Computer Sciences Commons^™

Simplification And Compression Service Construction Of 3d Model For Complex Products, Junjie Xue, Guoqiang Shi, Junhua Zhou, Huiyang Qu, Tao Luan, Ruiying Pu

Journal of System Simulation

Abstract: A simplification and compression (S&C) service construction method of the 3D model for the complex products is proposed. An improved method of the model data export and spatial index generation is adopted to preserve the assembly tree and label information of the model in the process of model S&C. An adaptive mesh simplification algorithm based on the mesh density is designed and implemented, and the meshes can be simplified adaptively on condition of satisfying the requirement of error for the geometric model. And a model data compression algorithm with the high compression ratio and efficiency is adopted to reduce …

3d Tree Model Matching Based On Tree Shape Space, Liang Shuang, Zuoteng Zhu, Jinyuan Jia

Journal of System Simulation

Abstract: The retrieval of the 3D tree models cannot get the higher accuracy, retrieving efficiency and descripting match. In order to solve the problems, a three-dimensional tree model matching method based on tree space is proposed. By constructing the tree space of the three-dimensional tree model dataset, the method computes the difference between the Euclidean distance and the geodesic distance in the tree space by means of spindle matching, contour matching, and branch matching, and gradually matches the corresponding model. The matching implementation results are displayed in the general model dataset and the self-designed model dataset respectively. …

Sight Helper, Misael Uzcategui

Other Student Works

No abstract provided.

Human Activity Recognition & Mobily Path Prediction, Priya Patel

Other Student Works

Individual Mobility is the study that depicts how individuals move inside a region or system. As of late a few researches have been accomplished for this reason and there has been a flood in enormous informational accessible in individual developments. Most of these information’s are gathered from cellphone or potentially GPS with variable accuracy relying upon the distance from the tower. Enormous scope information, for example, cell phone follows are significant hotspot for urban modeling. The individual travel designs breakdown into a solitary likelihood distribution however despite the assorted variety of their travel history people follow basic reproducible examples. This …

Context Aware Data Generation Through Domain Specific Language, Caleb Druckemiller

Other Student Works

No abstract provided.

Uprising Digital Cyber-Attacks And Stagnant Cyber Laws Worldwide, Rahul Parekh

Other Student Works

Cybersecurity is vast area for research with multiple domains. Any individual can focus on minimum topics for research and it becomes cumbersome to follow various tools and technique. In this research paper, I have tried to focus on digital cybersecurity crime and terrorism efforts by hackers however there are numerous ways to minimize the hacking activities. To reduce such attacks and understand the chronology, research paper will display research and understanding.

A Study Over Registration Server System Simulation, Maolin Hang

Other Student Works

This paper is a continuous study of the registration server system using a previous created real-time simulation application for my working product- T-Mobile Digits’ registration server system - an Enterprise-level solution ensembles Skype for Business, but with a sizable testing user pool.

As a standard system design normally includes the hardware infrastructure, computational logics and its own assigned rules/configures, and as all the complex system, a well-set server structure is the kernel for no matter testing or commercial purpose. The challenges are real and crucial for both business success besides the concerns of access capability and security. It will begin …

A Tool For Securities Analysis And Trading Strategy Back-Testing, Qiaosheng Yu

Other Student Works

The platform created in this project will help users to analyze stock/ETF performance, define and improve their asset allocation plans and trade ideas. Using real market data, user can do a deep dive into securities performance, historical correlation, risk and return. Furthermore, user can use the platform back test their trade ideas, modify the parameters, including percentage allocation, risk limits and rebalancing frequency, and create the best strategy which suits their risk/return criteria and long-term wealth management goals.

Stock Investment Helper, Mingrui Xu

Other Student Works

No abstract provided.

Evote – The Revolution Of Vote, Tianming Zhu

Other Student Works

This report focuses on independently developed applications that are primarily used to support group or group solutions. The tools used to develop this application include Android development tools, and Google Firebase development tools are also involved in the development process.

Chat Application With Distributed System, Shuyang Zhu

Other Student Works

This project created a web application for users to instantly communicate with each other. An the most important part of building a good chat application is focus on the data flow on web. This project spends a good amount of research to find the most appropriate technology for deliver the data flow, which is REST api for account management, WebSocket for text chat and WebRTC for video and audio and find the result pleasant.

Effective Smart Video Survillance And Alertinng, Kranthikumar Putta

Other Student Works

Machine learning models & AI are popular term in past couple of years. Almost every aspect on our daily life we are using these machine learning models & AI knowing or unknowing. The simple statement of this research paper is to prove reduction of human effort in camera surveillance by using machine learning models such as objection detection & facial recognition.

TensorFlow is leading open source tool developed and published by Google helps to build objection detection and facial recognition library. The code we about to develop can allow user to train model with his choice of images of objects …

Deep Learning Framework Of Vehicle Detection And Tracking System, Rui Zhang

Other Student Works

In the last semester, I designed a system to detect and track vehicle system on the highway. The system is based on the public deep learning framework and utilize pre-trained model to implement the functions of this system. In this paper, I will use my own framework to implement this system. This try will help us better understand the details of deep learning framework. I will public the code of deep learning framework and make sure everyone can modify it.

