Entire DC Network | Open Access Articles | Digital Commons Network™

Synthetic Heart Sound Dataset, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Datasets

The repository contains synthetic heart sound recordings. The publication related to this dataset is "Exploring the impact of noise and degradations on heart sound classification models", Biomedical Signal Processing and Control journal.

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Hdd Dataset: Optimising Retrofitted Insulation For Irish Residential Building Walls, Rakshit D. Muddu, Aimee Byrne, Anthony James Robinson

Datasets

In this study, a thermal-economic analysis was conducted to determine the optimum insulation thickness of retrofitted insulation walls in different regions in Ireland. This was based on the Heating Degree Day method (HDD). This dataset contains optimum insulation thickness, payback period, cost savings and carbon emission for all 25 counties in the Republic of Ireland

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Dataset For Gendered Language, Shweta Soundararajan

Datasets

Gendered language is the use of words that denote an individual’s gender. This can be explicit where the gender is evident in the actual word used, e.g. mother, she, man, but it can also be implicit where social roles or behaviours can signal an individual’s gender - for example, expectations that women display communal traits (e.g., affectionate, caring, gentle) and men display agentic traits (e.g., assertive, competitive, decisive). The use of gendered language in NLP systems can perpetuate gender stereotypes and bias. This paper proposes an approach to generating gendered language datasets using ChatGPT which will provide data for data-driven …

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Towards Quantum Computing For Audio And Music Expression, Paulo Vitor Itaboraí

School of Law, Humanities and Social Sciences Theses

This research delves into the initial approaches of the use of emerging Quantum Computing technologies in music practice, anchored on preliminary studies on Quantum Representation of Audio (QRA). The approach is interdisciplinary, and attempts to explore the object - QRA - both from a technical scope and an artistic scope. A systematic review unifies diverse QRA techniques, spotlighting the need to bring accessibility to these techniques and terminologies (dense in theoretical physics formalism) for a computer music and/or artistic audiences, in a didactic manner. In this review, some additional techniques initially only discussed for representing images are translated into audio. …

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Implementing The Fast Full-Wave Electromagnetic Forward Solver Using The Deep Convolutional Encoder-Decoder Architecture, He Ming Yao, Lijun Jiang, Michael Ng

Electrical and Computer Engineering Faculty Research & Creative Works

In this communication, a novel deep learning (DL)-based solver is proposed for the electromagnetic forward (EMF) process. It is based on the complex-valued deep convolutional neural networks (DConvNets) comprising an encoder network and a corresponding decoder network with pixel-wise regression layer. The encoder network takes the incident EM wave and the contrast (permittivity) distribution of the object as the input. It channels the processed data into the corresponding decoder network to predict the total EM field due to the scatter of the input incident EM wave. The training of the proposed DConvNets is done using the simple synthetic dataset. Due …

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Application Of A Variable Path Length Repetitive Process Control For Direct Energy Deposition Of Thin-Walled Structures, Elias B. Snider, Douglas A. Bristow

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Direct Energy Deposition (DED) Additive Manufacturing is Well Suited to Fabricating Large Thin-Walled Metal Structures Such as Rocket Nozzles but Suffers from Layer-To-Layer Defect Propagation. Propagating Defects May Exhibit as Slumping or a Ripple in Bead Geometry. Recent Works Have Used Repetitive Process Control (RPC) Methods for Additive Manufacturing to Stabilize the Layer-Wise Defect Propagation, But These Methods Require Repetition of the Same Path. However, Typical Thin-Wall DED Applications, Sometimes Referred to as Vase Structures, Have Changing Paths with Each Layer Such as Expanding or Contracting Diameters and Changing Profiles. This Paper Presents an Extension to Optimal RPC that Uses …

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Quantifying Stranded Assets Of The Coal-Fired Power In China Under The Paris Agreement Target, Weirong Zhang, Yiou Zhou, Zhen Gong, Junjie Kang, Changhong Zhao, Zhixu Meng, Jian Zhang, Tao Zhang, Jiahai Yuan

