Browsing by Subject "Localization"

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Active Target Localization and Tracking with Application to Robotic Environmental Monitoring
(2015-09) Vander Hook, Joshua
Thanks to advances in miniaturization, computing power, reliable sensors, and battery life, mobile robots are increasingly being used for a wide variety of environmental monitoring tasks. No longer confined to factory floors or controlled environments, robots for remote sensing in dangerous or hard-to-reach environments could provide the same scalability, precision, and reliability to environmental monitoring as they did to industrial applications. To enable this kind of long-term, reliable, autonomous mobile sensor deployment, algorithms which can ensure that the robots achieve their sensing tasks are required. In the first part of the thesis, we study the problem of using one or more mobile robots equipped with bearing sensors to locate a stationary target in minimum time. The problem requires optimizing the measurement locations of the robots to gather the required information about the target's location. In addition, when multiple robots collaborate, we include communication constraints in the path planning objective. Two formulations for this problem are studied. First, we study the offline problem of finding measurement trajectories when the true target location is known. Second, we study the online version and show how to adapt the offline solution to the situation when the target location is not known, while preserving the quality guarantees of the offline solution. In the second part of the thesis, we study the problem of locating multiple stationary targets using a single mobile robot. We formulate a novel coverage problem and provide two main results. We first study the problem of initializing consistent estimate of the targets' locations. These initial estimates are used to seed an active localization algorithm which is shown to localize the targets quickly. In a second formulation, we assume that the targets are within a set of polygonal regions, but have no further information about the distribution or number of targets in the environment. An algorithm is provided which can choose measurement locations to localize all the targets to within desired precision in near optimal time. In the third part of the thesis, we study the problem of using bearing information to track and capture a moving target. We present two formulations based on pursuit-evasion games. In the open plane, the objective is for a mobile robot to minimize the distance to a maneuvering target when only uncertain bearing information is available to the robot. Then, we study the problem of capturing the maneuvering target in a closed environment by moving close to it. We show that the size of the environment relative to the sensing noise determines if this is possible. HASH(0x7febe3ca4040) In addition to theoretical results, we present field studies of using one or more mobile robots to detect radio transmitters using these results. We show that the algorithms presented are suitable for use in monitoring invasive fish.
APOBEC3 subcellular localization and genomic editing
(2012-10) Lackey, Lela Lynn
The APOBEC proteins are DNA cytosine deaminases with roles in immunity, including retroviral restriction and antibody maturation. Their activity theoretically makes them a danger to genomic DNA. The subfamily of APOBEC3 genes has expanded to included seven different genes in primates. Based on their subcellular localization, only a subset of these APOBEC3 proteins have access to genomic DNA, and may potentially deaminate genomic DNA. Although the nuclear envelope breaks down during mitosis, I demonstrate that none of the APOBEC3s gain access to genomic DNA during cell division. However, APOBEC3B and other APOBEC3 proteins have access to genomic DNA during interphase. I also show that APOBEC3B is actively imported into the nuclear compartment. In general, APOBEC3 nuclear localization and deaminase activity correlate with ability to affect cell cycle progression, implicating these APOBEC3s in deamination of genomic DNA. In support of these conclusions, I observed cell death, activation of the DNA damage response and DNA mutations after ectopic expression of APOBEC3A and APOBEC3B. Moreover, endogenous APOBEC3B is demonstrably nuclear and active in breast cancer cell lines where it causes genomic deamination and mutations. Endogenous APOBEC3B is highly expressed in more than half of human breast cancers compared to normal breast tissues. In addition, sequences from tumors with higher levels of APOBEC3B have more mutations, and these mutations match APOBEC3B's deamination signature. My thesis work further defines the subcellular localization of the APOBEC3 family and provides the first evidence that APOBEC3B is involved in a human cancer type.
Convex Measures and Associated Geometric and Functional Inequalities
(2015-07) Melbourne, James
Convex measures represent a class of measures that satisfy a variant of the classical Brunn-Minkowski Inequality. Background on the associated functional and geometric inequalities is given, and the elementary theory of such measures is explored. A generalization of the Lovasz and Simonovits localization technique is developed, and some applications to large deviations are explained. In a more geometric direction, a modified Brunn-Minkowski Inequality is explored on some discrete spaces. The significance of such a notion is in its potential to serve as a definition for a lower Ricci curvature bound in non-smooth spaces.
The extreme C-terminus of human Topoisomerase IΙ alpha defines a novel bi-modular DNA tether essential for the formation of mitotic chromosomes.
