Systems and Communications Commons^™

Enhanced Interest Aware Peoplerank For Opportunistic Mobile Social Networks, Yosra Saad Shahin

Theses and Dissertations

Network infrastructures are being continuously challenged by increased demand, resource-hungry applications, and at times of crisis when people need to work from homes such as the current Covid-19 epidemic situation, where most of the countries applied partial or complete lockdown and most of the people worked from home. Opportunistic Mobile Social Networks (OMSN) prove to be a great candidate to support existing network infrastructures. However, OMSNs have copious challenges comprising frequent disconnections and long delays. we aim to enhance the performance of OMSNs including delivery ratio and delay. We build upon an interest-aware social forwarding algorithm, namely Interest Aware PeopleRank …

Reinforcement Learning-Based Access Schemes In Cognitive Radio Networks, Ehab Maged Elguindy

Theses and Dissertations

In this thesis, we propose different MAC protocols based on three Reinforcement Learning (RL) approaches, namely Q-Learning, Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG). We exploit the primary user (PU) feedback, in the form of ARQ and CQI bits, to enhance the performance of the secondary user (SU) MAC protocols. Exploiting the PU feedback information can be applied on the top of any SU sensing-based MAC protocol. Our proposed model relies on two main pillars, namely, an infinite-state Partially Observable Markov Decision Process (POMDP) to model the system dynamics besides a queuing-theoretic model for the PU queue; the …

An Optimized Lte-Based Technique For Drone Base Station 3d Placement And Resource Allocation In Delay-Sensitive M2m Networks, Ahmed Fahim

Theses and Dissertations

The deployment of drone-mounted communication systems has received increasing interest and attention recently as it allows significant improvement to the network access capacity and coverage. Many applications can benefit from such deployments in particular machine-to-machine (M2M) communications. Drones are expected to facilitate extending wireless network access for both human users and the smart machine-type-communication devices (MTCDs) that have strict and diverse quality of service (QoS) requirements. In this thesis, we propose an optimal solution for the dynamic placement of an LTE drone-mounted base station to maximize the coverage of MTCDs deployed over a large geographical area in disaster situations or …

Time Series Data Analysis Using Machine Learning-(Ml) Approach, Mvv Prasad Kantipudi Dr., Pradeep Kumar N.S Dr., S.Sreenath Kashyap Dr., Ss Anusha Vemuri Ms

Library Philosophy and Practice (e-journal)

Healthcare benefits related to continuous monitoring of human movement and physical activity can potentially reduce the risk of accidents associated with elderly living alone at home. Based on the literature review, it is found that many studies focus on human activity recognition and are still active towards achieving practical solutions to support the elderly care system. The proposed system has introduced a joint approach of machine learning and signal processing technology for the recognition of human's physical movements using signal data generated by accelerometer sensors. The framework adopts the concept of DSP to select very descriptive feature sets and uses …

Iotiwc: Iot Industrial Wireless Controller, Mohammad Abdellatif, Tassnim , Awad, Walaa Mohamed

Electrical Engineering

Industrial controller systems are crucially essential to the cutting edge power systems industries. Industrial controllers link the integrated technologies of a computer, communication devices, and electric devices. The communication systems act as a physical intermediary layer for transferring, controlling, and acquirement of data within the system from distant locations. This chapter discusses the Supervisory Control And Data Acquisition (SCADA) systems and proposes a similar system that is an IoT based industrial wireless controller. The proposed system can control multiple devices through the network without the need to be physically near the devices. Because it uses simple and cheap devices, the …

Dales Objects: A Large Scale Benchmark Dataset For Instance Segmentation In Aerial Lidar, Nina M. Singer, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

We present DALES Objects, a large-scale instance segmentation benchmark dataset for aerial lidar. DALES Objects contains close to half a billion hand-labeled points, including semantic and instance segmentation labels. DALES Objects is an extension of the DALES (Varney et al., 2020) dataset, adding additional intensity and instance segmentation annotation. This paper provides an overview of the data collection, preprocessing, hand-labeling strategy, and final data format. We propose relevant evaluation metrics and provide insights into potential challenges when evaluating this benchmark dataset. Finally, we provide information about how researchers can access the dataset for their use at go.udayton.edu/dales3d.

