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Full-Text Articles in Engineering

Administrative Law In The Automated State, Cary Coglianese Apr 2021

Administrative Law In The Automated State, Cary Coglianese

Faculty Scholarship at Penn Law

In the future, administrative agencies will rely increasingly on digital automation powered by machine learning algorithms. Can U.S. administrative law accommodate such a future? Not only might a highly automated state readily meet longstanding administrative law principles, but the responsible use of machine learning algorithms might perform even better than the status quo in terms of fulfilling administrative law’s core values of expert decision-making and democratic accountability. Algorithmic governance clearly promises more accurate, data-driven decisions. Moreover, due to their mathematical properties, algorithms might well prove to be more faithful agents of democratic institutions. Yet even if an automated ...


Towards A Framework For Certification Of Reliable Autonomous Systems, Michael Fisher, Viviana Mascardi, Kristin Yvonne Rozier, Bernd-Holger Schlingloff, Michael Winikoff, Neil Yorke-Smith Apr 2021

Towards A Framework For Certification Of Reliable Autonomous Systems, Michael Fisher, Viviana Mascardi, Kristin Yvonne Rozier, Bernd-Holger Schlingloff, Michael Winikoff, Neil Yorke-Smith

Aerospace Engineering Publications

A computational system is called autonomous if it is able to make its own decisions, or take its own actions, without human supervision or control. The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification ...


Incremental Design-Space Model Checking Via Reusable Reachable State Approximations, Rohit Dureja, Kristin Yvonne Rozier Jan 2021

Incremental Design-Space Model Checking Via Reusable Reachable State Approximations, Rohit Dureja, Kristin Yvonne Rozier

Aerospace Engineering Publications

The design of safety-critical systems often requires design space exploration: comparing several system models that differ in terms of design choices, capabilities, and implementations. Model checking can compare different models in such a set, however, it is continuously challenged by the state space explosion problem. Therefore, learning and reusing information from solving related models becomes very important for future checking efforts. For example, reusing variable ordering in BDD-based model checking leads to substantial performance improvement. In this paper, we present a SAT-based algorithm for checking a set of models. Our algorithm, FuseIC3, extends IC3 to minimize time spent in exploring ...


Scalable Online Vetting Of Android Apps For Measuring Declared Sdk Versions And Their Consistency With Api Calls, Daoyuan Wu, Debin Gao, David Lo Jan 2021

Scalable Online Vetting Of Android Apps For Measuring Declared Sdk Versions And Their Consistency With Api Calls, Daoyuan Wu, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Android has been the most popular smartphone system with multiple platform versions active in the market. To manage the application’s compatibility with one or more platform versions, Android allows apps to declare the supported platform SDK versions in their manifest files. In this paper, we conduct a systematic study of this modern software mechanism. Our objective is to measure the current practice of declared SDK versions (which we term as DSDK versions afterwards) in real apps, and the (in)consistency between DSDK versions and their host apps’ API calls. To successfully analyze a modern dataset of 22,687 popular ...


A Deep Machine Learning Approach For Predicting Freeway Work Zone Delay Using Big Data, Abdullah Shabarek Dec 2020

A Deep Machine Learning Approach For Predicting Freeway Work Zone Delay Using Big Data, Abdullah Shabarek

Dissertations

The introduction of deep learning and big data analytics may significantly elevate the performance of traffic speed prediction. Work zones become one of the most critical factors causing congestion impact, which reduces the mobility as well as traffic safety. A comprehensive literature review on existing work zone delay prediction models (i.e., parametric, simulation and non-parametric models) is conducted in this research. The research shows the limitations of each model. Moreover, most previous modeling approaches did not consider user delay for connected freeways when predicting traffic speed under work zone conditions. This research proposes Deep Artificial Neural Network (Deep ANN ...


Deep Neural Network For Complex Open-Water Wetland Mapping Using High-Resolution Worldview-3 And Airborne Lidar Data, Vitor S. Martins, Amy L. Kaleita, Brian K. Gelder, Gustavo W. Nagel, Daniel A. Maciel Dec 2020

Deep Neural Network For Complex Open-Water Wetland Mapping Using High-Resolution Worldview-3 And Airborne Lidar Data, Vitor S. Martins, Amy L. Kaleita, Brian K. Gelder, Gustavo W. Nagel, Daniel A. Maciel

Agricultural and Biosystems Engineering Publications

Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several ...