(2012-05) Lane, Andrew Besançon
Topoisomerase II is the target of an important class of anti-cancer drugs, but tumor cells can become resistant by reducing the association of the enzyme with chromosomes. We have determined the mechanism of Topo IIA recruitment to chromatin and provide new insight into the formation of mitotic chromosomes. We describe the first example of what is likely to be a widespread mechanism for recruitment of chromosomal proteins involving a bi-modular element consisting of an NLS and an associated DNA tether. Catalytically dead Topo IIA is successfully targeted to chromatin, but both the catalytic activity and the bi-modular targeting element are essential for mitotic chromosome formation. Because reduced strand passage activity protects cells from Topo IIA-targeted drugs, it is likely that mutations in the bi-modular element would lead to drug-resistance.
A Human-Centered Cyber-Physical System Framework and its Applications in Gig Delivery
(2022-10) Ding, Yi
With wider and deeper interaction between humans and systems in modern society, the study of human-centered cyber-physical systems (human-centered CPS) has become increasingly important. Thanks to the massive data collected by ubiquitous devices (e.g., smartphones) and advanced machine learning and data mining techniques, numerous human-centered CPS applications and studies are emerging. However, two essential problems still exist: (1) unlike purely internet-based systems, in human-centered CPS, different people engage the system at different places using different devices, which brings technical challenges like scalability and heterogeneity; (2) unlike CPS without wide and deep human participation, in human-centered CPS, human behavior (e.g., locations, mobility, activity) plays a key role, but human behavior is difficult to predict given its inherent uncertainty. To address the challenges, we have done a variety of works that can be organized under the three-layer framework of sensing, prediction, and decision-making. In the sensing layer, we design and build wireless sensing systems to capture human behavior like the arrival and departure at certain locations. We address the scalability challenge by studying human mobility and adopting their smartphones as virtual sensors, and we address the heterogeneity challenge by studying the impacts of environment and hardware on sensing and modeling the similarity with graph learning. In the prediction layer, we study the indoor localization problem by transforming it into a travel time prediction problem and solving it with graph learning based on the human behavior data collected from wireless sensing. In the decision-making layer, we utilize the data from the sensing and the knowledge from prediction to make decisions that lead to higher efficiency compared to the state-of-the-art. We also show how to utilize the feedback from humans to benefit the system design and achieve human-system synergy. In addition to the in-lab design and experiments, we implement our works in one of the latest and largest human-centered CPS applications, gig delivery. By studying couriers’ and merchants’ behavior and building corresponding sensing, prediction, and decision-making systems, we not only improve the system performance but also achieve the synergy between the couriers and systems, saving millions of dollars for the platform and benefiting millions and merchants, couriers, and customers.
Intraurban Accessibility and Employment Density
(2006-08-01) Iacono, Michael J; Cao, Jason X; Cui, Mengying; Levinson, David M
This study investigates the relationship between urban accessibility and firm agglomeration, as reflected in patterns of urban employment densities. We use measures of accessibility derived from the regional highway network, combined with small-scale (Census block-level) data on employment from the Longitudinal Employer-Household Dynamics (LEHD) data set to generate proxies for different sources of agglomeration, specifically urbanization and localization economies. These variables are employed in a set of employment density regressions for 20 two-digit NAICS code sectors to identify the propensity of each sector to agglomerate in response to varying levels of accessibility. The density regressions are applied to sample data from the Minneapolis-St. Paul, Minnesota (Twin Cities) metropolitan region for the years 2000 and 2010. We find that in general urbanization effects tend to overshadow those of localization effects. Moreover, these effects tend to vary by sector, with many service-based sectors showing a stronger propensity to agglomerate than manufacturing and several "basic" sectors like agriculture, mining and utilities.
Motion induced robot-to-robot extrinsic calibration.