Research Collection School Of Computing and Information Systems

Coal-fired power plays a critical role in China's compliance with the Paris Agreement. This research quantifies China's stranded coal assets under different coal capacity expansion scenarios with an integrated approach and high-precision coal-fired power database. From a top-down perspective, firstly, the pathway of China's coal-fired power capacity consistent with the global 2 degrees C scenario is outlined and then those stranded coal-fired power plants are identified with a bottom-up perspective. Stranded value is estimated based upon a cash flow algorithm. Results show that if coal capacity stabilizes during 2020-2030, China will only incur a sizeable yet manageable stranded asset loss …

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Just-In-Time Obsolete Comment Detection And Update, Zhongxin Liu, Xin Xia, David Lo, Meng Yan, Shangping Li

Research Collection School Of Computing and Information Systems

Comments are valuable resources for the development, comprehension and maintenance of software. However, while changing code, developers sometimes neglect the evolution of the corresponding comments, resulting in obsolete comments. Such obsolete comments can mislead developers and introduce bugs in the future, and are therefore detrimental. We notice that by detecting and updating obsolete comments in time with code changes, obsolete comments can be effectively reduced and even avoided. We refer to this task as Just-In-Time (JIT) Obsolete Comment Detection and Update. In this work, we propose a two-stage framework named CUP2 (Two-stage Comment UPdater) to automate this task. CUP2 consists …

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Instantaneous Frequency Estimation Of Fm Signals Under Gaussian And Symmetric Alpha-Stable Noise: Deep Learning Versus Time-Frequency Analysis, Huda Saleem Razzaq, Zahir M. Hussain

Research outputs 2022 to 2026

Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation …

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Reading Group As Method For Feminist Environmental Humanities, James Gardiner, Hayley Singer, Jennifer Hamilton, Astrida Neimanis, Mindy Blaise

Research outputs 2022 to 2026

This article argues that reading groups are a collective field building and research method in Feminist Environmental Humanities, an interdisciplinary scholarly area at the intersections of feminist social justice and environmental concerns. We begin by historicising three Australian Feminist Environmental reading groups (COMPOSTING Feminisms, Eco Feminist Fridays, The Ediths) within a longer feminist tradition, then demonstrate how they respond to declining research funding in the neoliberal university and accelerating ecological crisis. Drawing on survey data, we first thematically code and analyse the results to categorise the groups’ functions and impacts. Departing from more traditional data analysis, we then develop a …

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Predicting Dissolution Kinetics Of Tricalcium Silicate Using Deep Learning And Analytical Models, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

The dissolution kinetics of Portland cement is a critical factor in controlling the hydration reaction and improving the performance of concrete. Tricalcium silicate (C₃S), the primary phase in Portland cement, is known to have complex dissolution mechanisms that involve multiple reactions and changes to particle surfaces. As a result, current analytical models are unable to accurately predict the dissolution kinetics of C₃S in various solvents when it is undersaturated with respect to the solvent. This paper employs the deep forest (DF) model to predict the dissolution rate of C₃S in the undersaturated solvent. The …

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Synthesis, Densification, And Cation Inversion In High Entropy (Co,Cu,Mg,Ni,Zn)Al2o4 Spinel, Cole A. Corlett, Nina Obradovic, Jeremy Lee Watts, Eric W. Bohannan, William Fahrenholtz

Materials Science and Engineering Faculty Research & Creative Works

The synthesis, densification behavior, and crystallographic site occupancy were investigated for four different spinel-based ceramics, including a high-entropy spinel (Co_0.2Cu_0.2Mg_0.2Ni_0.2 Zn_0.2)Al₂O₄. Each composition was reacted to form a single phase, but analysis of X-ray diffraction patterns revealed differences in cation site occupancy with the high-entropy spinel being nearly fully normal. Densification behavior was investigated and showed that fully dense ceramics could be produced by hot pressing at temperatures as low as 1375°C for all compositions. Vickers' hardness values were at least 10 GPa for all compositions. The …

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Real-Time Air Gap And Thickness Measurement Of Continuous Caster Mold Flux By Extrinsic Fabry-Perot Interferometer, Abhishek Prakash Hungund, Hanok Tekle, Bohong Zhang, Ronald J. O'Malley, Jeffrey D. Smith, Rex E. Gerald, Jie Huang

