Digital Commons Network^™

Data-Driven Predictive Modeling To Enhance Search Efficiency Of Glowworm-Inspired Robotic Swarms In Multiple Emission Source Localization Tasks, Payal Nandi

Mechanical & Aerospace Engineering Theses & Dissertations

In time-sensitive search and rescue applications, a team of multiple mobile robots broadens the scope of operational capabilities. Scaling multi-robot systems (< 10 agents) to larger robot teams (10 – 100 agents) using centralized coordination schemes becomes computationally intractable during runtime. One solution to this problem is inspired by swarm intelligence principles found in nature, offering the benefits of decentralized control, fault tolerance to individual failures, and self-organizing adaptability. Glowworm swarm optimization (GSO) is unique among swarm-based algorithms as it simultaneously focuses on searching for multiple targets. This thesis presents GPR-GSO—a modification to the GSO algorithm that incorporates Gaussian Process Regression (GPR) based data-driven predictive modeling—to improve the search efficiency of robotic swarms in multiple emission source localization tasks. The problem formulation and methods are presented, followed by numerical simulations to illustrate the working of the algorithm. Results from a comparative analysis show that the GPR-GSO algorithm exceeds the performance of the benchmark GSO algorithm on evaluation metrics of swarm size, search completion time, and travel distance.

A Quantitative Comparison Of Algorithmic And Machine Learning Network Flow Throughput Prediction, Cayden Wagner

All Theses

Applications ranging from video meetings, live streaming, video games, autonomous vehicle operations, and algorithmic trading heavily rely on low latency communication to operate optimally. A solution to fully support this growing demand for low latency is called dual-queue active queue management (AQM). Dual-queue AQM's functionality is reduced without network traffic throughput prediction.

Perhaps due to the current popularity of machine learning, there is a trend to adopt machine learning models over traditional algorithmic throughput prediction approaches without empirical support. This study tested the effectiveness of machine learning as compared to time series forecasting algorithms in predicting per-flow network traffic throughput …

Self-Efficacy Development In Elementary-Aged Learners Through Dance As An Algorithmic Thinking Tool, Niva Shrestha

Honors Theses

The purpose of this research is to demonstrate the effectiveness of a transdisciplinary approach in teaching computational thinking through dance to elementary-aged learners, with primary attention to females. With limited literature available on how pre-adolescents begin to construct conceptions of computer science and other engineering domains, including potential career pathways, the incentive of this project was to leverage a day camp for about 20 rising 3rd - 5th-grade learners to assess their identity development in computer science. Modules that teach computational thinking through dance paired with Unruly splats (block-based programmable electronic gadgets) were implemented. By conducting pre-and post-surveys and a …

Comparing Political Implications Of Punitive Paradigms In Digital Surveillance And Data Driven Algorithms Between The Polities Of The United States Of America And The People's Republic Of China, Shedelande Lily Carpenter

Senior Projects Spring 2022

Senior Project submitted to The Division of Social Studies of Bard College.

Asynchronous, Distributed Optical Mutual Exclusion And Applications, Ahmed Bahaael Mansour

LSU Doctoral Dissertations

Silicon photonics have drawn much recent interest in the setting of intra-chip andmodule communication. In this dissertation, we address a fundamental computationalproblem, mutual exclusion, in the setting of optical interconnects. As a main result, wepropose an optical network and an algorithm for it to distribute a token (shared resource)mutually exclusively among a set ofnprocessing elements. Following a request, the tokenis granted in constant amortized time andO(n) worst case time; this assumes constantpropagation time for light within the chip. Additionally, the distribution of tokens is fair,ensuring that no token request is denied more thann−1 times in succession; this is thebest possible. …

Object Manipulation With Modular Planar Tensegrity Robots, Maxine Perroni-Scharf

Dartmouth College Undergraduate Theses

This thesis explores the creation of a novel two-dimensional tensegrity-based mod- ular system. When individual planar modules are linked together, they form a larger tensegrity robot that can be used to achieve non-prehensile manipulation. The first half of this dissertation focuses on the study of preexisting types of tensegrity mod- ules and proposes different possible structures and arrangements of modules. The second half describes the construction and actuation of a modular 2D robot com- posed of planar three-bar tensegrity structures. We conclude that tensegrity modules are suitably adapted to object manipulation and propose a future extension of the modular 2D …

The Exploration And Analysis Of Mancala From An Ai Perspective, Trevon J. Hunter

Honors Theses

Through the study of popular games such as Chess and Go, countless artificial intelligence (AI) research has been conducted in an attempt to create algorithms equipped for adversarial search problems. However, there are still a plethora of avenues that offer insight into further development. Mancala is traditionally a two-player board game that originated in the East and offers a unique opponent-based playing experience. This thesis not only attempts to create a competitive AI algorithm for mancala games by analyzing the performance of several different algorithms on this classic board game, but it also attempts to extract applications that may have …

Efficient Elevator Algorithm, Sean M. Toll, Owen Barbour, Carl Edwards, Daniel Nichols, Austin Day

Chancellor’s Honors Program Projects

No abstract provided.

Let’S Face It: The Effect Of Orthognathic Surgery On Facial Recognition Algorithm Analysis, Carolyn Bradford Dragon

Theses and Dissertations

Aim: To evaluate the ability of a publicly available facial recognition application program interface (API) to calculate similarity scores for pre- and post-surgical photographs of patients undergoing orthognathic surgeries. Our primary objective was to identify which surgical procedure(s) had the greatest effect(s) on similarity score.