Machine Learning Augmentation Micro-Sensors For Smart Device Applications, Mohammad H. Hasan Nov 2020

Machine Learning Augmentation Micro-Sensors For Smart Device Applications, Mohammad H. Hasan

Mechanical (and Materials) Engineering -- Dissertations, Theses, and Student Research

Novel smart technologies such as wearable devices and unconventional robotics have been enabled by advancements in semiconductor technologies, which have miniaturized the sizes of transistors and sensors. These technologies promise great improvements to public health. However, current computational paradigms are ill-suited for use in novel smart technologies as they fail to meet their strict power and size requirements. In this dissertation, we present two bio-inspired colocalized sensing-and-computing schemes performed at the sensor level: continuous-time recurrent neural networks (CTRNNs) and reservoir computers (RCs). These schemes arise from the nonlinear dynamics of micro-electro-mechanical systems (MEMS), which facilitates computing, and the inherent ability ...


Extending The Functional Subnetwork Approach To A Generalized Linear Integrate-And-Fire Neuron Model, Nicholas Szczecinski, Roger Quinn, Alexander J. Hunt Nov 2020

Extending The Functional Subnetwork Approach To A Generalized Linear Integrate-And-Fire Neuron Model, Nicholas Szczecinski, Roger Quinn, Alexander J. Hunt

Mechanical and Materials Engineering Faculty Publications and Presentations

Engineering neural networks to perform specific tasks often represents a monumental challenge in determining network architecture and parameter values. In this work, we extend our previously-developed method for tuning networks of non-spiking neurons, the “Functional subnetwork approach” (FSA), to the tuning of networks composed of spiking neurons. This extension enables the direct assembly and tuning of networks of spiking neurons and synapses based on the network’s intended function, without the use of global optimization ormachine learning. To extend the FSA, we show that the dynamics of a generalized linear integrate and fire (GLIF) neuronmodel have fundamental similarities to those ...


Communicating Uncertain Information From Deep Learning Models In Human Machine Teams, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank, Luke Andrews, Cihan H. Dagli Oct 2020

Communicating Uncertain Information From Deep Learning Models In Human Machine Teams, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank, Luke Andrews, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The role of human-machine teams in society is increasing, as big data and computing power explode. One popular approach to AI is deep learning, which is useful for classification, feature identification, and predictive modeling. However, deep learning models often suffer from inadequate transparency and poor explainability. One aspect of human systems integration is the design of interfaces that support human decision-making. AI models have multiple types of uncertainty embedded, which may be difficult for users to understand. Humans that use these tools need to understand how much they should trust the AI. This study evaluates one simple approach for communicating ...


Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools, Murat Kuzlu, Umit Cali, Vinayak Sharma, Özgür Güler Oct 2020

Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools, Murat Kuzlu, Umit Cali, Vinayak Sharma, Özgür Güler

Engineering Technology Faculty Publications

Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a ‘‘black-box’’ due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using ...


Attention-Based Road Registration For Gps-Denied Uas Navigation, Teng Wang, Ye Zhao, Jiawei Wang, Arun K. Somani, Changyin Sun Sep 2020

Attention-Based Road Registration For Gps-Denied Uas Navigation, Teng Wang, Ye Zhao, Jiawei Wang, Arun K. Somani, Changyin Sun

Electrical and Computer Engineering Publications

Matching and registration between aerial images and prestored road landmarks are critical techniques to enhance unmanned aerial system (UAS) navigation in the global positioning system (GPS)-denied urban environments. Current registration processes typically consist of two separate stages of road extraction and road registration. These two-stage registration approaches are time-consuming and less robust to noise. To that end, in this article, we, for the first time, investigate the problem of end-to-end Aerial-Road registration. Using deep learning, we develop a novel attention-based neural network architecture for Aerial-Road registration. In this model, we construct two-branch neural networks with shared weights to map ...


Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet Sep 2020

Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the "right" requests to travel together in the "right" available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible (with respect to the available delay for customers) combinations of requests as ...


Machine Learning Model Based On Transthoracic Bioimpedance And Heart Rate Variability For Lung Fluid Accumulation Detection: Prospective Clinical Study, Natasa Reljin, Hugo F. Posada-Quintero, Caitlin Eaton-Robb, Sophia Binici, Emily Ensom, Eric Y. Ding, Anna Hayes, Jarno Riistama, Chad E. Darling, David D. Mcmanus, Ki H. Chon Aug 2020

Machine Learning Model Based On Transthoracic Bioimpedance And Heart Rate Variability For Lung Fluid Accumulation Detection: Prospective Clinical Study, Natasa Reljin, Hugo F. Posada-Quintero, Caitlin Eaton-Robb, Sophia Binici, Emily Ensom, Eric Y. Ding, Anna Hayes, Jarno Riistama, Chad E. Darling, David D. Mcmanus, Ki H. Chon

Open Access Publications by UMMS Authors

BACKGROUND: Accumulation of excess body fluid and autonomic dysregulation are clinically important characteristics of acute decompensated heart failure. We hypothesized that transthoracic bioimpedance, a noninvasive, simple method for measuring fluid retention in lungs, and heart rate variability, an assessment of autonomic function, can be used for detection of fluid accumulation in patients with acute decompensated heart failure.