Materials Science and Engineering Faculty Research & Creative Works

Mold Flux plays a critical role in continuous casting of steel. Along with many other functions, the mold flux in the gap between the solidifying steel shell and the mold serves as a medium for controlling heat transfer and as a barrier to prevent shell sticking to the mold. This manuscript introduces a novel method of monitoring the structural features of a mold flux film in real-time in a simulated mold gap. A 3-part stainless-steel mold was designed with a 2 mm, 4 mm and, 6 mm step profile to contain mold flux films of varying thickness. An Extrinsic Fabry-Perot …

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Mapping For Impact: Actionable Spatial Literacy Through Counter-Mapping, Rachel Hansen

Dissertations and Theses @ UNI

Counter-mapping is a technique that young people can use to take informed action on community issues through the mapping process. This mixed-methods study examined how ninth-graders in a large urban district in Iowa developed spatial thinking skills while engaging in counter-mapping in their community and the extent to which they used those maps to take informed action. A nine-week learning progression for counter-mapping scaffolded progress variables across three different spatial thinking standards and one inquiry standard. Findings indicated that students improved throughout the learning progression, but some needed teacher support to conduct spatial inquiries. Chances of reaching the upper anchor …

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Managing Reserve Deliverability Risk Of Integrated Electricity-Heat Systems In Day-Ahead Market: A Distributionally Robust Joint Chance Constrained Approach, Yang Chen, Jianxue Wang, Siyuan Wang, Rui Bo, Chenjia Gu, Qingtao Li

Electrical and Computer Engineering Faculty Research & Creative Works

The integrated electricity and heat system (IEHS) is an emerging demand-side flexible resource for power systems. IEHS operators participating in electricity markets considering their capabilities in reserve provision will face the reserve deliverability risk due to the energy-limited storage nature of heat systems. To address this challenge and increase profitability, a distributionally robust joint chance-constrained mechanism with enhanced quantifications is adopted for the heating system and reserve deployment uncertainties. Detailed pipeline storage representation for thermal networks and integrated demand response are incorporated into this strategic participation model. A two-stage distributionally robust joint chance constrained program is then incorporated to effectively …

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Lifelong Learning Control Of Nonlinear Systems With Constraints Using Multilayer Neural Networks With Application To Mobile Robot Tracking, Irfan Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Presents a Novel Lifelong Multilayer Neural Network (MNN) Tracking Approach for an Uncertain Nonlinear Continuous-Time Strict Feedback System that is Subject to Time-Varying State Constraints. the Proposed Method Uses a Time-Varying Barrier Function to Accommodate the Constraints Leading to the Development of an Efficient Control Scheme. the Unknown Dynamics Are Approximated using a MNN, with Weights Tuned using a Singular Value Decomposition (SVD)-Based Technique. an Online Lifelong Learning (LL) based Elastic Weight Consolidation (EWC) Scheme is Also Incorporated to Alleviate the Issue of Catastrophic Forgetting. the Stability of the overall Closed-Loop System is Analyzed using Lyapunov Analysis. the …

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An Explainable Deep Learning Model For Prediction Of Severity Of Alzheimer's Disease, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Chemistry Faculty Research & Creative Works

Deep Convolutional Neural Networks (CNNs) have become the go-To method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer's disease (AD). AD diagnosis and prognosis heavily rely …

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Advances In Bioelectrode Design For Developing Electrochemical Biosensors, Nabajyoti Kalita, Sudarshan Gogoi, Shelley D. Minteer, Pranab Goswami

Chemistry Faculty Research & Creative Works

The critical performance factors such as selectivity, sensitivity, operational and storage stability, and response time of electrochemical biosensors are governed mainly by the function of their key component, the bioelectrode. Suitable design and fabrication strategies of the bioelectrode interface are essential for realizing the requisite performance of the biosensors for their practical utility. A multifaceted attempt to achieve this goal is visible from the vast literature exploring effective strategies for preparing, immobilizing, and stabilizing biorecognition elements on the electrode surface and efficient transduction of biochemical signals into electrical ones (i.e., current, voltage, and impedance) through the bioelectrode interface with the …

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Delivering Broadband Light Deep Into Diffusive Media, Rohin Mcintosh, Nicholas Bender, Alexey Yamilov, Arthur Goetschy, Chia Wei Hsu, Hasan Yilmaz, Hui Cao

