Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (87)
- Robotics (74)
- Computer Sciences (61)
- Digital Communications and Networking (52)
- Electrical and Computer Engineering (32)
-
- Systems and Communications (29)
- Environmental Sciences (23)
- Sustainability (21)
- Earth Sciences (17)
- Life Sciences (17)
- Soil Science (17)
- Water Resource Management (16)
- Artificial Intelligence and Robotics (14)
- Genetics and Genomics (12)
- Social and Behavioral Sciences (10)
- Signal Processing (8)
- Computer and Systems Architecture (7)
- Other Environmental Sciences (7)
- Hardware Systems (6)
- Environmental Monitoring (5)
- Medicine and Health Sciences (5)
- Agriculture (4)
- Civil and Environmental Engineering (4)
- Library and Information Science (4)
- OS and Networks (4)
- Operations Research, Systems Engineering and Industrial Engineering (4)
- Systems Engineering (4)
- Aerospace Engineering (3)
- Institution
- Keyword
-
- Computer Science and Engineering (69)
- Internet (26)
- Software agents (25)
- Robotics (15)
- Computer Science and Engineering, Biology (13)
-
- Internet of Underground Things (12)
- Underground Communications (12)
- Wireless Underground Channel (12)
- Genomics (11)
- Multi-agent systems (11)
- Navigation (11)
- Cognitive robotics (9)
- Genome (9)
- Sustainability (9)
- Agriculture (8)
- Broadband antennas (8)
- Dielectric (8)
- Digital Agriculture (8)
- Dipole antennas (8)
- Information resources (8)
- Internet of Things (8)
- Moisture (8)
- Permittivity (8)
- Soil (8)
- Underground communication (8)
- Computer vision (6)
- Journal Publications (6)
- Sensory fusion (6)
- Validation (6)
- Behavior-Based (5)
Articles 1 - 30 of 321
Full-Text Articles in Computer Engineering
When Disasters Trigger Cyber Vulnerabilities: Mapping Physical-Digital Interdependencies In Critical Infrastructure Systems, Dikshya Panta, Sicheng Wang, Aditya Sapkota, Prakash Ranganathan
When Disasters Trigger Cyber Vulnerabilities: Mapping Physical-Digital Interdependencies In Critical Infrastructure Systems, Dikshya Panta, Sicheng Wang, Aditya Sapkota, Prakash Ranganathan
Faculty Publications
Critical infrastructure (CI) systems such as power, water, communications, and emergency services are increasingly exposed to compound risks in which natural disasters and cyber incidents interact and amplify one another. Traditional risk assessments often isolate physical and digital threats, overlooking the cascading dependencies that emerge when operational stress, emergency reconfiguration, and adversarial exploitation coincide. This study conducts a 2019–2025 scoping review and introduces a Geographic Information System (GIS) driven six-stage disaster cyber compounding framework that characterizes, maps, and operationalizes compound risk across interdependent CI sectors. The framework integrates a common operating picture, analytic situational understanding, exposure mapping, threat-fingerprint encoding, detection …
Neurosymbolic Knowledge-Grounded Planning And Reasoning In Ai Systems, Amit Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty
Neurosymbolic Knowledge-Grounded Planning And Reasoning In Ai Systems, Amit Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty
Faculty Publications
To build AI systems capable of decision-support assistance, such as AI-assisted healthcare, it is essential to develop user-centric decision-making processes that are robust, interpretable, and capable of effectively processing and acting on natural language interactions. Instruction-based prompting of large language models has demonstrated considerable success in supporting humans with information assistance tasks, including creative writing and content generation. However, recent studies reveal that language models exhibit limitations in performing complex reasoning and planning tasks, such as constructing compositional or hierarchical plans involving multiple reasoning steps. To address these challenges, we propose a neurosymbolic framework that integrates large language models with …
Msbzip55 Regulates Salinity Tolerance By Modulating Melatonin Biosynthesis In Alfalfa, Tingting Wang, Jiaqi Yang, Jiamin Cao, Qi Zhang, Huayue Liu, Peng Li, Yizhi Huang, Wenwu Qian, Xiaojing Bi, Hui Wang, Yunwei Zhang
Msbzip55 Regulates Salinity Tolerance By Modulating Melatonin Biosynthesis In Alfalfa, Tingting Wang, Jiaqi Yang, Jiamin Cao, Qi Zhang, Huayue Liu, Peng Li, Yizhi Huang, Wenwu Qian, Xiaojing Bi, Hui Wang, Yunwei Zhang
Faculty Publications
No abstract provided.
