Computer Sciences Commons^™

Design Exploration And Application Of Reversible Circuits In Emerging Technologies, Saurabh Kotiyal

USF Tampa Graduate Theses and Dissertations

The reversible logic has promising applications in emerging computing paradigms, such as quantum computing, quantum dot cellular automata, optical computing, etc. In reversible logic gates, there is a unique one-to-one mapping between the inputs and outputs. To generate a useful gate function, the reversible gates require some constant ancillary inputs called ancilla inputs. Also to maintain the reversibility of the circuits some additional unused outputs are required that are referred to as the garbage outputs. The number of ancilla inputs, the number of garbage outputs and quantum cost plays an important role in the evaluation of reversible circuits. Thus minimizing …

Investigating Security Attacks On Wireless Sensor Networks (Wsns) Via An Iot Environmental Monitoring System, Mauricio Tellez Nava, Samy El-Tawab, M. Hossain Heydari

Showcase of Graduate Student Scholarship and Creative Activities

To demonstrate our work, we have implemented a secure temperature monitoring system that makes use of the IEEE 802.15.4 standard, AES-128 hardware encryption and TelosB sensor nodes. In our design, we consider one of the nodes to be a coordinator node, which plays the role of a central node that establishes network association requests, listens for incoming packets, decrypts the incoming packets and forwards any received packets to the monitoring device. Three other nodes act as end-devices, which play the role of collecting temperature measurements, encrypting the collected data and sending the encrypted packets to the coordinator node. A last …

Optimal Scheduling Of Pev Charging/Discharging In Microgrids With Combined Objectives, Chong Cao, Ming Cheng, Bo Chen

Michigan Tech Publications, Part 1

While renewable power generation and vehicle electrification are promising solutions to reduce greenhouse gas emissions, it faces great challenges to effectively integrate them in a power grid. The weather-dependent power generation of renewable energy sources, such as Photovoltaic (PV) arrays, could introduce significant intermittency to a power grid. Meanwhile, uncontrolled PEV charging may cause load surge in a power grid. This paper studies the optimization of PEV charging/discharging scheduling to reduce customer cost and improve grid performance. Optimization algorithms are developed for three cases: 1) minimize cost, 2) minimize power deviation from a pre-defined power profile, and 3) combine objective …

Grounding Robot Motion In Natural Language And Visual Perception, Scott Alan Bronikowski

Open Access Dissertations

The current state of the art in military and first responder ground robots involves heavy physical and cognitive burdens on the human operator while taking little to no advantage of the potential autonomy of robotic technology. The robots currently in use are rugged remote-controlled vehicles. Their interaction modalities, usually utilizing a game controller connected to a computer, require a dedicated operator who has limited capacity for other tasks.

I present research which aims to ease these burdens by incorporating multiple modes of robotic sensing into a system which allows humans to interact with robots through a natural-language interface. I conduct …

In The Face Of Anticipation: Decision Making Under Visible Uncertainty As Present In The Safest-With-Sight Problem, Bryan A. Knowles

Masters Theses & Specialist Projects

Pathfinding, as a process of selecting a fixed route, has long been studied in

Computer Science and Mathematics. Decision making, as a similar, but intrinsically different, process of determining a control policy, is much less studied. Here, I propose a problem that appears to be of the first class, which would suggest that it is easily solvable with a modern machine, but that would be too easy, it turns out. By allowing a pathfinding to anticipate and respond to information, without setting restrictions

on the \structure" of this anticipation, selecting the \best step" appears to be an intractable problem.

After …

Pedestrian Detection Using Basic Polyline: A Geometric Framework For Pedestrian Detection, Liang Gongbo

Masters Theses & Specialist Projects

Pedestrian detection has been an active research area for computer vision in recently years. It has many applications that could improve our lives, such as video surveillance security, auto-driving assistance systems, etc. The approaches of pedestrian detection could be roughly categorized into two categories, shape-based approaches and appearance-based approaches. In the literature, most of approaches are appearance-based. Shape-based approaches are usually integrated with an appearance-based approach to speed up a detection process.

In this thesis, I propose a shape-based pedestrian detection framework using the geometric features of human to detect pedestrians. This framework includes three main steps. Give a static …

Development Of A Wireless Mobile Computing Platform For Fall Risk Prediction, Akm Jahangir Alam Majumder

Dissertations (1934 -)

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is …

Flying By Fire: Making Controlled Burns Safer For Humans And Uavs, Rebecca Horzewski, Carrick Detweiler

UCARE: Research Products

A temperature sensing circuit board was developed that will allow Nimbus Lab's controlled burn starting UAV to react to the temperatures around it.