Physics Faculty Research & Creative Works

Waves propagate diffusively through disordered media, such as biological tissue, clouds, and paint, due to random scattering. Recent advances in optical wavefront shaping techniques have enabled controlling coherent light propagation in multiple-scattering samples. We overcome wave diffusion to deliver optical energy into a target region of arbitrary size and shape anywhere inside a strong-scattering system. This is particularly important for applications such as photoacoustic microscopy and optogenetics, where light needs to be deposited deep into biological tissue. For monochromatic light, we previously introduced the deposition matrix (DM) Z(ω), which maps its input wavefront to the field distribution in the target …

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Multi-Layered Energy Management Framework For Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, And Forecast Uncertainties, Waqas Ur Rehman, Jonathan W. Kimball, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy …

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Environmentally-Aware And Energy-Efficient Multi-Drone Coordination And Networking For Disaster Response, Chengyi Qu, Francesco Betti Sorbelli, Rounak Singh, Prasad Calyam, Sajal K. Das

Computer Science Faculty Research & Creative Works

In a Disaster Response Management (DRM) Scenario, Communication and Coordination Are Limited, and Absence of Related Infrastructure Hinders Situational Awareness. Unmanned Aerial Vehicles (UAVs) or Drones Provide New Capabilities for DRM to Address These Barriers. However, There is a Dearth of Works that Address Multiple Heterogeneous Drones Collaboratively Working Together to Form a Flying Ad-Hoc Network (FANET) with Air-To-Air and Air-To-Ground Links that Are Impacted By: (I) Environmental Obstacles, (Ii) Wind, and (Iii) Limited Battery Capacities. in This Paper, We Present a Novel Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking Scheme that Features a Reinforcement Learning (RL) based Location Prediction …

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Programmable Software-Defined Testbed For Visible Light Uav Networks: Architecture Design And Implementation, Yue Zhang, Nan Cen

Computer Science Faculty Research & Creative Works

As of Today, There Has Been Increasing Research on Designing Optimization Algorithms and Intelligent Network Control Methods for Visible Light Unmanned Aerial Vehicles (UAV) Networks to Provide Pervasive and Broadband Connections. for Those Theoretical Analysis based Algorithms, there is an Urgent Need to Have a Visible Light UAV Network Platform that Can Help Evaluate the Proposed Algorithms in Real-World Scenarios. However, to the Best of Our Knowledge, there is Currently No Dedicated High Data Rate and Flexible Visible Light UAV Networking Prototype. to Bridge This Gap, in This Paper, We First Design a Novel Programmable Software-Defined Architecture for Visible Light …

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Deep Meta Q-Learning Based Multi-Task Offloading In Edge-Cloud Systems, Nelson Sharma, Aswini Ghosh, Rajiv Misra, Sajal K. Das

Computer Science Faculty Research & Creative Works

Resource-Constrained Edge Devices Can Not Efficiently Handle the Explosive Growth of Mobile Data and the Increasing Computational Demand of Modern-Day User Applications. Task Offloading Allows the Migration of Complex Tasks from User Devices to the Remote Edge-Cloud Servers Thereby Reducing their Computational Burden and Energy Consumption While Also Improving the Efficiency of Task Processing. However, Obtaining the Optimal Offloading Strategy in a Multi-Task Offloading Decision-Making Process is an NP-Hard Problem. Existing Deep Learning Techniques with Slow Learning Rates and Weak Adaptability Are Not Suitable for Dynamic Multi-User Scenarios. in This Article, We Propose a Novel Deep Meta-Reinforcement Learning-Based Approach to …

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Sigmoid Activation-Based Long Short-Term Memory For Time Series Data Classification, Sajal Das

Computer Science Faculty Research & Creative Works

With the enhanced usage of Artificial Intelligence (AI) driven applications, the researchers often face challenges in improving the accuracy of the data classification models, while trading off the complexity. In this paper, we address the classification of time series data using the Long Short-Term Memory (LSTM) network while focusing on the activation functions. While the existing activation functions such as sigmoid and tanh are used as LSTM internal activations, the customizability of these activations stays limited. This motivates us to propose a new family of activation functions, called log-sigmoid, inside the LSTM cell for time series data classification, and analyze …