Lab: Developing Explainable Multimodal Ai Models With Hands-On Lab On The Life-Cycle Of Rare Event Prediction In Manufacturing, Chathurangi Shyalika, Ruwan Wickramarachchi, Revathy Venkataramanan, Dhaval Patel, Amit Sheth
Lab: Developing Explainable Multimodal Ai Models With Hands-On Lab On The Life-Cycle Of Rare Event Prediction In Manufacturing, Chathurangi Shyalika, Ruwan Wickramarachchi, Revathy Venkataramanan, Dhaval Patel, Amit Sheth
Faculty Publications
In the age of Industry 4.0 and smart automation, unplanned downtime is costing industries over $50 billion annually. Even with preventive maintenance, industries like automotive lose more than $2 million per hour due to downtime caused by unexpected or "rare'' events. The extreme rarity of these events makes their detection and prediction a significant challenge for AI practitioners. Factors such as the lack of high-quality data, methodological gaps in the literature, and limited practical experience with multimodal data exacerbate the difficulty of rare event detection and prediction. This lab will provide hands-on experience to learn how to address these challenges …
Neuro-Symbolic Ai For Deep Analysis Of Social Media Big Data, Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie Shalin, Amit P. Sheth
Neuro-Symbolic Ai For Deep Analysis Of Social Media Big Data, Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie Shalin, Amit P. Sheth
Faculty Publications
This tutorial introduces a neuro-symbolic AI framework to analyze big data from social media platforms. Integrating human-curated knowledge through symbolic AI with the pattern recognition capabilities of neural networks enhances the adaptability and efficiency of traditional neural network approaches. Knowledge-guided zero-shot learning techniques enable swift adaption to new linguistic contexts and emerging events [6]. Participants will explore how to design, develop, and utilize these models in specific domains, such as public health surveillance, that require dynamic adaptation to new terminologies. This session The tutorial aims to equip attendees with practical skills and a deep understanding of how to apply neuro-symbolic …
Detecting Substance Use Disorder Using Social Media Data And Dark Web: Time And Knowledge Aware Study, Usha Lokala, Orchid Chetia Phukan, Triyasha Ghosh Dastidar, Francois Lamy, Raminta Daniulaityte, Amit Sheth
Detecting Substance Use Disorder Using Social Media Data And Dark Web: Time And Knowledge Aware Study, Usha Lokala, Orchid Chetia Phukan, Triyasha Ghosh Dastidar, Francois Lamy, Raminta Daniulaityte, Amit Sheth
Faculty Publications
Opioid and substance misuse is rampant in the United States today, with the phenomenon known as the "opioid crisis". The relationship between substance use and mental health has been extensively studied, with one possible relationship being: substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means. This study analyzes the substance use posts on social media with opioids being sold through crypto market listings. We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion for the social media …
Ads-B Classification Using Multivariate Long Short-Term Memory–Fully Convolutional Networks And Data Reduction Techniques, Sarah Bolton, Richard Dill, Michael R. Grimaila, Douglas Hodson
Ads-B Classification Using Multivariate Long Short-Term Memory–Fully Convolutional Networks And Data Reduction Techniques, Sarah Bolton, Richard Dill, Michael R. Grimaila, Douglas Hodson
Faculty Publications
Researchers typically increase training data to improve neural net predictive capabilities, but this method is infeasible when data or compute resources are limited. This paper extends previous research that used long short-term memory–fully convolutional networks to identify aircraft engine types from publicly available automatic dependent surveillance-broadcast (ADS-B) data. This research designs two experiments that vary the amount of training data samples and input features to determine the impact on the predictive power of the ADS-B classification model. The first experiment varies the number of training data observations from a limited feature set and results in 83.9% accuracy (within 10% of …
Drone Detection Using Yolov5, Burchan Aydin, Subroto Singha
Drone Detection Using Yolov5, Burchan Aydin, Subroto Singha
Faculty Publications
The rapidly increasing number of drones in the national airspace, including those for recreational and commercial applications, has raised concerns regarding misuse. Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. In addition, automated drone detection systems need to be trained with ample amounts of data to provide high accuracy. Real-time detection is also necessary, but this requires highly configured devices such as a graphical processing unit (GPU). …
To The Moon: Strategic Competition In The Cislunar Region, Shawn M. Willis
To The Moon: Strategic Competition In The Cislunar Region, Shawn M. Willis
Faculty Publications
China’s advancing space capabilities, particularly in the cislunar region, call for increased cislunar space domain awareness on the part of the United States. US military and civilian decisionmakers must take into account the full scope of China’s cislunar plans and capabilities as the military builds space strategies and future force designs. The United States must also increase near-term investments that support more robust cislunar space domain awareness.