Learning From Minimally Labeled Data With Accelerated Convolutional Neural Networks, Aysegul Dundar

Open Access Dissertations

The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an image as an input and correctly classifies it into one of the user-determined categories. There are several important properties to be satisfied by the mapping function for visual understanding. First, the function should produce good representations of the visual world, which will be able to recognize images independently of pose, scale and illumination. Furthermore, the designed artificial vision system has to learn these representations by itself. Recent studies on Convolutional Neural Networks (ConvNets) produced promising advancements in visual understanding. These networks attain significant …

Learning In Vision And Robotics, Daniel P. Barrett

Open Access Dissertations

I present my work on learning from video and robotic input. This is an important problem, with numerous potential applications. The use of machine learning makes it possible to obtain models which can handle noise and variation without explicitly programming them. It also raises the possibility of robots which can interact more seamlessly with humans rather than only exhibiting hard-coded behaviors. I will present my work in two areas: video action recognition, and robot navigation. First, I present a video action recognition method which represents actions in video by sequences of retinotopic appearance and motion detectors, learns such models automatically …

Incorporating Energy Related Concepts Into Ee And Cs Laboratory Work And Coursework, Jack Adams, Christopher S. Stuetzle

Electrical Engineering Faculty Publications

During the course of this interdisciplinary effort, members of the Electrical Engineering (EE) and Computer Science (CS) departments collaborated on energy related curricular efforts. Initially work was carried out to develop and utilize an inexpensive, open-source system for measuring, storing, and displaying energy related data from across campus. Hardware and software components chosen were open source or free for educational use. A low power Linux server was utilized. The LAN-enabled Arduinos included sensors to measure energy related quantities such as power and temperature. EE and CS students were engaged in various aspects of the project – EE students focused on …

Front Matter: Proceedings Of The Maics 2016 Conference, University Of Dayton

Content presented at the MAICS conference

Front matter contains:

• A list of program chairs and committee members
• Foreword to the proceedings by James P. Buckley, conference chair; Saverio Perugini, general chair

Editors: Phu H. Phung, University of Dayton; Ju Shen, University of Dayton; Michael Glass, Valparaiso University

User-Centric Workload Analytics: Towards Better Cluster Management, Suhas Raveesh Javagal

Open Access Theses

Effective management of computing clusters and providing a high quality customer support is not a trivial task. Due to rise of community clusters there is an increase in the diversity of workloads and the user demographic. Owing to this and privacy concerns of the user, it is difficult to identify performance issues, reduce resource wastage and understand implicit user demands. In this thesis, we perform in-depth analysis of user behavior, performance issues, resource usage patterns and failures in the workloads collected from a university-wide community cluster and two clusters maintained by a government lab. We also introduce a set of …

A Faster Version Of Louvain Method For Community Detection For Efficient Modeling And Analytics Of Cyber Systems, Sunanda Vivek Shanbhaq

Open Access Theses

Cyber networks are complex networks with various hosts forming the entities of the network and the communication between them forming the edges of the network. Most cyber networks exhibit a community structure. A community is a group of nodes that are densely connected with each other as compared to other nodes in the network. Representing an IP network in the form of communities helps in viewing the network from different levels of granularity and makes the visualization of the network cleaner and more pleasing to the eye. This will help significantly in cyber attack detection in large scale cyber networks. …

Unsupervised Learning Framework For Large-Scale Flight Data Analysis Of Cockpit Human Machine Interaction Issues, Abhishek B. Vaidya

Open Access Theses

As the level of automation within an aircraft increases, the interactions between the pilot and autopilot play a crucial role in its proper operation. Issues with human machine interactions (HMI) have been cited as one of the main causes behind many aviation accidents. Due to the complexity of such interactions, it is challenging to identify all possible situations and develop the necessary contingencies. In this thesis, we propose a data-driven analysis tool to identify potential HMI issues in large-scale Flight Operational Quality Assurance (FOQA) dataset. The proposed tool is developed using a multi-level clustering framework, where a set of basic …

On The 3d Point Cloud For Human-Pose Estimation, Kai-Chi Chan

Open Access Dissertations

This thesis aims at investigating methodologies for estimating a human pose from a 3D point cloud that is captured by a static depth sensor. Human-pose estimation (HPE) is important for a range of applications, such as human-robot interaction, healthcare, surveillance, and so forth. Yet, HPE is challenging because of the uncertainty in sensor measurements and the complexity of human poses. In this research, we focus on addressing challenges related to two crucial components in the estimation process, namely, human-pose feature extraction and human-pose modeling.