(2012-05) Zhou, Xun
Multi-robot systems, or mobile sensor networks, which have become increasingly popular due to recent advances in electronics and communications, can be used in a wide range of applications, such as space exploration, search and rescue, target tracking, and cooperative localization and mapping. In contrast to single robots, multi-robot teams are more robust against single-point failures, accomplish coverage tasks more efficiently by dispersing multiple robots into large areas, and achieve higher estimation accuracy by directly communicating and fusing their sensor measurements. Realizing these advantages of multi-robot systems, however, requires addressing certain challenges. Specifically, in order for teams of robots to cooperate, or fuse measurements from geographically dispersed sensors, they need to know their poses with respect to a common frame of reference. Initializing the robots' poses in a common frame is relatively easy when using GPS, but very challenging in the absence of external aids. Moreover, planning the motion of multiple robots to achieve optimal estimation accuracy is quite challenging. Specifically, since the estimation accuracy depends on the locations where the robots record their sensor measurements, it may take an extensive amount of time to reach a required level of accuracy, if the robots' motions are not properly designed. This thesis offers novel solutions to the aforementioned challenges. The first part of the thesis investigates the problem of relative robot pose initialization, using robot-to-robot distance and/or bearing measurements collected over multiple time steps. In particular, it focuses on solving minimal problems and proves that in 3D there exist only 14 such problems that need to be solved. Furthermore, it provides efficient algorithms for computing the robot-to-robot transformation, which exploit recent advances in algebraic geometry. The second part of the thesis investigates the problem of optimal motion strategies for localization in leader-follower formations using distance or bearing measurements. Interestingly, the robot-to-robot pose is unobservable if the robots move on a straight line and maintain their formations, hence, the uncertainty of the robots' poses increases over time. If the robots, however, deviate from the desired formation, their measurements provide additional information which makes the relative pose observable. This thesis addresses the trade-off between maintaining the formation and estimation accuracy, and provides algorithms for computing the optimal positions where the robots should move to in order to collect the most informative measurements at the next time step. By providing solutions to two important problems for multi-robot systems: motion-induced extrinsic calibration, and optimal motion strategies for relative localization, the work presented in this thesis is expected to promote the use of multi-robot teams in real-world applications.
Ranging and positioning in wireless sensor networks.
(2008-08) Srirangarajan, Seshan
Ranging and positioning in wireless sensor networks refers to the ability to determine the positions of all nodes in a sensor network using the known positions of a few nodes called reference nodes and pairwise distance or range estimates between neighboring nodes. This is also known as the sensor network localization problem. In this thesis we first present two time-of arrival based localization algorithms for indoor quasi-static environments based on statistical modeling of the ultra-wideband multipath channel. A model of the multipath channel in the form of the signal return and noise characterization is derived, and utilized to distinguish signal components from noise. The first localization algorithm uses multiple (ranging) signal receptions at each reference node, to differentiate between line-of-sight and non-line-of-sight components, and to accurately estimate the position of the line-of-sight component in the received multipath signal. The second localization algorithm employs a time-of-arrival based algorithm to obtain pseudo range estimates which are then used in a spatial domain quasi-maximum likelihood method for location estimation. Furthermore, the associated range estimation error does not increase with increase in the transmitter-receiver range. We next present a distributed solution of the sensor network localization problem based on second-order cone programming relaxation. This algorithm is independent of the ranging technique being used and is computationally more efficient than most contemporary approaches, and scalable to networks with thousands of nodes. We show that the nodes can estimate their positions based on local information. Unlike previous approaches, we also consider the effect of inaccurate reference node positions. In the presence of reference node position errors, the localization is performed in three steps. First, the unlocalized nodes estimate their positions using information from their neighbors. In the second step, the reference nodes refine their positions using relative distance information exchanged with their neighbors and finally, the previously unlocalized nodes refine their position estimates. We demonstrate the convergence of the algorithm numerically. The simulation results, shown for both uniform and irregular network topologies, illustrate the robustness of the algorithm to reference node position and distance estimation errors. We also present the prototype implementation of a directional beacon based positioning algorithm using radio frequency signals. This algorithm allows each unlocalized node to compute its position with respect to a set of reference nodes which are equipped with rotating directional antenna. The directional beacon based algorithm eliminates the need for strict synchronization between the reference nodes and the unlocalized node. In contrast to time-of-arrival based positioning algorithms that rely on signal propagation time and bandwidth, the directional beacon based algorithm depends on the width of the antenna beampattern and the rotational speed of the directional antenna. We will show that these parameters can be optimized in a low cost solution while providing good position estimates. The system implementation is based on the GNU Radio software platform and the Universal Software Radio Peripheral as the hardware component. To deal with obstructed line-of-sight scenarios, we do not rely purely on the received signal strength and instead formulate a least squares problem to estimate the line-of-sight component in a multipath environment. These signal processing techniques yield a more accurate estimation of the bearing of the unlocalized node with respect to each of the reference nodes. We demonstrate the ability to obtain unlocalized node position estimates with sub-meter accuracy by transmitting a narrowband signal of 1 KHz bandwidth in the 2.4 GHz band. Finally, event detection scenarios in sensor networks are considered. The goal in these network deployments is to detect certain critical or emergency conditions with minimum possible delay. We propose a heuristic based sensor selection and a sequential detection procedure that significantly improves the detection speed, measured in terms of the number of measurements needed for detection. In the proposed model, the fusion center selects one sensor at a time for measurement while maximizing a greedy heuristic. Instead of collecting a fixed number of measurements, the fusion center collects one measurement at each time step, until by some sequential decision rule the collection stops and a decision is made. The sequential detection procedure significantly outperforms a non-sequential (or fixed sample size) detector in that it always needs fewer measurements on average to achieve the same detection performance. In addition, we derive a simplified heuristic under the Gaussian probabilistic model. It is seen that the simplified heuristic performs as good as or slightly better than the greedy heuristic. The greedy heuristic based sensor selection provides a general framework for probabilistic models where a simplified heuristic is difficult to obtain. (Abstract shortened by UMI.)