Quantifying Dds-Cerberus Network Control Overhead, Andrew T. Park, Nathaniel R. Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Quantifying Dds-Cerberus Network Control Overhead, Andrew T. Park, Nathaniel R. Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Faculty Publications
Securing distributed device communication is critical because the private industry and the military depend on these resources. One area that adversaries target is the middleware, which is the medium that connects different systems. This paper evaluates a novel security layer, DDS-Cerberus (DDS-C), that protects in-transit data and improves communication efficiency on data-first distribution systems. This research contributes a distributed robotics operating system testbed and designs a multifactorial performance-based experiment to evaluate DDS-C efficiency and security by assessing total packet traffic generated in a robotics network. The performance experiment follows a 2:1 publisher to subscriber node ratio, varying the number of …
Securing Information On A Web Application System To Facilitate Online Blood Donation Booking, Hrishitva Patel
Securing Information On A Web Application System To Facilitate Online Blood Donation Booking, Hrishitva Patel
Faculty Publications
Blood donation has saved many lives in the past. According to statistics presented by the American Red Cross, a patient is in need of a blood transfusion every two seconds. There are many benefits that arise from blood donation to both the donor and the blood recipients. With blood donation, cancer patients, people involved in accidents, or those battling diseases that require blood donation have access to enough blood to sustain their survival. There is a need to digitize the blood donation booking to facilitate blood donation across the United States, and ensure patients in need of blood, receive their …
A Monte Carlo Framework For Incremental Improvement Of Simulation Fidelity, Damian M. Lyons, James Finocchiaro, Misha Novitzky, Chris Korpela
A Monte Carlo Framework For Incremental Improvement Of Simulation Fidelity, Damian M. Lyons, James Finocchiaro, Misha Novitzky, Chris Korpela
Faculty Publications
Robot software developed in simulation often does not be- have as expected when deployed because the simulation does not sufficiently represent reality - this is sometimes called the `reality gap' problem. We propose a novel algorithm to address the reality gap by injecting real-world experience into the simulation. It is assumed that the robot program (control policy) is developed using simulation, but subsequently deployed on a real system, and that the program includes a performance objective monitor procedure with scalar output. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate …
A Monte Carlo Framework For Incremental Improvement Of Simulation Fidelity, Damian Lyons, James Finocchiaro, Misha Novitsky, Chris Korpela
A Monte Carlo Framework For Incremental Improvement Of Simulation Fidelity, Damian Lyons, James Finocchiaro, Misha Novitsky, Chris Korpela
Faculty Publications
Robot software developed in simulation often does not be- have as expected when deployed because the simulation does not sufficiently represent reality - this is sometimes called the `reality gap' problem. We propose a novel algorithm to address the reality gap by injecting real-world experience into the simulation. It is assumed that the robot program (control policy) is developed using simulation, but subsequently deployed on a real system, and that the program includes a performance objective monitor procedure with scalar output. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate …
An Ontology For Cardiothoracic Surgical Education And Clinical Data Analytics, Maryam Panahiazar, Yorick Chern, Ramon Riojas, Omar S.Latif, Usha Lokala, Dexter Hadley, Amit Sheth, Ramin E.Beygui
An Ontology For Cardiothoracic Surgical Education And Clinical Data Analytics, Maryam Panahiazar, Yorick Chern, Ramon Riojas, Omar S.Latif, Usha Lokala, Dexter Hadley, Amit Sheth, Ramin E.Beygui
Faculty Publications
The development of an ontology facilitates the organization of the variety of concepts used to describe different terms in different resources. The proposed ontology will facilitate the study of cardiothoracic surgical education and data analytics in electronic medical records (EMR) with the standard vocabulary.