6g Enabled Smart Infrastructure For Sustainable Society: Opportunities, Challenges, And Research Roadmap, Abotinam Lucky Imoize, Oluwadara Adedeji, Nistha Tandiya, Sachin Shetty

VMASC Publications

The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to …

Ieee Access Special Section Editorial: Trends And Advances In Bio-Inspired Image-Based Deep Learning Methodologies And Applications, Peter Peer, Carlos M. Travieso-Gonzalez, Vijayan K. Asari, Malay Kishore Dutta

Electrical and Computer Engineering Faculty Publications

Many of the technological advances we enjoy today have been inspired by biological systems due to their ease of operation and outstanding efficiency. Designing technological solutions based on biological inspiration has become a cornerstone of research in a variety of areas ranging from control theory and optimization to computer vision, machine learning, and artificial intelligence. Especially in the latter few areas, biologically relevant solutions are becoming increasingly important as we look for new ways to make artificial systems more efficient, intelligent, and overall effective.

A Two-Level Information Modelling Translation Methodology And Framework To Achieve Semantic Interoperability In Constrained Geoobservational Sensor Systems, Paul Stacey

Doctoral

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation.

Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data …

Hard Hat Ambient Liability Observer (Halo), Hunter Hykes, Nathan Kish, Brian Thomson

Williams Honors College, Honors Research Projects

Capturing workplace incident information is a growing area of concern for most companies. To assist with this, the design team proposed the H.A.L.O. This design uses time-of-flight sensors connected to LEDs to create a proximity-based hazard warning system. It also records incident data using an accelerometer and micro-SD card. This helps workers avoid some of the most common workplace injuries, slips, trips, and falls and accidental collisions.

Students have created a design with engineering, and marketing requirements that accomplish this task. The proposed design allows for this monitoring and mitigation systems to be attached to hard hats. Team members developed …

Design Project: Smart Headband, John Michel, Jack Durkin, Noah Lewis

Williams Honors College, Honors Research Projects

Concussion in sports is a prevalent medical issue. It can be difficult for medical professionals to diagnose concussions. With the fast pace nature of many sports, and the damaging effects of concussions, it is important that any concussion risks are assessed immediately. There is a growing trend of wearable technology that collects data such as steps and provides the wearer with in-depth information regarding their performance. The Smart Headband project created a wearable that can record impact data and provide the wearer with a detailed analysis on their risk of sustaining a concussion. The Smart Headband uses accelerometers and gyroscopes …

Automated Blind Control, Daniel Nahra, Matthew Lacek, Timothy Kurczewski, William Daulton Baksa

Williams Honors College, Honors Research Projects

The objective of this project would be to design and prototype an automated, light and temperature sensing window blinds system. The device would detect temperature, both inside and outside, and incoming sunlight to determine proper window blind position for maximum energy savings. The user would also have the ability to change the settings of the blind from a remote device to a setting that they desire at any given time

Soil Sensor Network, Andrea Wyder, Ross Klonowski, Alexis Alves, Luke Farnsworth

Williams Honors College, Honors Research Projects

Water management during crop irrigation is a problem for the agricultural industry. To help farmers better maintain water usage, a wireless soil sensor network comprised of a sensor pod and wireless communication has been designed and implemented. It was proven that the sensor pod can be installed 6-8 inches below the ground and communicate up to at least a 6km distance back to the gateway. The senor pod shells have a 2 mm thick shell to prevent the pod from shattering when coming into contact with the ground after being released from the planter, as calculated through the force of …

Mental Health And The Covid-19 Pandemic: Analysis Of Twitter Discourse, Omar El-Gayar, Abdullah Wahbeh, Tareq Nasralah, Ahmed El Noshokaty, Mohammad A. Al-Ramahi

Computer Information Systems Faculty Publications (Archived)

This study analyzed Twitter discourse to understand the association of the COVID-19 pandemic with mental health. The study compared tweets’ volume over time, tweets’ volume per mental health category, emotions, and the top hashtags on mental health before and after November 2019, the month on which the first COVID-19 case was reported. We analyzed a total of 273 million English tweets on mental health collected from 56 million unique users. Results and analysis showed a significant shift in trend for the volume of tweets on mental health over time. There was also a notable increase in the volume of tweets …

Coordinated Control For Dc Energy Hubs Involving Ders, Evs, And Subway Systems, Rohama Ahmad

Dissertations and Theses

No abstract provided.