In feature extraction, the main challenge involves reducing feature ambiguity. We propose a 3D-point-cloud feature called …

Optimal Monitoring And Mitigation Of Systemic Risk In Lending Networks, Zhang Li

Open Access Dissertations

This thesis proposes optimal policies to manage systemic risk in financial networks. Given a one-period borrower-lender network in which all debts are due at the same time and have the same seniority, we address the problem of allocating a fixed amount of cash among the nodes to minimize the weighted sum of unpaid liabilities. Assuming all the loan amounts and cash flows are fixed and that there are no bankruptcy costs, we show that this problem is equivalent to a linear program. We develop a duality-based distributed algorithm to solve it which is useful for applications where it is desirable …

Energy Efficiency In Data Collection Wireless Sensor Networks, Miquel Andres Navarro Patino

Open Access Dissertations

This dissertation studies the problem of energy efficiency in resource constrained and heterogeneous wireless sensor networks (WSNs) for data collection applications in real-world scenarios. The problem is addressed from three different perspectives: network routing, node energy profiles, and network management. First, the energy efficiency in a WSN is formulated as a load balancing problem, where the routing layer can diagnose and exploit the WSN topology redundancy to reduce the data traffic processed in critical nodes, independent of their hardware platform, improving their energy consumption and extending the network lifetime. We propose a new routing strategy that extends traditional cost-based routing …

Smokey: Ubiquitous Smoking Detection With Commercial Wifi Infrastructures, Xiaolong Zheng, Jiliang Wang, Longfei Shangguan, Zimu Zhou, Yunhao Liu

Research Collection School Of Computing and Information Systems

Even though indoor smoking ban is being put into practice in civilized countries, existing vision or sensor-based smoking detection methods cannot provide ubiquitous smoking detection. In this paper, we take the first attempt to build a ubiquitous passive smoking detection system, which leverages the patterns smoking leaves on WiFi signals to identify the smoking activity even in the non-line-of-sight and through-wall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detection based motion acquisition method …

Introduction To Atomic Requirements, William L. Honig

Computer Science: Faculty Publications and Other Works

An introduction to requirements and the importance of making single atomic requirements statements. Atomic requirements have advantages and improve the requirements process, support requirement verification and validation, enable traceability, support testability of systems, and provide management advantages.

Why has there been so little emphasis on atomic requirements?

The Anisotropy Of Hexagonal Close-Packed And Liquid Interface Free Energy Using Molecular Dynamics Simulations Based On Modified Embedded-Atom Method, Ebrahim Asadi, Mohsen Asle Zaeem

Materials Science and Engineering Faculty Research & Creative Works

This work aims to comprehensively study the anisotropy of the hexagonal close-packed (HCP)-liquid interface free energy using molecular dynamics (MD) simulations based on the modified-embedded atom method (MEAM). As a case study, all the simulations are performed for Magnesium (Mg). The solid-liquid coexisting approach is used to accurately calculate the melting point and melting properties. Then, the capillary fluctuation method (CFM) is used to determine the HCP-liquid interface free energy (γ) and anisotropy parameters. In CFM, a continuous order parameter is employed to accurately locate the HCP-liquid interface location, and the HCP symmetry-adapted spherical harmonics are used to expand γ …

Least-Squares Mapping From Kinematic Data To Acoustic Synthesis Parameters For Rehabilitative Acoustic Learning, Xiangyu Zhou

Master's Theses (2009 -)

Thousands of people suffer from dysarthria resulting from neurological injury of the motor component of the motor-speech system, and need to rely on alternative methods to communicate in daily life, such as body language or text-to-speech [1] . However, there are currently very few effective rehabilitative therapies for helping these patients improve their speech. Because of this, research is needed to develop better rehabilitative therapies. One such area of research is the use of involuntary acoustic learning. The Speech and Swallowing lab at Marquette University has an Electromagnetic Articulography (EMA) system to collect kinematic data and a software system called …

Dual-Server Public-Key Encryption With Keyword Search For Secure Cloud Storage, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang

Research Collection School Of Computing and Information Systems

Searchable encryption is of increasing interest for protecting the data privacy in secure searchable cloud storage. In this paper, we investigate the security of a well-known cryptographic primitive, namely, public key encryption with keyword search (PEKS) which is very useful in many applications of cloud storage. Unfortunately, it has been shown that the traditional PEKS framework suffers from an inherent insecurity called inside keyword guessing attack (KGA) launched by the malicious server. To address this security vulnerability, we propose a new PEKS framework named dual-server PEKS (DS-PEKS). As another main contribution, we define a new variant of the smooth projective …

Tuning By Turning: Enabling Phased Array Signal Processing For Wifi With Inertial Sensors, Kun Qian, Chenshu Wu, Zheng Yang, Zimu Zhou, Xu Wang, Yunhao Liu

Research Collection School Of Computing and Information Systems

Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users’ natural rotation to formulate …

Improving Structure Mcmc For Bayesian Networks Through Markov Blanket Resampling, Chengwei Su, Mark E. Borsuk

Dartmouth Scholarship

Algorithms for inferring the structure of Bayesian networks from data have become an increasingly popular method for uncovering the direct and indirect influences among variables in complex systems. A Bayesian approach to structure learning uses posterior probabilities to quantify the strength with which the data and prior knowledge jointly support each possible graph feature. Existing Markov Chain Monte Carlo (MCMC) algorithms for estimating these posterior probabilities are slow in mixing and convergence, especially for large networks. We present a novel Markov blanket resampling (MBR) scheme that intermittently reconstructs the Markov blanket of nodes, thus allowing the sampler to more effectively …

Ensemble Forecasts: Probabilistic Seasonal Forecasts Based On A Model Ensemble, Hannah Aizenman, Michael D. Grossberg, Nir Y. Krakauer, Irina Gladkova

Publications and Research

Ensembles of general circulation model (GCM) integrations yield predictions for meteorological conditions in future months. Such predictions have implicit uncertainty resulting from model structure, parameter uncertainty, and fundamental randomness in the physical system. In this work, we build probabilistic models for long-term forecasts that include the GCM ensemble values as inputs but incorporate statistical correction of GCM biases and different treatments of uncertainty. Specifically, we present, and evaluate against observations, several versions of a probabilistic forecast for gridded air temperature 1 month ahead based on ensemble members of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 …

The Subject Librarian Newsletter, Engineering And Computer Science, Spring 2016, Ven Basco

Libraries' Newsletters

No abstract provided.

Pointing Analysis And Design Drivers For Low Earth Orbit Satellite Quantum Key Distribution, Jeremiah A. Specht

Theses and Dissertations

The world relies on encryption to perform critical and sensitive tasks every day. If quantum computing matures, the capability to decode keys and decrypt messages becomes possible. Quantum key distribution (QKD) is a method of distributing secure cryptographic keys which relies on the laws of quantum mechanics. Current implementations of QKD use fiber-based channels which limit the number of users and the distance between users. Satellite-based QKD using free-space channels is proposed as a feasible secure global communication solution. Since a free-space link does not use a waveguide, pointing a transmitter to receiver is required to ensure signal arrival. In …

Key Detection Rate Modeling And Analysis For Satellite-Based Quantum Key Distribution, Jonathan C. Denton

Theses and Dissertations

A satellite QKD model was developed and validated, that allows a user to determine the optimum wavelength for use in a satellite-based QKD link considering the location of ground sites, selected orbit and hardware performance. This thesis explains how the model was developed, validated and presents results from a simulated year-long study of satellite-based quantum key distribution. It was found that diffractive losses and atmospheric losses define a fundamental trade space that drives both orbit and wavelength selection. The optimal orbit is one which generates the highest detection rates while providing equal pass elevation angles and durations to multiple ground …

Sefo: A Package For Generating Probabilistic Forecasts From Nmme Predictive Ensembles, Nir Krakauer

Publications and Research

Long-range weather forecasts based on output from ensembles of computer simulations are attracting increasing interest. A variety of methods have been proposed to convert the ensemble outputs to calibrated probabilistic forecasts. The package presented here (SeFo, for Seasonal Forecasting) implements a number of methods for producing forecasts of monthly surface air temperature anomalies up to 9 months in advance using output from the North American Multi-Model Ensemble (NMME). The package contains modules for downloading and reading past observations and ensemble output; producing forecast probability distributions; and verifying and calibrating a user-determined subset of methods using arbitrary past periods. By changing …