Identification Of Return-Oriented Programming Attacks Using Risc-V Instruction Trace Data, Daniel F. Koranek, Scott R. Graham, Brett J. Borghetti, Wayne C. Henry
Identification Of Return-Oriented Programming Attacks Using Risc-V Instruction Trace Data, Daniel F. Koranek, Scott R. Graham, Brett J. Borghetti, Wayne C. Henry
Faculty Publications
An increasing number of embedded systems include dedicated neural hardware. To benefit from this specialized hardware, deep learning techniques to discover malware on embedded systems are needed. This effort evaluated candidate machine learning detection techniques for distinguishing exploited from non-exploited RISC-V program behavior using execution traces. We first developed a dataset of execution traces containing Return Oriented Programming (ROP) exploitation on the RISC-V Instruction Set Architecture (ISA) and then developed several deep learning bidirectional Long Short-Term Memory (LSTM) models capable of distinguishing exploited traces from non-exploited traces, each using subsets of features from the execution traces. An objective of this …
Visual Homing For Robot Teams: Do You See What I See?, Damian Lyons, Noah Petzinger
Visual Homing For Robot Teams: Do You See What I See?, Damian Lyons, Noah Petzinger
Faculty Publications
Visual homing is a lightweight approach to visual navigation which does not require GPS. It is very attractive for robot platforms with a low computational capacity. However, a limitation is that the stored home location must be initially within the field of view of the robot. Motivated by the increasing ubiquity of camera information we propose to address this line-of-sight limitation by leveraging camera information from other robots and fixed cameras. To home to a location that is not initially within view, a robot must be able to identify a common visual landmark with another robot that can be used …
Visual Homing For Robot Teams: Do You See What I See?, Damian Lyons, Noah Petzinger
Visual Homing For Robot Teams: Do You See What I See?, Damian Lyons, Noah Petzinger
Faculty Publications
Visual homing is a lightweight approach to visual navigation which does not require GPS. It is very attractive for robot platforms with a low computational capacity. However, a limitation is that the stored home location must be initially within the field of view of the robot. Motivated by the increasing ubiquity of camera information we propose to address this line-of-sight limitation by leveraging camera information from other robots and fixed cameras. To home to a location that is not initially within view, a robot must be able to identify a common visual landmark with another robot that can be used …
Electron Mobility And Velocity In Ai 0.45 Ga 0.55 N-Channel Ultra-Wide Bandgap Hemts At High Temperatures For Rf Power Applications, Hansheng Ye, Mikhail Gaevski, Grigory Simin, Asif Khan, Patrick Fay
Electron Mobility And Velocity In Ai 0.45 Ga 0.55 N-Channel Ultra-Wide Bandgap Hemts At High Temperatures For Rf Power Applications, Hansheng Ye, Mikhail Gaevski, Grigory Simin, Asif Khan, Patrick Fay
Faculty Publications
Ultra-wide bandgap AlGaN has attracted recent attention as a promising channel material for next-generation high electron mobility transistors (HEMTs) for RF power due to its high critical field, excellent transport properties, and potential for operation in extreme environments. However, the effects of temperature on the transport properties are not fully understood. Here, Al0.62Ga0.38N/Al0.45Ga0.55N HEMTs have been fabricated and characterized up to 150 °C at DC and RF to evaluate the effect of temperature on electron mobility and carrier velocity. Measured results indicate that both mobility and carrier velocity exhibit modest dependence on …
Madfam: Microarchitectural Data Framework And Methodology, Tor J. Langehaug, Scott R. Graham, Christine M. Schubert Kabban, Brett J. Borghetti
Madfam: Microarchitectural Data Framework And Methodology, Tor J. Langehaug, Scott R. Graham, Christine M. Schubert Kabban, Brett J. Borghetti
Faculty Publications
In the aftermath of Spectre and Meltdown researchers have proposed a variety of attack detection solutions by applying machine learning to data collected from hardware performance monitoring units. Although many microarchitectural attack detection systems provide high-accuracy detection results, the behavior of the underlying data collection mechanisms is not well described or understood. This research introduces the MicroArchitectural Data Framework And Methodology (MADFAM) to prescribe a systematic approach to collecting and preserving the information available in sequences of microarchitectural data. The proposed framework focuses on hardware performance counters (HPCs) as the primary data source. HPC configuration is complex, which makes it …
Large Scale Dataset Of Real Space Electronic Charge Density Of Cubic Inorganic Materials From Density Functional Theory (Dft) Calculations, Fancy Qian Wang, Kamal Choudhary, Jianjun Hu, Ming Hu