This semester will focus on the researching of Naïve Convolutional Neural Networks. Neural networks are commonlyused for the analysis of …

Nonlinear Least Squares 3-D Geolocation Solutions Using Time Differences Of Arrival, Michael V. Bredemann

Mathematics & Statistics ETDs

This thesis uses a geometric approach to derive and solve nonlinear least squares minimization problems to geolocate a signal source in three dimensions using time differences of arrival at multiple sensor locations. There is no restriction on the maximum number of sensors used. Residual errors reach the numerical limits of machine precision. Symmetric sensor orientations are found that prevent closed form solutions of source locations lying within the null space. Maximum uncertainties in relative sensor positions and time difference of arrivals, required to locate a source within a maximum specified error, are found from these results. Examples illustrate potential requirements …

Fast Clustering Using A Grid-Based Underlying Density Function Approximation, Daniel Brown

Master of Science in Computer Science Theses

Clustering is an unsupervised machine learning task that seeks to partition a set of data into smaller groupings, referred to as “clusters”, where items within the same cluster are somehow alike, while differing from those in other clusters. There are many different algorithms for clustering, but many of them are overly complex and scale poorly with larger data sets. In this paper, a new algorithm for clustering is proposed to solve some of these issues. Density-based clustering algorithms use a concept called the “underlying density function”, which is a conceptual higher-dimension function that describes the possible results from the continuous …

Achieving Obfuscation Through Self-Modifying Code: A Theoretical Model, Heidi Waddell

Senior Honors Theses

With the extreme amount of data and software available on networks, the protection of online information is one of the most important tasks of this technological age. There is no such thing as safe computing, and it is inevitable that security breaches will occur. Thus, security professionals and practices focus on two areas: security, preventing a breach from occurring, and resiliency, minimizing the damages once a breach has occurred. One of the most important practices for adding resiliency to source code is through obfuscation, a method of re-writing the code to a form that is virtually unreadable. …

Storage Management Strategy In Mobile Phones For Photo Crowdsensing, En Wang, Zhengdao Qu, Xinyao Liang, Xiangyu Meng, Yongjian Yang, Dawei Li, Weibin Meng

Department of Computer Science Faculty Scholarship and Creative Works

In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to …

Engagement And Computational Thinking Through Creative Coding, Dana Hoppe

Honors Program: Senior Projects (Public)

Rising enrollments in Computer Science pose an opportunity to engage students from diverse backgrounds and interests; and a challenge to deliver on positive learning outcomes. While student engagement is the driving factor for increased learning performance and retention, it has been declining to new lows for Computer Science students in recent years. In order to further explore the potential of contextualized computing as a tool for increasing engagement in computing and developing Computational Thinking aptitude in students, we have developed an introductory computing course contextualized with Art and Design with modules centered around guiding pedagogical principles and aimed at middle …

Perceived-Value-Driven Optimization Of Energy Consumption In Smart Homes, Atieh R. Khamesi, Simone Silvestri, Denise A. Baker, Alessandra De Paola

Computer Science Faculty Research & Creative Works

Residential energy consumption has been rising rapidly during the last few decades. Several research efforts have been made to reduce residential energy consumption, including demand response and smart residential environments. However, recent research has shown that these approaches may actually cause an increase in the overall consumption, due to the complex psychological processes that occur when human users interact with these energy management systems. In this article, using an interdisciplinary approach, we introduce a perceived-value driven framework for energy management in smart residential environments that considers how users perceive values of different appliances and how the use of some appliances …

Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning, Xi Hu

Theses and Dissertations

With the progressive development of infrastructure construction, conventional analytical methods such as correlation index, quantifying factors, and peer review are no longer satisfactory in support for decision-making of implementing an infrastructure project in the age of big data. This study proposes using a mathematical model named Fuzzy-Neural Comprehensive Evaluation Model (FNCEM) to improve the reliability of the feasibility study of infrastructure projects by using data mining and machine learning. Specifically, the data collection on time-series data, including traffic videos (278 Gigabytes) and historical weather data, uses transportation cameras and online searching, respectively. Meanwhile, the researcher sent out a questionnaire for …

Feature Extraction And Analysis Of Binaries For Classification, Micah Flack

Annual Research Symposium

The research project, Feature Extraction and, Analysis of Binaries for Classification, provides an in-depth examination of the features shared by unlabeled binary samples, for classification into the categories of benign or malicious software using several different methods. Because of the time it takes to manually analyze or reverse engineer binaries to determine their function, the ability to gather features and then instantly classify samples without explicitly programming the solution is incredibly valuable. It is possible to use an online service; however, this is not always viable depending on the sensitivity of the binary. With Python3 and the Pefile library, we …

Dugesia Japonica Is The Best Suited Of Three Planarian Species For High-Throughput Toxicology Screening, Danielle Ireland, Veronica Bochenek , '22, Daniel Chaiken , '20, Christina Rabeler, Sumi Onoe , '21, Ameet Soni, Eva-Maria S. Collins

