343,113 research outputs found
Doctor of Philosophy
dissertationIn the era of big data, many applications generate continuous online data from distributed locations, scattering devices, etc. Examples include data from social media, financial services, and sensor networks, etc. Meanwhile, large volumes of data can be archived or stored offline in distributed locations for further data analysis. Challenges from data uncertainty, large-scale data size, and distributed data sources motivate us to revisit several classic problems for both online and offline data explorations. The problem of continuous threshold monitoring for distributed data is commonly encountered in many real-world applications. We study this problem for distributed probabilistic data. We show how to prune expensive threshold queries using various tail bounds and combine tail-bound techniques with adaptive algorithms for monitoring distributed deterministic data. We also show how to approximate threshold queries based on sampling techniques. Threshold monitoring problems can only tell a monitoring function is above or below a threshold constraint but not how far away from it. This motivates us to study the problem of continuous tracking functions over distributed data. We first investigate the tracking problem on a chain topology. Then we show how to solve tracking problems on a distributed setting using solutions for the chain model. We studied online tracking of the max function on ""broom"" tree and general tree topologies in this work. Finally, we examine building scalable histograms for distributed probabilistic data. We show how to build approximate histograms based on a partition-and-merge principle on a centralized machine. Then, we show how to extend our solutions to distributed and parallel settings to further mitigate scalability bottlenecks and deal with distributed data
Data acquisition software for the CMS strip tracker
The CMS silicon strip tracker, providing a sensitive area of approximately 200 m2 and comprising 10 million readout channels, has recently been completed at the tracker integration facility at CERN. The strip tracker community is currently working to develop and integrate the online and offline software frameworks, known as XDAQ and CMSSW respectively, for the purposes of data acquisition and detector commissioning and monitoring. Recent developments have seen the integration of many new services and tools within the online data acquisition system, such as event building, online distributed analysis, an online monitoring framework, and data storage management. We review the various software components that comprise the strip tracker data acquisition system, the software architectures used for stand-alone and global data-taking modes. Our experiences in commissioning and operating one of the largest ever silicon micro-strip tracking systems are also reviewed
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking
Various efforts have been devoted to developing stabilizing distributed Model
Predictive Control (MPC) schemes for tracking piecewise constant references. In
these schemes, terminal sets are usually computed offline and used in the MPC
online phase to guarantee recursive feasibility and asymptotic stability.
Maximal invariant terminal sets do not necessarily respect the distributed
structure of the network, hindering the distributed implementation of the
controller. On the other hand, ellipsoidal terminal sets respect the
distributed structure, but may lead to conservative schemes. In this paper, a
novel distributed MPC scheme is proposed for reference tracking of networked
dynamical systems where the terminal ingredients are reconfigured online
depending on the closed-loop states to alleviate the aforementioned issues. The
resulting non-convex infinite-dimensional problem is approximated using a
quadratic program. The proposed scheme is tested in simulation where the
proposed MPC problem is solved using distributed optimization
Distributed Online Optimization via Gradient Tracking with Adaptive Momentum
This paper deals with a network of computing agents aiming to solve an online
optimization problem in a distributed fashion, i.e., by means of local
computation and communication, without any central coordinator. We propose the
gradient tracking with adaptive momentum estimation (GTAdam) distributed
algorithm, which combines a gradient tracking mechanism with first and second
order momentum estimates of the gradient. The algorithm is analyzed in the
online setting for strongly convex and smooth cost functions. We prove that the
average dynamic regret is bounded and that the convergence rate is linear. The
algorithm is tested on a time-varying classification problem, on a (moving)
target localization problem and in a stochastic optimization setup from image
classification. In these numerical experiments from multi-agent learning,
GTAdam outperforms state-of-the-art distributed optimization methods
X-DisETrac: Distributed Eye-Tracking with Extended Realities
Humans use heterogeneous collaboration mediums such as in-person, online, and extended realities for day-to-day activities. Identifying patterns in viewpoints and pupillary responses (a.k.a eye-tracking data) provide informative cues on individual and collective behavior during collaborative tasks. Despite the increasing ubiquity of these different mediums, the aggregation and analysis of eye-tracking data in heterogeneous collaborative environments remain unexplored. Our study proposes X-DisETrac: Extended Distributed Eye Tracking, a versatile framework for eye tracking in heterogeneous environments. Our approach tackles the complexity by establishing a platform-agnostic communication protocol encompassing three data streams to simplify data aggregation and analytics. Our study establishes seminal work in multi-user eye-tracking in heterogeneous environments.https://digitalcommons.odu.edu/gradposters2023_sciences/1010/thumbnail.jp
You never surf alone. Ubiquitous tracking of users' browsing habits
In the early age of the internet users enjoyed a large level of anonymity. At
the time web pages were just hypertext documents; almost no personalisation of
the user experience was o ered. The Web today has evolved as a world wide
distributed system following specific architectural paradigms. On the web now,
an enormous quantity of user generated data is shared and consumed by a network
of applications and services, reasoning upon users expressed preferences and
their social and physical connections. Advertising networks follow users'
browsing habits while they surf the web, continuously collecting their traces
and surfing patterns. We analyse how users tracking happens on the web by
measuring their online footprint and estimating how quickly advertising
networks are able to pro le users by their browsing habits
Anytime Hierarchical Clustering
We propose a new anytime hierarchical clustering method that iteratively
transforms an arbitrary initial hierarchy on the configuration of measurements
along a sequence of trees we prove for a fixed data set must terminate in a
chain of nested partitions that satisfies a natural homogeneity requirement.