Large Scale Dataset Of Real Space Electronic Charge Density Of Cubic Inorganic Materials From Density Functional Theory (Dft) Calculations, Fancy Qian Wang, Kamal Choudhary, Jianjun Hu, Ming Hu
Faculty Publications
Driven by the big data science, material informatics has attracted enormous research interests recently along with many recognized achievements. To acquire knowledge of materials by previous experience, both feature descriptors and databases are essential for training machine learning (ML) models with high accuracy. In this regard, the electronic charge density ρ(r), which in principle determines the properties of materials at their ground state, can be considered as one of the most appropriate descriptors. However, the systematic electronic charge density ρ(r) database of inorganic materials is still in its infancy due to the difficulties in …
Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi, Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi, Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Faculty Publications
The majority of cyber infiltration & exfiltration intrusions leave a network footprint, and due to the multi-faceted nature of detecting network intrusions, it is often difficult to detect. In this work a Zeek-processed PCAP dataset containing the metadata of 36,667 network packets was modeled with several machine learning algorithms to classify normal vs. anomalous network activity. Principal component analysis with a 10% contamination factor was used to identify anomalous behavior. Models were created using recursive feature elimination on logistic regression and XGBClassifier algorithms, and also using Bayesian and bandit optimization of neural network hyperparameters. These models were trained on a …
Traffic Collision Avoidance System: False Injection Viability, John Hannah, Robert F. Mills, Richard Dill, Douglas D. Hodson
Traffic Collision Avoidance System: False Injection Viability, John Hannah, Robert F. Mills, Richard Dill, Douglas D. Hodson
Faculty Publications
Safety is a simple concept but an abstract task, specifically with aircraft. One critical safety system, the Traffic Collision Avoidance System II (TCAS), protects against mid-air collisions by predicting the course of other aircraft, determining the possibility of collision, and issuing a resolution advisory for avoidance. Previous research to identify vulnerabilities associated with TCAS’s communication processes discovered that a false injection attack presents the most comprehensive risk to veritable trust in TCAS, allowing for a mid-air collision. This research explores the viability of successfully executing a false injection attack against a target aircraft, triggering a resolution advisory. Monetary constraints precluded …
A Reduced-Order Lumped Model For Li-Ion Battery Packs During Operation, Paul T. Coman, Eric C. Darcy, Brad Strangways, Ralph E. White
A Reduced-Order Lumped Model For Li-Ion Battery Packs During Operation, Paul T. Coman, Eric C. Darcy, Brad Strangways, Ralph E. White
Faculty Publications
Modeling heat distribution in Li-ion battery packs can be challenging, especially if the battery pack is large and the cells are operated at high C-rates, which usually requires high-order physics-based mathematical models. Reduced and simplifying models can, however, be used at lower rates. This paper presents a fast novel reduced lumped model (RLM) that can be used to calculate the temperature increase during the high-current discharge of cylindrical Li-ion cells in a subscale of a battery pack. By reducing the PDE utilized to calculate the state of charge (SoC) to ODE's and solving them analytically, the reduced model can be …
Shifting Satellite Control Paradigms: Operational Cybersecurity In The Age Of Megaconstellations, Carl A. Poole, Robert A. Bettinger, Mark Reith
Shifting Satellite Control Paradigms: Operational Cybersecurity In The Age Of Megaconstellations, Carl A. Poole, Robert A. Bettinger, Mark Reith
Faculty Publications
The introduction of automated satellite control systems into a space-mission environment historically dominated by human-in-the-loop operations will require a more focused understanding of cybersecurity measures to ensure space system safety and security. On the ground-segment side of satellite control, the debut of privately owned communication antennas for rent and a move to cloud-based operations or mission centers will bring new requirements for cyber protection for both Department of Defense and commercial satellite operations alike. It is no longer a matter of whether automation will be introduced to satellite operations, but how quickly satellite operators can adapt to the onset of …
A Meta-Level Approach For Multilingual Taint Analysis, Damian Lyons, Dino Becaj
A Meta-Level Approach For Multilingual Taint Analysis, Damian Lyons, Dino Becaj
Faculty Publications