Methods: Standard treatment progress photographs for 25 retrospectively identified, orthodontic-orthognathic patients were analyzed using the API to calculate similarity scores between the pre- and post-surgical photographs. Photographs from two pre-surgical timepoints were compared as controls. Both relaxed and smiling photographs were included in the study to assess for the added impact of facial pose on …

Youtube’S Terms Of Service: Posthumanism, Algorithms, And Professional Writing, Sarah Bresnahan

Graduate Research Theses & Dissertations

This thesis aims to examine YouTube’s Terms of Service as it applies to content creators (known as YouTubers) who use the platform as a means of financial gain and how YouTube’s demonetization policy via an algorithm is negatively affecting them. I conducted a case study featuring one creator, Michelle Guido, and attempted to determine why some of her content is demonetized when it fulfills YouTube’s content standards for monetization. This study is meant as an examination through the lens of Dr. N. Katherine Hayles’s theory of posthumanism as stated in her book, How We Became Posthuman, and will offer insight …

Oracle Guided Incremental Sat Solving To Reverse Engineer Camouflaged Circuits, Xiangyu Zhang

Masters Theses

This study comprises two tasks. The first is to implement gate-level circuit camouflage techniques. The second is to implement the Oracle-guided incremental de-camouflage algorithm and apply it to the camouflaged designs.

The circuit camouflage algorithms are implemented in Python, and the Oracle- guided incremental de-camouflage algorithm is implemented in C++. During this study, I evaluate the Oracle-guided de-camouflage tool (Solver, in short) performance by de-obfuscating the ISCAS-85 combinational benchmarks, which are camouflaged by the camouflage algorithms. The results show that Solver is able to efficiently de-obfuscate the ISCAS-85 benchmarks regardless of camouflaging style, and is able to do so 10.5x …

Smarter Neat Nets, Ryan Swords Dehaven

Master's Theses

This paper discusses a modification to improve usability and functionality of a ge- netic neural net algorithm called NEAT (NeuroEvolution of Augmenting Topolo- gies). The modification aims to accomplish its goal by automatically changing parameters used by the algorithm with little input from a user. The advan- tage of the modification is to reduce the guesswork needed to setup a successful experiment with NEAT that produces a usable Artificial Intelligence (AI). The modified algorithm is tested against the unmodified NEAT with several different setups and the results are discussed. The algorithm shows strengths in some areas but can increase the …

Most Progress Made Algorithm: Combating Synchronization Induced Performance Loss On Salvaged Chip Multi-Processors, Jacob J. Dutson

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Most modern personal computers come with processors which contain multiple cores. Often, one or more of these cores is damaged during manufacturing. These faults are increasing as manufacturers try to make processors run faster. Many processor designs allow a damaged core to continue working after manufacturing, but these salvaged cores run slower than a fully functional core.

In an attempt to make software run as fast as possible for its users, software designers write applications that are split into multiple parts called threads. These threads can be run on separate cores at the same time and get more work done …

Synthetic Aperture Radar Tool And Libraries: A Framework For Geo-Referenced Data Processing And Algorithm Prototyping, Nathan R. Crookston

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Creating a system for Synthetic Aperture Radar (SAR) image formation can be a huge undertaking as it requires knowledge of several disparate domains. Researchers may be prevented from applying interesting techniques in a particular domain due to hurdles in working with those areas outside their area of interest. This paper presents the SyntheTic Aperture Radar Tool and Libraries (STARTAL) framework for SAR processing that simplifies adding new data formats and prototyping algorithms.

STARTAL provides a user interface for viewing the full data region on ground geometry, selecting sub-regions to process, and viewing processed results. Many common, difficult tasks are provided …

Scalable Co-Evolution Of Soft Robot Properties And Gaits, Davis K. Knox

Honors Theses

The field of soft robotics is very promising; applications in-clude urban search and rescue and covert surveillance, but these projects are not yet realized, partly because of the difficulties in soft robot shape and locomotion design. Be-cause of this, traditional design methods do not prove to be effective. This project attempts to come up with solu-tions to this soft robot design problem; utilizing a genetic algorithm, a computer simulation of Darwin’s “Survival of the Fittest,” this project attempts to make soft bodies move. This genetic algorithm evaluates each solution in simulation, and assigns each one a fitness based on distance …

An Fpga Based Implementation Of The Exact Stochastic Simulation Algorithm, Phani Bharadwaj Vanguri

Masters Theses

Mathematical and statistical modeling of biological systems is a desired goal for many years. Many biochemical models are often evaluated using a deterministic approach, which uses differential equations to describe the chemical interactions. However, such an approach is inaccurate for small species populations as it neglects the discrete representation of population values, presents the possibility of negative populations, and does not represent the stochastic nature of biochemical systems. The Stochastic Simulation Algorithm (SSA) developed by Gillespie is able to properly account for these inherent noise fluctuations. Due to the stochastic nature of the Monte Carlo simulations, large numbers of simulations …

High Performance Text Document Clustering, Yanjun Li

Browse all Theses and Dissertations

Data mining, also known as knowledge discovery in database (KDD), is the process to discover interesting unknown knowledge from a large amount of data. Text mining is to apply data mining techniques to extract interesting and nontrivial information and knowledge from unstructured text. Text clustering is one of important techniques of text mining, which is the unsupervised classification of similar documents into different groups.

This research focuses on improving the performance of text clustering. We investigated the text clustering algorithms in four aspects: document representation, documents closeness measurement, high dimension reduction and parallelization. We propose a group of high performance …