Each recursive step re-edits the tree so as to improve a local measure of
cluster homogeneity that is compatible with a number of commonly used (e.g.,
single, average, complete) linkage functions. As an alternative to the standard
batch algorithms, we present numerical evidence to suggest that appropriate
adaptations of this method can yield decentralized, scalable algorithms
suitable for distributed/parallel computation of clustering hierarchies and
online tracking of clustering trees applicable to large, dynamically changing
databases and anomaly detection.Comment: 13 pages, 6 figures, 5 tables, in preparation for submission to a
conferenc
Distributed Recursive Least-Squares: Stability and Performance Analysis
The recursive least-squares (RLS) algorithm has well-documented merits for
reducing complexity and storage requirements, when it comes to online
estimation of stationary signals as well as for tracking slowly-varying
nonstationary processes. In this paper, a distributed recursive least-squares
(D-RLS) algorithm is developed for cooperative estimation using ad hoc wireless
sensor networks. Distributed iterations are obtained by minimizing a separable
reformulation of the exponentially-weighted least-squares cost, using the
alternating-minimization algorithm. Sensors carry out reduced-complexity tasks
locally, and exchange messages with one-hop neighbors to consent on the
network-wide estimates adaptively. A steady-state mean-square error (MSE)
performance analysis of D-RLS is conducted, by studying a stochastically-driven
`averaged' system that approximates the D-RLS dynamics asymptotically in time.
For sensor observations that are linearly related to the time-invariant
parameter vector sought, the simplifying independence setting assumptions
facilitate deriving accurate closed-form expressions for the MSE steady-state
values. The problems of mean- and MSE-sense stability of D-RLS are also
investigated, and easily-checkable sufficient conditions are derived under
which a steady-state is attained. Without resorting to diminishing step-sizes
which compromise the tracking ability of D-RLS, stability ensures that per
sensor estimates hover inside a ball of finite radius centered at the true
parameter vector, with high-probability, even when inter-sensor communication
links are noisy. Interestingly, computer simulations demonstrate that the
theoretical findings are accurate also in the pragmatic settings whereby
sensors acquire temporally-correlated data.Comment: 30 pages, 4 figures, submitted to IEEE Transactions on Signal
Processin
Design and Experimental Verification of Robust Motion Synchronization Control with Integral Action
A robust attitude motion synchronization problem is investigated for multiple 3-degrees-of-freedom (3-DOF) helicopters with input disturbances. The communication topology among the helicopters is modeled by a directed graph, and each helicopter can only access the angular position measurements of itself and its neighbors. The desired trajectories are generated online and not accessible to all helicopters. The problem is solved by embedding in each helicopter some finite-time convergent (FTC) estimators and a distributed controller with integral action. The FTC estimators generate the estimates of desired angular acceleration and the derivative of the local neighborhood synchronization errors. The distributed controller stabilizes the tracking errors and attenuates the effects of input disturbances. The conditions under which the tracking error of each helicopter converges asymptotically to zero are identified, and, for the cases with nonzero tracking errors, some inequalities are derived to show the relationship between the ultimate bounds of tracking errors and the design parameters. Simulation and experimental results are presented to demonstrate the performance of the controllers
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