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关键词： Robotics   Design   Engineering   Load   Sliding friction   Gravity   Computer science   Yield stress   Robots   Climbing   Wheels   Bridges   Designers  

关键词： Robotics   Computer science   Artificial intelligence  

摘要： Perception and understanding are key for fully autonomous robotics. Topics such as scene recognition and context are required for an autonomous system to determine what would be an appropriate response to the information received by its sensors. Thus, this thesis has two primary objectives. The first objective is an exploration of collaborative localization techniques for heterogeneous robot teams. The resulting contribution of is a scheme for relative localization of a heterogeneous team of low-cost mobile robots. An error-state, complementary Kalman Filter was developed to fuse analytically-derived uncertainty of stereoscopic pose measurements of an aerial robot, made by a ground robot, with the inertial/visual proprioceptive measurements of both robots. Additionally, this approach is expanded to include and account for additional ground vehicles. The second objective is the development of a near real-time, multi-stage classifier for threat detection. It identifies people and handguns in images, and then further assesses the level of threat that a person poses based on their body posture. Additionally, the effectiveness of the approach was evaluated for the influence of partial skeletons including a study regarding whether a Common Objects in COntext (COCO) skeleton or a Max Planck Institute for Informatics (MPII) skeleton model is better suited for the classification stage.

关键词： Computer science   Bioinformatics   Artificial intelligence   Neurosciences   Biomedical engineering   Gerontology   Health care management  

摘要： Alzheimer's Disease (AD) is a serious public health issue that results in significant social and financial burdens on the individuals and communities impacted. In order to tackle this public health crisis it is critical that the clinical and computational research communities collaborate to identify possible causes of this progressive memory disease. Close collaboration between these two communities has the potential to result in promising therapeutic treatments for AD and other health conditions. This dissertation presents a collection of algorithms and associated derivations designed to predict the progression of AD using multi-task and structured regularization techniques, clustering membership by way of nonnegative matrix factorization, and COVID-19 clinical outcome prediction using multi-instance learning methods. This work presents novel algorithms for handling multimodal and longitudinal data and details approaches for multitask and multi-instance learning techniques that can be applied in other fields. Extensive discussions on algorithm predictive performance, interpretability, and implementation are provided for each method and are designed to serve as a framework for future research.

关键词： Radiometry   Power supply   Meteorological satellites   Algorithms   Radiometers   Sensors   Antennas   Moisture content   Parameter estimation   Aerospace engineering   Computer science   Engineering  

摘要： Satellite radiometers are used to remotely measure properties of the Earth's surface. Radiometers enable wide spatial coverage and daily temporal coverage. Radiometer measurements are used in a wide array of applications, including freeze/thaw states inference, vegetation index calculations, rainfall estimation, and soil moisture estimation. Resolution enhancement of these radiometer measurements enable finer details to be resolved and improve our understanding of Earth. The Soil Moisture Active Passive (SMAP) radiometer was launched in April 2014 with a goal to produce high resolution soil moisture estimates. However, due to hardware failure of the radar channels, prepared algorithms could no longer be used. Current algorithms utilize a narrow spatial and temporal overlap between the SMAP radiometer and the SENTINEL-1 radar to produce high resolution soil moisture estimates that are spatially and temporally limited. This thesis explores the use of resolution enhancing algorithms to produce high resolution soil moisture estimates without the spatial coverage limitations caused by using multiple sensors. Two main approaches are considered: calculating the iterative update in brightness temperature and calculating the update in soil moisture. The best performing algorithm is the Soil Moisture Image Reconstruction (SMIR) algorithm that is a variation of the Radiometer form of the Scatterometer Image Reconstruction (rSIR) algorithm that has been adapted to operate in parameter space. This algorithm utilizes a novel soil moisture measurement response function (SMRF) in the reconstruction. It matches or exceeds the performance of other algorithms and allows for wide spatial coverage.

关键词： Approximation algorithms   Artificial intelligence   Computer science   Dragon boat festivals   Machine learning   Neural networks  

摘要： We investigate approximating the Dragon Boat Partition problem, a practical real-world variant of the Partition problem. A team of dragon boat participants must be partitioned with an approximately balanced arrangement with a preferable weight difference of 0. We present two approaches that capture the participant characteristics. The first approach takes a heuristic route. The second approach applies Deep Convolutional Neural Networks to the problem, with two versions. In our 10,000 episodes per experiment, our heuristic implementation had an average episode runtime of 1.84ms, an average of 7.39 steps per episode,perfect left-right approximation rate of 98.53%, perfect front-back approximation rate of89.16%, and a perfect combined approximation rate of 90.15%. Whereas our best deep learning model has an average episode runtime of 1.23ms, an average of 4.65 steps perepisode, perfect left-right approximate rate of 98.00%, perfect front-back approximation rate of 95.13%, and a perfect combined approximation rate of 94.28%.

关键词： Circuits   Maps   Quantum computing   Motivation   Success   Protocol   Mechanics   Optimization   Computer science   Electrical engineering   Engineering   Physics   Quantum physics  

摘要： This licentiate thesis will introduce the background of the appended paper. The scope of this thesis is to find a Gaussian conversion protocol that allows one to prepare cubic phase states, which are resourceful states for achieving universality in continuous-variable quantum computation. The starting point of the protocol is a trisqueezed state, which has been realized experimentally with superconducting microwave circuits. With the help of machine learning, large fidelity can be achieved in these protocols. Furthermore, these protocols could also be helpful for other applications in continuous-variable systems as well.

