Index Catalog // ScholarsArchive@OSU

Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

Creator:: Senewiratne, Yomal K.
Abstract:: We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of...
Resource Type:: Masters Thesis

Enhancing macrocell downlink performance through femtocell user cooperation

Creator:: Zaid, Adem Mabruk
Abstract:: This thesis studies cooperative techniques that rely on femtocell user diversity to improve the downlink communication quality of macrocell users. We analytically analyze and evaluate the achievable performance of these techniques in the downlink of Rayleigh fading channels. We provide an approximation of both the bit-error rate (BER) and the...
Resource Type:: Masters Thesis

Enhancing Search-Based Techniques with Information Control Dependencies

Creator:: Aburas, Ali
Abstract:: Software testing is a very important task during software development and it can be used to improve the quality and reliability of the software system. One potential way to reduce the cost and increase the efficiency of software testing is to generate test data automatically. Search-based approaches successfully generate unit...
Resource Type:: Dissertation

Agile Bipedal Maneuvers Optimized then Learned from Single Rigid-Body Model Dynamics

Creator:: Batke, Ryan
Abstract:: As bipedal robots move ever closer to being integrated into all manner of real world envi-ronments there is a necessity to push their dynamic capabilities to meet or exceed those of humans and animals. Advancements must be made to address ordinary challenges that arise everyday in the same environments that...
Resource Type:: Masters Thesis

Network Coding, Random Matrices, and Their Applications to Communication Systems

Creator:: Nguyen-Huu, Duong
Abstract:: In this work, we study network coding technique, its relation to random matrices, and their applications to communication systems. The dissertation consists of three main contributions. First, we propose efficient algorithms for data synchronization via a broadcast channel using random network coding. Second, we study the resiliency of network coding...
Resource Type:: Dissertation

Weakly Supervised Learning for Activity Recognition from Time Series Data

Creator:: Guan, Xinze
Abstract:: The thesis focuses on activity recognition from sensor data, which has spurred a great deal of interest due to its impact on health care and security. Previous work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort...
Resource Type:: Dissertation

Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Creator:: Khorram, Saeed
Abstract:: In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
Resource Type:: Masters Thesis

Interactive player tracking for videos in American football

Creator:: Bawaskar, Amit
Abstract:: This thesis presents an interactive software tool for tracking a moving object in a video. In particular, we focus on the problem of tracking a player in American football videos. Object tracking is one of the fundamental problems in computer vision. It is one of the most important components in...
Resource Type:: Masters Thesis

Predicting activity type from accelerometer data

Creator:: Zheng, Yonglei
Abstract:: The study of physical activity is important in improving people’s health as it can help people understand the relationship between physical activity and health. Accelerometers, due to its small size, low cost, convenience and its ability to provide objective information about the frequency, intensity, and duration of physical activity, has...
Resource Type:: Masters Thesis

Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces

Creator:: Raghavan, Aswin
Abstract:: Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research...
Resource Type:: Dissertation
Full Text:: encouragement to par- ticiapte in the International Planning Competition (2011) directly led to the realisation

ScholarsArchive@OSU

Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

Enhancing macrocell downlink performance through femtocell user cooperation

Enhancing Search-Based Techniques with Information Control Dependencies

Agile Bipedal Maneuvers Optimized then Learned from Single Rigid-Body Model Dynamics

Network Coding, Random Matrices, and Their Applications to Communication Systems

Weakly Supervised Learning for Activity Recognition from Time Series Data

Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Interactive player tracking for videos in American football

Predicting activity type from accelerometer data

Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces

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