Biology Faculty Works

High-throughput screening (HTS) using new approach methods is revolutionizing toxicology. Asexual freshwater planarians are a promising invertebrate model for neurotoxicity HTS because their diverse behaviors can be used as quantitative readouts of neuronal function. Currently, three planarian species are commonly used in toxicology research: Dugesia japonica, Schmidtea mediterranea, and Girardia tigrina. However, only D. japonica has been demonstrated to be suitable for HTS. Here, we assess the two other species for HTS suitability by direct comparison with D. japonica. Through quantitative assessments of morphology and multiple behaviors, we assayed the effects of 4 common solvents (DMSO, …

Game Theory Based Distributed Coordination With Multi-Agent Reinforcement Learning, Charlotte M. Morrison

Showcase of Osprey Advancements in Research and Scholarship (SOARS)

We study the problem of automated object manipulation using two arms of a Baxter robot. The robot uses a novel multi-agent reinforcement learning strategy to learn how to complete the task without any prior experience. It learns what actions to take by storing its interactions with the environment and uses these experiences to create a policy that guides future actions. Each of Baxter’s arms is modeled as an independent agent that can move and learn separately from the other. Each arm learns independent policies (i.e., environment state to robot action mapping) about how to best move in order to complete …

Simulation Environment For Object Manipulation With Soft Robots In Shared Autonomy, Devin Hunter, Fabio Stroppa, Allison Okamura

Showcase of Osprey Advancements in Research and Scholarship (SOARS)

The robots of today have grown to be of much more significant use than their predecessors. Robots are now being used in industries outside of the factory setting which can be seen primarily in the medical, transportation, and social fields. With robots taking on all of these new roles within our society, the establishment of robust human-robot collaboration is crucial in order for robots to be able to successfully complete desired tasks without becoming a hinderance to nearby humans. We explored this concept by implementing a shared-autonomy algorithm named MBSA (Motion Based Smart Assistance) to a soft robot simulation and …

Need For Adoption Of Koha Integrated Library Management Software In Nigerian Academic Libraries, Isaac Echezonam Anyira

Library Philosophy and Practice (e-journal)

The main objective of this paper is to justify the need for Nigerian libraries which have not automated their functions to adopt of Koha ILMS as their automation software. The paper therefore examined the need for automation, the reasons for the choice of Koha ILMS and potential benefits accruable to the libraries, and functions that Koha can facilitate their perform in their libraries. The paper identified the need for automation to include need to handle information explosion, for effective management of library operations, to improve operation speed, resource sharing etc; the choice of Koha was informed by its features and …

Teaching Data Carving Using The Real World Problem Of Text Message Extraction From Unstructured Mobile Device Data Dumps, Gary D. Cantrell, Joan Runs Through

Journal of Digital Forensics, Security and Law

Data carving is a technique used in data recovery to isolate and extract files based on file content without any file system guidance. It is an important part of data recovery and digital forensics, but it is also useful in teaching computer science students about file structure and binary encoding of information especially within a digital forensics program. This work demonstrates how the authors teach data carving using a real world problem they encounter in digital forensics evidence processing involving the extracting of text messages from unstructured small device binary extractions. The authors have used this problem for instruction in …

Cyber-Security Risks Of Fedwire, Mark J. Bilger

Journal of Digital Forensics, Security and Law

This paper will review the risks associated with the Federal Reserve's Fedwire network as a key resource necessary for the efficient function of the American financial system. It will examine the business model of the Fedwire system of real-time interbank transfers, the network characteristics of Fedwire, and the possibility of a successful attack on Fedwire and its potential impact on the U.S. financial system.

A Data Scientist Looks At Covid-19 Part I: Local And National Statistics, Anthony Breitzman

College of Science & Mathematics Departmental Research

No abstract provided.

Information Mining For Covid-19 Research From A Large Volume Of Scientific Literature, Sabber Ahamed, Manar D. Samad

Computer Science Faculty Research

The year 2020 has seen an unprecedented COVID-19 pandemic due to the outbreak of a novel strain of coronavirus in 180 countries. In a desperate effort to discover new drugs and vaccines for COVID-19, many scientists are working around the clock. Their valuable time and effort may benefit from computer-based mining of a large volume of health science literature that is a treasure trove of information. In this paper, we have developed a graph-based model using abstracts of 10,683 scientific articles to find key information on three topics: transmission, drug types, and genome research related to coronavirus. A subgraph is …

A Distributed Model-View-Controller Design Pattern For A Graphical Remote Control Of A Multi-User Application, Dale E. Parson

Computer Science and Information Technology Faculty

The Model-View-Controller (MVC) is a design pattern for architecting the interactions among human users of graphical computing systems with the software Controller that manages user input, the Model that houses system state, and the View that projects the Model state into intelligible graphical form. The present work examines extending MVC into a Distributed Model-View-Controller pattern, starting with a stand-alone MVC system that is amenable to distribution over a local area network (LAN). Distribution takes the form of cloning a stand-alone MVC application into distinct client and server programs, and then altering each for its purpose while maintaining the initial graphical …