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Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets, Sourav Kanti Addya, Anurag Satpathy, Bishakh Chandra Ghosh, Sandip Chakraborty, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications' power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the …

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Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making, or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack …

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Preserving Privacy In Image Database Through Bit-Planes Obfuscation, Vishesh K. Tanwar, Ashish Gupta, Sanjay Kumar Madria, Sajal K. Das

Computer Science Faculty Research & Creative Works

The recent surge in computer vision applications has caused visual privacy concerns to people who are either users or exposed to an underlying surveillance system. To preserve their privacy, image obfuscation lays out a strong road through which the usability of images can also be maintained without revealing any visual private information. However, prior solutions are susceptible to reconstruction attacks or produce non-trainable images even by leveraging the obfuscation ways. This paper proposes a novel bit-planes-based image obfuscation scheme, called Bimof, to protect the visual privacy of the user in the images that are input into a recognition-based system. By …

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Welcome From General Chairs, Sajal K. Das, Wen Zhan Song

Computer Science Faculty Research & Creative Works

No abstract provided.

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Urban Air Mobility: Vision, Challenges And Opportunities, Debjyoti Sengupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Urban Air Mobility (UAM) involving piloted or autonomous aerial vehicles, is envisioned as emerging disruptive technology for next-generation transportation addressing mobility challenges in congested cities. This paradigm may include aircrafts ranging from small unmanned aerial vehicles (UAVs) or drones, to aircrafts with passenger carrying capacity, such as personal air vehicles (PAVs). This paper highlights the UAM vision and brings out the underlying fundamental research challenges and opportunities from computing, networking, and service perspectives for sustainable design and implementation of this promising technology providing an innovative infrastructure for urban mobility. Important research questions include, but are not limited to, real-Time autonomous …

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Dispatching Point Selection For A Drone-Based Delivery System Operating In A Mixed Euclidean–Manhattan Grid, Francesco Betti Sorbelli, Federico Corò, Sajal K. Das, Cristina M. Pinotti, Anil Shende

Computer Science Faculty Research & Creative Works

In this paper, we present a drone-based delivery system that assumes to deal with a mixed-area, i.e., two areas, one rural and one urban, placed side-by-side. In the mixed-areas, called EM-grids, the distances are measured with two different metrics, and the shortest path between two destinations concatenates the Euclidean and Manhattan metrics. Due to payload constraints, the drone serves a single customer at a time returning back to the dispatching point (DP) after each delivery to load a new parcel for the next customer. In this paper, we present the 1 -Median Euclidean–Manhattan grid Problem (MEMP) for EM-grids, whose goal …

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Full-Text Articles in Entire DC Network

Synthetic Heart Sound Dataset, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Datasets

Hdd Dataset: Optimising Retrofitted Insulation For Irish Residential Building Walls, Rakshit D. Muddu, Aimee Byrne, Anthony James Robinson

Datasets

Dataset For Gendered Language, Shweta Soundararajan

Datasets

Towards Quantum Computing For Audio And Music Expression, Paulo Vitor Itaboraí

School of Law, Humanities and Social Sciences Theses

Implementing The Fast Full-Wave Electromagnetic Forward Solver Using The Deep Convolutional Encoder-Decoder Architecture, He Ming Yao, Lijun Jiang, Michael Ng

Electrical and Computer Engineering Faculty Research & Creative Works

Application Of A Variable Path Length Repetitive Process Control For Direct Energy Deposition Of Thin-Walled Structures, Elias B. Snider, Douglas A. Bristow

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Quantifying Stranded Assets Of The Coal-Fired Power In China Under The Paris Agreement Target, Weirong Zhang, Yiou Zhou, Zhen Gong, Junjie Kang, Changhong Zhao, Zhixu Meng, Jian Zhang, Tao Zhang, Jiahai Yuan

Research Collection School Of Computing and Information Systems

Just-In-Time Obsolete Comment Detection And Update, Zhongxin Liu, Xin Xia, David Lo, Meng Yan, Shangping Li

Research Collection School Of Computing and Information Systems

Instantaneous Frequency Estimation Of Fm Signals Under Gaussian And Symmetric Alpha-Stable Noise: Deep Learning Versus Time-Frequency Analysis, Huda Saleem Razzaq, Zahir M. Hussain

