dataset consists of sediment properties including grain size distribution, percent moisture, percent organic matter, sediment bed erodibility, as well as (in most cases) x-ray images of the sediment structure. Most samples were taken in support of an Acoustic Doppler Velocimeter (ADV) tripod deployed in nearby location.

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