OBJECTIVE: We aimed to evaluate the performance of transthoracic bioimpedance and heart rate variability parameters obtained using a fluid accumulation vest with carbon black-polydimethylsiloxane dry electrodes in a prospective clinical study (System for Heart Failure Identification Using an External Lung Fluid Device; SHIELD ...


Embedding Online Runtime Verification For Fault Disambiguation On Robonaut2, Brian Kempa, Pei Zhang, Phillip H. Jones, Joseph Zambreno, Kristin Yvonne Rozier Aug 2020

Embedding Online Runtime Verification For Fault Disambiguation On Robonaut2, Brian Kempa, Pei Zhang, Phillip H. Jones, Joseph Zambreno, Kristin Yvonne Rozier

Aerospace Engineering Conference Papers, Presentations and Posters

Robonaut2 (R2) is a humanoid robot onboard the International Space Station (ISS), performing specialized tasks in collaboration with astronauts. After deployment, R2 developed an unexpected emergent behavior. R2’s inability to distinguish between knee-joint faults (e.g., due to sensor drift versus violated environmental assumptions) began triggering safety-preserving freezes-in-place in the confined space of the ISS, preventing further motion until a ground-control operator determines the root-cause and initiates proper corrective action. Runtime verification (RV) algorithms can efficiently disambiguate the temporal signatures of different faults in real-time. However, no previous RV engine can operate within the limited available resources and specialized ...


Challenges And Opportunities In Machine-Augmented Plant Stress Phenotyping, Arti Singh, Sarah Jones, Baskar Ganapathysubramanian, Soumik Sarkar, Daren S. Mueller, Kulbir Sandhu, Koushik Nagasubramanian Aug 2020

Challenges And Opportunities In Machine-Augmented Plant Stress Phenotyping, Arti Singh, Sarah Jones, Baskar Ganapathysubramanian, Soumik Sarkar, Daren S. Mueller, Kulbir Sandhu, Koushik Nagasubramanian

Mechanical Engineering Publications

Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of plant stress phenotyping at multiple scales across different types of stresses, program goals, and environments.


Semantic Segmentation Of Smartphone Wound Images: Comparative Analysis Of Ahrf And Cnn-Based Approaches, Ameya Wagh, Shubham Jain, Apratim Mukherjee, Emmanuel Agu, Peder Pedersen, Diane Strong, Bengisu Tulu, Clifford Lindsay, Ziyang Liu Aug 2020

Semantic Segmentation Of Smartphone Wound Images: Comparative Analysis Of Ahrf And Cnn-Based Approaches, Ameya Wagh, Shubham Jain, Apratim Mukherjee, Emmanuel Agu, Peder Pedersen, Diane Strong, Bengisu Tulu, Clifford Lindsay, Ziyang Liu

Radiology Publications

Smartphone wound image analysis has recently emerged as a viable way to assess healing progress and provide actionable feedback to patients and caregivers between hospital appointments. Segmentation is a key image analysis step, after which attributes of the wound segment (e.g. wound area and tissue composition) can be analyzed. The Associated Hierarchical Random Field (AHRF) formulates the image segmentation problem as a graph optimization problem. Handcrafted features are extracted, which are then classified using machine learning classifiers. More recently deep learning approaches have emerged and demonstrated superior performance for a wide range of image analysis tasks. FCN, U-Net and ...


A Modeling Framework For Urban Growth Prediction Using Remote Sensing And Video Prediction Technologies: A Time-Dependent Convolutional Encoder-Decoder Architecture, Ahmed Hassan Jaad Aug 2020

A Modeling Framework For Urban Growth Prediction Using Remote Sensing And Video Prediction Technologies: A Time-Dependent Convolutional Encoder-Decoder Architecture, Ahmed Hassan Jaad

Civil and Environmental Engineering Theses and Dissertations

Studying the growth pattern of cities/urban areas has received considerable attention during the past few decades. The goal is to identify directions and locations of potential growth, assess infrastructure and public service requirements, and ensure the integration of the new developments with the existing city structure. This dissertation presents a novel model for urban growth prediction using a novel machine learning model. The model treats successive historical satellite images of the urban area under consideration as a video for which future frames are predicted. A time-dependent convolutional encoder-decoder architecture is adopted. The model considers as an input a satellite ...