Research outputs 2022 to 2026

Reading Group As Method For Feminist Environmental Humanities, James Gardiner, Hayley Singer, Jennifer Hamilton, Astrida Neimanis, Mindy Blaise

Research outputs 2022 to 2026

Predicting Dissolution Kinetics Of Tricalcium Silicate Using Deep Learning And Analytical Models, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

Synthesis, Densification, And Cation Inversion In High Entropy (Co,Cu,Mg,Ni,Zn)Al2o4 Spinel, Cole A. Corlett, Nina Obradovic, Jeremy Lee Watts, Eric W. Bohannan, William Fahrenholtz

Materials Science and Engineering Faculty Research & Creative Works

Real-Time Air Gap And Thickness Measurement Of Continuous Caster Mold Flux By Extrinsic Fabry-Perot Interferometer, Abhishek Prakash Hungund, Hanok Tekle, Bohong Zhang, Ronald J. O'Malley, Jeffrey D. Smith, Rex E. Gerald, Jie Huang

Materials Science and Engineering Faculty Research & Creative Works

Mapping For Impact: Actionable Spatial Literacy Through Counter-Mapping, Rachel Hansen

Dissertations and Theses @ UNI

Managing Reserve Deliverability Risk Of Integrated Electricity-Heat Systems In Day-Ahead Market: A Distributionally Robust Joint Chance Constrained Approach, Yang Chen, Jianxue Wang, Siyuan Wang, Rui Bo, Chenjia Gu, Qingtao Li

Electrical and Computer Engineering Faculty Research & Creative Works

Lifelong Learning Control Of Nonlinear Systems With Constraints Using Multilayer Neural Networks With Application To Mobile Robot Tracking, Irfan Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

An Explainable Deep Learning Model For Prediction Of Severity Of Alzheimer's Disease, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Chemistry Faculty Research & Creative Works

Advances In Bioelectrode Design For Developing Electrochemical Biosensors, Nabajyoti Kalita, Sudarshan Gogoi, Shelley D. Minteer, Pranab Goswami

Chemistry Faculty Research & Creative Works

Delivering Broadband Light Deep Into Diffusive Media, Rohin Mcintosh, Nicholas Bender, Alexey Yamilov, Arthur Goetschy, Chia Wei Hsu, Hasan Yilmaz, Hui Cao

Physics Faculty Research & Creative Works

Multi-Layered Energy Management Framework For Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, And Forecast Uncertainties, Waqas Ur Rehman, Jonathan W. Kimball, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Environmentally-Aware And Energy-Efficient Multi-Drone Coordination And Networking For Disaster Response, Chengyi Qu, Francesco Betti Sorbelli, Rounak Singh, Prasad Calyam, Sajal K. Das

Computer Science Faculty Research & Creative Works

Programmable Software-Defined Testbed For Visible Light Uav Networks: Architecture Design And Implementation, Yue Zhang, Nan Cen

Computer Science Faculty Research & Creative Works

Deep Meta Q-Learning Based Multi-Task Offloading In Edge-Cloud Systems, Nelson Sharma, Aswini Ghosh, Rajiv Misra, Sajal K. Das

Computer Science Faculty Research & Creative Works

Sigmoid Activation-Based Long Short-Term Memory For Time Series Data Classification, Sajal Das

Computer Science Faculty Research & Creative Works

Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets, Sourav Kanti Addya, Anurag Satpathy, Bishakh Chandra Ghosh, Sandip Chakraborty, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Preserving Privacy In Image Database Through Bit-Planes Obfuscation, Vishesh K. Tanwar, Ashish Gupta, Sanjay Kumar Madria, Sajal K. Das

Computer Science Faculty Research & Creative Works

Welcome From General Chairs, Sajal K. Das, Wen Zhan Song

Computer Science Faculty Research & Creative Works

Urban Air Mobility: Vision, Challenges And Opportunities, Debjyoti Sengupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Dispatching Point Selection For A Drone-Based Delivery System Operating In A Mixed Euclidean–Manhattan Grid, Francesco Betti Sorbelli, Federico Corò, Sajal K. Das, Cristina M. Pinotti, Anil Shende

Computer Science Faculty Research & Creative Works