关键词： Chemical engineering   Mathematics   Computer science  

摘要： This dissertation studies graph-structured nonlinear programs, nonlinear optimization problems whose algebraic structures are induced by graphs. A graph-structured nonlinear program is a generalized abstraction for dynamic optimization, stochastic optimization, optimization with embedded partial differential equations, and network system optimization. This generalized abstraction allows studying properties these problems share in common and creating tailored solution algorithms. In the first part, we study the fundamental property of solutions of graph-structured nonlinear programs. Specifically, we study how strongly a nodal parametric perturbation influences the nodal solution of another node. Building upon existing nonlinear programming sensitivity theory, we prove that solution sensitivity decays exponentially with the distance from the perturbation point on the graph. Remarkably, this result, which we call the exponential decay of sensitivity, holds under fairly standard assumptions used in classical nonlinear programming sensitivity theory: strong second-order sufficiency conditions and linear independence constraint qualifications. In addition, we establish uniform regularity conditions, the sufficient conditions under which the sensitivity decay rate remains uniformly bounded (independent of the size of the problem). Such uniformity allows studying sensitivity behaviors of problems with arbitrarily large graphs. The exponential decay of sensitivity enables the creation of a specialized decomposition method for graph-structured nonlinear programs, the overlapping Schwarz method. In the second part, we study the convergence properties and implementation of this method. The overlapping Schwarz method has been traditionally used for the solution of sparse linear systems, which typically arise in discretized partial differential equations. In this work, we generalize this algorithm for graph-structured nonlinear programs. The proposed overlapping Schwarz algorith

关键词： Databases   Bars   Heuristic algorithms   Computer science   Syntactics   Optimization   Licenses   Dynamic database   incomplete database   pairwise comparison   skyline queries  

摘要： Skyline queries have been widely used as an effective query tool in many contemporary database applications. The main concept of skyline queries relies on retrieving the non-dominated tuples in the database which are known skylines. In most database applications, the contents of the databases are dynamic due to the continuous changes made towards the database. Typically, the changes in the contents of the database occur through data manipulation operations (INSERT and/or UPDATE). Performing these operations on the database results in invalidating the most recent skylines before changes are made on the database. Furthermore, the presence of incomplete data in databases becomes frequent phenomena in recent database applications. Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic. Reapplying skyline technique on the entire updated incomplete database to determine the new skylines is unwise due to the exhaustive pairwise comparisons. Thus, this paper proposes an approach, named Incomplete Dynamic Skyline Algorithm (IDSA) which attempts to determine the skylines on dynamic and incomplete databases. Two optimization techniques have been incorporated in IDSA, namely: pruning and selecting superior local skylines. The pruning process attempts to exploit the derived skylines before the INSERT/UPDATE operation made on the database to identify the new skylines. Moreover, selecting superior local skylines process assists in further eliminating the remaining non-skylines from further processing. These two optimization techniques lead to a large reduction in the number of domination tests due to avoiding re-computing of skylines over the entire updated database to derive the new skylines. Extensive experiments have been accomplished on both real and synthetic datasets, and the results demonstrate that IDSA outperforms the existing solutions in

关键词： Ontologies   Text categorization   Semantic Web   Semantics   Computer science   Tools   Libraries   Clustering   recommendation system   semantic web   ontology   text categorization   text mining   unsupervised learning  

摘要： Semantic Web is considered as the backbone of web 3.0 and ontologies are an integral part of the Semantic Web. Though an increase of ontologies in different domains is reported due to various benefits which include data heterogeneity, automated information analysis, and reusability, however, finding an appropriate ontology according to user requirement remains cumbersome task due to time and efforts required, context-awareness, and computational complexity. To overcome these issues, an ontology recommendation framework is proposed. The Proposed framework employs text categorization and unsupervised learning techniques. The benefits of the proposed framework are twofold: 1) ontology organization according to the opinion of domain experts and 2) ontology recommendation with respect to user requirement. Moreover, an evaluation model is also proposed to assess the effectiveness of the proposed framework in terms of ontologies organization and recommendation. The main consequences of the proposed framework are 1) ontologies of a corpus can be organized effectively, 2) no effort and time are required to select an appropriate ontology, 3) computational complexity is only limited to the use of unsupervised learning techniques, and 4) due to no requirement of context awareness, the proposed framework can be effective for any corpus or online libraries of ontologies.

关键词： Brain research   Magnetic fields   Signal processing   Neurosciences   Natural language processing   Electroencephalography   Computer science   Artificial intelligence  

摘要： The field of machine learning is deeply intertwined with the dynamics that determine technical progress in our lives. It benefits invaluably from the increasing amount of available data and the access to computing power. Simultaneously, it drives those changes by continuously improving methodologies and thus extending the possible applications in our everyday lives. In order to keep up with those changes, machine learning models need to tackle the obstacles that arise in dynamic settings. This thesis contributes solutions to those challenges in the fields of computational neuroscience and natural language processing. The first contribution researches possible challenges when moving brain-computer interfaces out of the lab and into the real world. We have recorded data from 16 participants conducting a motor imagery task while handling secondary distraction tasks that simulate everyday life situations. We investigate the artifacts that contaminate the data which makes it difficult to successfully classify within the standard pipeline. We propose two approaches that tackle those difficulties and significantly improved classification results. In the second contribution, we propose a new version of Source Power Co-Modulation: Fourier-SPoC (f-SPoC) extracts brain components maximally correlating with a regular target function. We apply f-SPoC to the magnetoencephalogram (MEG) from a passive beat listening task to find neural correlates that show rhythmic entrainment with the regular beat. The resulting components show very regular patterns of peaks and troughs in the spectrogram on the group average and even on some individuals. The third contribution provides two new methods: Word2Vec with Structure Constraint (W2V&Constr) and Word2Vec with Structure Prediction (W2V&Pred) with which we can learn dynamic word embeddings of high quality based on the underlying structure in the dataset (W2V&Constr) and are even able to predict the structure of the dataset (W2V&Pred). We ap