It is increasingly common for software developers to leverage the features and ease-of-use of different languages in building software systems. Nonetheless, interaction between different languages has proven to be a source of software engineering concerns. Existing static analysis tools handle the software engineering concerns of monolingual software but there is little general work for multilingual systems despite the increasing visibility of these systems. While recent work in this area has greatly extended the scope of multilingual static analysis systems, the focus has still been on a primary, host language interacting with subsidiary, guest language functions. In this paper we propose …
Wall Detection Via Imu Data Classification In Autonomous Quadcopters, Jason Hughes, Damian Lyons
Wall Detection Via Imu Data Classification In Autonomous Quadcopters, Jason Hughes, Damian Lyons
Faculty Publications
Abstract—An autonomous drone flying near obstacles needs to be able to detect and avoid the obstacles or it will collide with them. In prior work, drones can detect and avoid walls using data from camera, ultrasonic or laser sensors mounted either on the drone or in the environment. It is not always possible to instrument the environment, and sensors added to the drone consume payload and power - both of which are constrained for drones. This paper studies how data mining classification techniques can be used to predict where an obstacle is in relation to the drone based only on …
Zynq System-On-Chip Dma Messaging For Processor Monitoring, Daniel F. Koranek, Douglas D. Hodson, Scott R. Graham
Zynq System-On-Chip Dma Messaging For Processor Monitoring, Daniel F. Koranek, Douglas D. Hodson, Scott R. Graham
Faculty Publications
Xilinx Zynq-7000 System-on-Chip architectures combine an ARM Cortex-A9 core with an FPGA fabric. One benefit of this hybrid architecture is that it allows fast prototyping of designs where the security of either the processing system (PS) is monitored by the programmable logic (PL) or vice versa. The choice of implementing a design in the PS or PL is driven by cost-to-benefit analysis across many factors. This effort examines the design process required to construct security monitoring designs that use both the PS and PL. For background, this effort reviews similar security monitoring projects. For the effort, a PL peripheral was …
Sparc: Statistical Performance Analysis With Relevance Conclusions, Justin C. Tullos, Scott R. Graham, Jeremy D. Jordan, Pranav R. Patel
Sparc: Statistical Performance Analysis With Relevance Conclusions, Justin C. Tullos, Scott R. Graham, Jeremy D. Jordan, Pranav R. Patel
Faculty Publications
The performance of one computer relative to another is traditionally characterized through benchmarking, a practice occasionally deficient in statistical rigor. The performance is often trivialized through simplified measures, such as the approach of central tendency, but doing so risks a loss of perspective of the variability and non-determinism of modern computer systems. Authentic performance evaluations are derived from statistical methods that accurately interpret and assess data. Methods that currently exist within performance comparison frameworks are limited in efficacy, statistical inference is either overtly simplified or altogether avoided. A prevalent criticism from computer performance literature suggests that the results from difference …
A Statistical Impulse Response Model Based On Empirical Characterization Of Wireless Underground Channel, Abdul Salam, Mehmet C. Vuran, Suat Irmak
A Statistical Impulse Response Model Based On Empirical Characterization Of Wireless Underground Channel, Abdul Salam, Mehmet C. Vuran, Suat Irmak
Faculty Publications
Wireless underground sensor networks (WUSNs) are becoming ubiquitous in many areas. The design of robust systems requires extensive understanding of the underground (UG) channel characteristics. In this paper, an UG channel impulse response is modeled and validated via extensive experiments in indoor and field testbed settings. The three distinct types of soils are selected with sand and clay contents ranging from $13\%$ to $86\%$ and $3\%$ to $32\%$, respectively. The impacts of changes in soil texture and soil moisture are investigated with more than $1,200$ measurements in a novel UG testbed that allows flexibility in soil moisture control. Moreover, the …
Evaluating The Potential Of Drone Swarms In Nonverbal Hri Communication, Kasper Grispino, Damian Lyons, Truong-Huy Nguyen
Evaluating The Potential Of Drone Swarms In Nonverbal Hri Communication, Kasper Grispino, Damian Lyons, Truong-Huy Nguyen
Faculty Publications
Human-to-human communications are enriched with affects and emotions, conveyed, and perceived through both verbal and nonverbal communication. It is our thesis that drone swarms can be used to communicate information enriched with effects via nonverbal channels: guiding, generally interacting with, or warning a human audience via their pattern of motions or behavior. And furthermore that this approach has unique advantages such as flexibility and mobility over other forms of user interface. In this paper, we present a user study to understand how human participants perceived and interpreted swarm behaviors of micro-drone Crazyflie quadcopters flying three different flight formations to bridge …