Application Of Artificial Intelligence And Geographic Information System For Developing Automated Walkability Score, Md Mehedi Hasan Aug 2020

Application Of Artificial Intelligence And Geographic Information System For Developing Automated Walkability Score, Md Mehedi Hasan

Dissertations

Walking is considered as one of the major modes of active transportation, which contributes to the livability of cities. It is highly important to ensure walk friendly sidewalks to promote human physical activities along roads. Over the last two decades, different walk scores were estimated in respect to walkability measures by applying different methods and approaches. However, in the era of big data and machine learning revolution, there is still a gap to measure the composite walkability score in an automated way by applying and quantifying the activityfriendliness of walkable streets. In this study, a street-level automated walkability score was ...


Secure Mobile Computing By Using Convolutional And Capsule Deep Neural Networks, Rui Ning Aug 2020

Secure Mobile Computing By Using Convolutional And Capsule Deep Neural Networks, Rui Ning

Electrical & Computer Engineering Theses & Disssertations

Mobile devices are becoming smarter to satisfy modern user's increasing needs better, which is achieved by equipping divers of sensors and integrating the most cutting-edge Deep Learning (DL) techniques. As a sophisticated system, it is often vulnerable to multiple attacks (side-channel attacks, neural backdoor, etc.). This dissertation proposes solutions to maintain the cyber-hygiene of the DL-Based smartphone system by exploring possible vulnerabilities and developing countermeasures.

First, I actively explore possible vulnerabilities on the DL-Based smartphone system to develop proactive defense mechanisms. I discover a new side-channel attack on smartphones using the unrestricted magnetic sensor data. I demonstrate that attackers ...


Deep Learning For Remote Sensing Image Processing, Yan Lu Aug 2020

Deep Learning For Remote Sensing Image Processing, Yan Lu

Computational Modeling and Simulation Engineering Theses & Dissertations

Remote sensing images have many applications such as ground object detection, environmental change monitoring, urban growth monitoring and natural disaster damage assessment. As of 2019, there were roughly 700 satellites listing “earth observation” as their primary application. Both spatial and temporal resolutions of satellite images have improved consistently in recent years and provided opportunities in resolving fine details on the Earth's surface. In the past decade, deep learning techniques have revolutionized many applications in the field of computer vision but have not fully been explored in remote sensing image processing. In this dissertation, several state-of-the-art deep learning models have ...


Leaf Angle Extractor: A High‐Throughput Image Processing Framework For Leaf Angle Measurements In Maize And Sorghum, Sunil K. Kenchanmane Raju, Miles Adkins, Alex Enersen, Daniel Santana De Carvalho, Anthony J. Studer, Baskar Ganapathysubramanian, Patrick S. Schnable, James C. Schnable Aug 2020

Leaf Angle Extractor: A High‐Throughput Image Processing Framework For Leaf Angle Measurements In Maize And Sorghum, Sunil K. Kenchanmane Raju, Miles Adkins, Alex Enersen, Daniel Santana De Carvalho, Anthony J. Studer, Baskar Ganapathysubramanian, Patrick S. Schnable, James C. Schnable

Mechanical Engineering Publications

PREMISE: Maize yields have significantly increased over the past half-century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment.

METHODS: High-throughput ...


An Investigation Into Multi-View Error Correcting Output Code Classifiers Applied To Organ Tissue Classification, Daniel Alvarez Aug 2020

An Investigation Into Multi-View Error Correcting Output Code Classifiers Applied To Organ Tissue Classification, Daniel Alvarez

UNLV Theses, Dissertations, Professional Papers, and Capstones

Large amounts of data is being generated constantly each day, so much data that it is difficult to find patterns in order to predict outcomes and make decisions for both humans and machines alike. It would be useful if this data could be simplified using machine learning techniques. For example, biological cell identity is dependent on many factors tied to genetic processes. Such factors include proteins, gene transcription, and gene methylation. Each of these factors are highly complex mechanism with immense amounts of data. Simplifying these can then be helpful in finding patterns in them. Error-Correcting Output Codes (ECOC) does ...


Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat Jul 2020

Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat

Electronic Thesis and Dissertation Repository

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models ...


Human Supremacy As Posthuman Risk, Daniel Estrada Jul 2020

Human Supremacy As Posthuman Risk, Daniel Estrada

The Journal of Sociotechnical Critique

Human supremacy is the widely held view that human interests ought to be privileged over other interests as a matter of ethics and public policy. Posthumanism is the historical situation characterized by a critical reevaluation of anthropocentrist theory and practice. This paper draws on animal studies, critical posthumanism, and the critique of ideal theory in Charles Mills and Serene Khader to address the appeal to human supremacist rhetoric in AI ethics and policy discussions, particularly in the work of Joanna Bryson. This analysis identifies a specific risk posed by human supremacist policy in a posthuman context, namely the classification of ...