Re-Design Of Precision Signal Conditioning Circuit For Detecting Schumann Resonance, Rohith Bikkina

Graduate Theses, Dissertations, and Problem Reports (ETD)

Extremely low frequencies signals are waves between 3 to 30Hz and corresponding wavelengths between 10,000 to 100,000 kilometers. The specific signals used here are generated from lightning and are excited at frequencies around 8Hz, 14Hz, 20Hz. These are often called Schumann Resonance frequencies. Several stations have been built around the world for identifying ELF waves. All of those required a sparsely populated area that was far away from electric power lines because of interference from electric noise at 50 Hz and 60Hz. This project develops and tests an amplifier and filter circuit that should assist in identifying the Schumann Resonance …

Deep Models For Improving The Performance And Reliability Of Person Recognition, Sobhan Soleymani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Deep models have provided high accuracy for different applications such as person recognition, image segmentation, image captioning, scene description, and action recognition. In this dissertation, we study the deep learning models and their application in improving the performance and reliability of person recognition. This dissertation focuses on five aspects of person recognition: (1) multimodal person recognition, (2) quality-aware multi-sample person recognition, (3) text-independent speaker verification, (4) adversarial iris examples, and (5) morphed face images. First, we discuss the application of multimodal networks consisting of face, iris, fingerprint, and speech modalities in person recognition. We propose multi-stream convolutional neural network architectures …

Automatic Dependent Surveillance Broadcast (Ads-B) Security Mitigation Through Multilateration, Skylar Stroman

UNF Graduate Theses and Dissertations

Automatic Dependent Surveillance Broadcast (ADS-B) was mandated January 1^st, 2020 to all commercial aircraft that fly over 10,000 ft. This radio frequency (RF) based technology is part of an international plan to phase out radar-based surveillance within the airspace. Unfortunately, due to a lack of encryption and other security measures, ADS-B transmission remains open for possible exploitation. This thesis will explore the use of Multilateration (MLAT) to validate location data provided within the ADS-B framework. MLAT uses the Time Difference of Arrival (TDOA) at multiple locations to determine the origin of a received signal. Additionally, as MLAT greatly …

Formal Power Series Approach To Nonlinear Systems With Static Output Feedback, G.S. Venkatesh, W. Steven Gray

Electrical & Computer Engineering Faculty Publications

The goal of this paper is to compute the generating series of a closed-loop system when the plant is described in terms of a Chen-Fliess series and static output feedback is applied. The first step is to reconsider the so called Wiener-Fliess connection consisting of a Chen-Fliess series followed by a memoryless function. Of particular importance will be the contractive nature of this map, which is needed to show that the closed-loop system has a Chen-Fliess series representation. To explicitly compute the generating series, two Hopf algebras are needed, the existing output feedback Hopf algebra used to describe dynamic output …

Continuity Of Chen-Fliess Series For Applications In System Identification And Machine Learning, Rafael Dahmen, W. Steven Gray, Alexander Schmeding

Electrical & Computer Engineering Faculty Publications

Model continuity plays an important role in applications like system identification, adaptive control, and machine learning. This paper provides sufficient conditions under which input-output systems represented by locally convergent Chen-Fliess series are jointly continuous with respect to their generating series and as operators mapping a ball in an Lp-space to a ball in an Lq-space, where p and q are conjugate exponents. The starting point is to introduce a class of topological vector spaces known as Silva spaces to frame the problem and then to employ the concept of a direct limit to describe convergence. The proof of the main …