Simulation data for: "Two parameter scaling in the crossover from symmetry class BDI to AI"
(2022-08-01) Kasturirangan, Saumitran; Kamenev, Alex; Burnell, Fiona J; kastu007@umn.edu; Kasturirangan, Saumitran
The transport statistics at finite energies near a quantum critical point in the presence of disorder were not well understood analytically. This was approached by performing extensive simulations of transport using the package KWANT for python for disordered 1D quantum chains and metallic arm-chair graphene nanoribbons. This dataset contains the resulting data for several system sizes, strengths, and energies. This was used to establish two-parameter scaling and characterize the transport statistics.
Towards Reality: Egocentric Environment Understanding for Mobile Devices
(2023-12) Do, Tien
Mixed reality (MR) allows the users to overlay the virtual contents onto the physical world. Given an image taken by a mounted camera on the MR headset, two fundamental questions must be solved: (1) Where am I? and (2) Where is what?. The first question concerns estimating the 6 degree-of-freedom of the camera and the second question concerns predicting the scene layout, contents and their geometries (depth, surface normal, shape) from the captured egocentric image. Even though these problems have been studied in robotics and computer vision, deploying the existing methods to the mobile MR headsets faces two challenges: (1) high computational cost and power consumption for tracking and localization algorithms, and (2) degeneration of existing scene understanding models due to the domain gap caused by the users' large head that motions capture egocentric views. This dissertation seeks to address these two major challenges to enable MR into our daily usage. Specifically, it explores two methods to obtain compact map representations that are suitable for camera localization (collection of scene landmarks) and navigation (topological map). Furthermore, it proposes a multimodal spatial rectifiers that allows the scene geometry (depth, surface normal) predictor module to focus on only a few modes of the head orientation, significantly reduces the requirement for high capacity model and large scale 3D-annotated egocentric data. The proposed methods are validated on exisiting datasets, as well as newly introduced challenging egocentric datasets. Finally, a large scale egocentric dataset with 3D objects is introduced to study the object's 3D shape and pose estimation benchmark tasks.
Wireless sensor networking for intelligent transportation systems.
(2009-11) Jeong, Jaehoon
This dissertation studies the Wireless Sensor Networking for Intelligent Transportation Systems, tailored and optimized for road networks. For military scenarios, since the road networks are used for main maneuver of military troops in cities or urban areas, they need to be protected for military operations. For civil engineering scenarios, the Intelligent Transportation Systems have been developed and been evolving to support the driving safety and transportation efficiency through the information computing and communications among transportation infrastructures and vehicles. Roadways are mainly used for the transportation of people and goods and also are nowadays equipped with intelligent devices, such as electronic tollgates and variable message signs for driving. In addition to this, vehicles are equipped with GPS-based navigation systems and emergency notification systems for the driving efficiency and safety. With this trend, Wireless Sensor Networks have been considered new parts for the Intelligent Transportation Systems and are being deployed into road networks in order to enhance further the driving safety and security. This dissertation studies the key technologies in the wireless sensor networking for the security and communications in the road networks as follows: (i) Localization for sensor location, (ii) Road Surveillance for vehicle monitoring, (iii) Data Forwarding for road sensing data delivery and (iv) Reverse Data Forwarding for road condition information sharing. In order to design the technologies to be tailored for road networks, this dissertation investigates the characteristics of road networks and takes advantage of the characteristics for the wireless networking. The first characteristic is the predictable vehicle mobility within the roadways. The second is the abstract representation of the layouts of the road networks into road maps. The third is the vehicular traffic statistics representing the vehicle density on the roadways and intersections. The fourth is the vehicle trajectory representing the future vehicle mobility along the roadways, guided by the GPS navigation systems. These four characteristics open a door of new research on wireless sensor networks. Therefore, using these road network characteristics, this dissertation designs and evaluates the wireless sensor networking technologies for road networks.

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