Nonlinear Dimensionality Reduction For The Thermodynamics Of Small Clusters Of Particles, Aditya Dendukuri Jul 2020

Nonlinear Dimensionality Reduction For The Thermodynamics Of Small Clusters Of Particles, Aditya Dendukuri

Theses and Dissertations

This work employs tools and methods from computer science to study clusters comprising a small number N of interacting particles, which are of interest in science, engineering, and nanotechnology. Specifically, the thermodynamics of such clusters is studied using techniques from spectral graph theory (SGT) and machine learning (ML). SGT is used to define the structure of the clusters and ML is used on ensembles of cluster configurations to detect state variables that can be used to model the thermodynamic properties of the system. While the most fundamental description of a cluster is in 3N dimensions, i.e., the Cartesian coordinates ...


Adaptive Large Neighborhood Search For Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu Jul 2020

Adaptive Large Neighborhood Search For Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu

Research Collection School Of Computing and Information Systems

Cross-docking is considered as a method to manage and control the inventory flow, which is essential in the context of supply chain management. This paper studies the integration of the vehicle routing problem with cross-docking, namely VRPCD which has been extensively studied due to its ability to reducethe overall costs occurring in a supply chain network. Given a fleet of homogeneous vehicles for delivering a single type of product from suppliers to customers through a cross-dock facility, the objective of VRPCD is to determine the number of vehicles used and the corresponding vehicle routes, such that the vehicleoperational and transportation ...


Closing The Data-Decisions Loop: Deploying Artificial Intelligence For Dynamic Resource Management, Pradeep Varakantham Jun 2020

Closing The Data-Decisions Loop: Deploying Artificial Intelligence For Dynamic Resource Management, Pradeep Varakantham

Asian Management Insights

Improving predictions and allocations to determine the optimal matching of demand and supply in a dynamic, uncertain future.


Monte Carlo Tree Search Applied To A Modified Pursuit/Evasion Scotland Yard Game With Rendezvous Spaceflight Operation Applications, Joshua A. Daughtery Jun 2020

Monte Carlo Tree Search Applied To A Modified Pursuit/Evasion Scotland Yard Game With Rendezvous Spaceflight Operation Applications, Joshua A. Daughtery

Theses and Dissertations

This thesis takes the Scotland Yard board game and modifies its rules to mimic important aspects of space in order to facilitate the creation of artificial intelligence for space asset pursuit/evasion scenarios. Space has become a physical warfighting domain. To combat threats, an understanding of the tactics, techniques, and procedures must be captured and studied. Games and simulations are effective tools to capture data lacking historical context. Artificial intelligence and machine learning models can use simulations to develop proper defensive and offensive tactics, techniques, and procedures capable of protecting systems against potential threats. Monte Carlo Tree Search is a ...


Efficient Hardware Implementations Of Bio-Inspired Networks, Anakha Vasanthakumaribabu May 2020

Efficient Hardware Implementations Of Bio-Inspired Networks, Anakha Vasanthakumaribabu

Dissertations

The human brain, with its massive computational capability and power efficiency in small form factor, continues to inspire the ultimate goal of building machines that can perform tasks without being explicitly programmed. In an effort to mimic the natural information processing paradigms observed in the brain, several neural network generations have been proposed over the years. Among the neural networks inspired by biology, second-generation Artificial or Deep Neural Networks (ANNs/DNNs) use memoryless neuron models and have shown unprecedented success surpassing humans in a wide variety of tasks. Unlike ANNs, third-generation Spiking Neural Networks (SNNs) closely mimic biological neurons by ...


Model-Based Deep Siamese Autoencoder For Clustering Single Cell Rna-Seq Data, Zixia Meng May 2020

Model-Based Deep Siamese Autoencoder For Clustering Single Cell Rna-Seq Data, Zixia Meng

Theses

In the biological field, the smallest unit of organisms in most biological systems is the single cell, and the classification of cells is an everlasting problem. A central task for analysis of single-cell RNA-seq data is to identify and characterize novel cell types. Currently, there are several classical methods, such as K-means algorithm, spectral clustering, and Gaussian Mixture Models (GMMs), which are widely used to cluster the cells. Furthermore, typical dimensional reduction methods such as PCA, t-SNE, and ZIDA have been introduced to overcome “the curse of dimensionality”. A more recent method scDeepCluster has demonstrated improved and promising performances in ...