Wireless sensor networks allow us to instrument our world in novel ways providing detailed insight that had not been possible earlier. Since these networks provide an interface to the physical world, it is necessary for each sensor node to learn its location in the physical space. The availability of location information at individual sensor nodes allows the network to provide higher layer services such as location stamped event reporting, geographic routing, in-network processing etc. A wide range of these sensor network protocols do not require absolute node coordinates and can work with relative node positions. This motivates for the need of anchor free localization algorithms that localize the individual sensor nodes with respect to each other in a local coordinate system. Such algorithms allow the sensor networks to be decoupled from external infrastructure and become truly place and play systems. The primary contributions of this thesis include two anchor free localization algorithms and one location refinement algorithm for ad-hoc wireless sensor networks. Our distributed anchor free localization algorithms do not require any external infrastructure in the form of landmark or manually initialized anchor nodes. These algorithms use measured inter-node distances among some node pairs and localize the entire network in a local coordinate system up to a global translation, rotation and reflection. The relative or virtual coordinates assigned by these algorithms can be readily used with a range of sensor network services like geographic routing, data aggregation, topology control etc. Our first localization algorithm is based on a distributed collaborative approach where all of the nodes in the network collaborate with each other to select a set of nodes. These nodes are localized and then used as reference nodes for the remaining sensor nodes. The novelty of this approach is that instead of solving the localization problem for the entire network upfront, first a small well-formed localization problem is solved and then these results are used to solve the localization problem for the remaining nodes in the network. Our second localization algorithm borrows ideas from the data visualization field and exploits the general undirected graph drawing theory to solve the sensor network localization problem. This algorithm divides the network into a large number of small overlapping clusters and creates local coordinate systems for each of the clusters. These clusters are then merged together in a single coordinate system using a novel distributed algorithm that seeks to minimize the error during this merge process. Our final contribution is a distributed location refinement algorithm that can be used with any of the range based localization algorithms to refine the sensor node coordinates to conform to the measured inter-node distances. We model this coordinate refinement problem as an unconstrained non-linear optimization problem and then transform this optimization problem into an aggregate computation problem. We propose two different approaches to solve this aggregate computation problem in a distributed manner. We evaluate our algorithms with detailed simulations using both Matlab and TinyOS simulator TOSSIM. We also validate our simulation results with experimentation carried out on a real network of MIT Cricket motes. We conclude this thesis with lessons learned during this research and discuss some future directions which can be explored to advance the research in sensor network localization.

This thesis is focused on the definition of localization positives and negatives of selected companies in the region in two ways -- in relation "companies affect the region" and in relation "region affect companies". Thesis is in its practice part focused on companies Linea Nivnice, a. s., Slovácké strojírny. a. s. and Česká zbrojovka, a. s. In thesis are defined possible positives and negatives across sectors and types of regions. These positives and negatives are divided into groups of "economic," social "and" environmental "aspects. Thesis contains a complex description of selected companies and the region.

Positioning systems enable location-awareness for mobile devices, computers, and even tactical radios. From the collected location information, location-based services can be realized. One type of positioning system is based on location fingerprints. Unlike the conventional positioning techniques of time of or time delay of arrival (TOA/TDOA) or even angle of arrival (AOA), fingerprinting associates unique characteristics such as received signal strength (RSS) that differentiates a location from another location. The location-dependent characteristics then can be used to infer a user's location. Furthermore, fingerprinting requires no specialized hardware because of its reliance on an existing communications infrastructure. In estimating a user's position, fingerprint-based positioning systems are centrally calculated on a mobile computer using either a Euclidean distance algorithm, Bayesian statistics, or neural networks. With large service areas and, subsequently, large radio maps, one mobile computer may not have the adequate resources to locally compute a user's position. Wireless distributed computing provides a means for the mobile computer to meet the location-based service requirements and increase its network lifetime. This thesis develops distributed localization algorithms to be used in an indoor fingerprint-based positioning system. Fingerprint calculations are not computed on a single device, but rather on a wireless distributed computing network on Virginia Tech's Cognitive Radio Network Testbed (CORNET). / Master of Science

Internet of Things (iot) is a growing field enabled by many different technologies. One of these technologies is  Bluetooth Low Energy (ble). It is of interest to investigate the potential of ble and one interesting, currently unsupported, feature is mesh networking. This thesis work aims to investigate whether it is possible to design and implement a mesh network protocol using ble. To verify the implemented mesh network protocols functionality an indoor localization system has been implemented upon the BLE mesh network protocol. Furthermore this thesis work investigates if an indoor localization system can benefit from using a mesh network. The results of the investigation is a proof of concept of a functional ble mesh network protocol implemented on hardware and tested in a real environment. Tests show that the implemented localization system has similar accuracy as other rssi based indoor localization systems. The largest advantage found for a mesh based indoor localization is the ability to localize objects outside of the radio propagation range of the user. This feature is enabled by multi-hop messaging in the mesh network.

Robots have long been used for completing tasks that are too difficult, dangerous, or distant to be accomplished by humans. In many cases, these robots are highly specialized platforms - often expensive and capable of completing every task related to a mission's objective. An alternative approach is to use multiple platforms, each less capable in terms of number of tasks and thus significantly less complex and less costly. With advancements in embedded computing and wireless communications, multiple such platforms have been shown to work together to accomplish mission objectives. In the extreme, collections of very simple robots have demonstrated emergent behavior akin to that seen in nature (e.g., bee colonies) motivating the moniker of ''swarm robotics'' - a group of robots working collaboratively to accomplish a task. The use of robotic swarms offers the potential to solve complex tasks more efficiently than a single robot by introducing robustness and flexibility to the system. This work investigates localization in heterogeneous and autonomous robotic swarms to improve their ability to carry out exploratory missions in unknown terrain. Collaboratively, these robots can, for example, conduct sensing and mapping of an environment while simultaneously evolving a communication network. For this application, among many others, it is required to determine an accurate knowledge of the robot's pose (i.e., position and orientation). The act of determining the pose of the robot is known as localization. Some low cost robots can provide location estimates using inertial measurements (i.e., odometry), however this method alone is insufficient due to cumulative errors in sensing. Image tracking and wireless localization methods are implemented in this work to increase the accuracy of localization estimates. These localization methods complement each other: image tracking yields higher accuracy than wireless, however a line-of-sight (LOS) with the target is required; wireless localization can operate under LOS or non-LOS conditions, however has issues in multipath conditions. Together, these methods can be used to improve localization results under all sight conditions. The specific contributions of this work are: (1) a concept of 'shared sensing' in which extremely simple and inexpensive robots with unreliable localization estimates are used in a heterogeneous swarm of robots in a way that increases the accuracy of localization for the simple agents and simultaneously extends the sensing capabilities of the more complex robots, (2) a description, evaluation, and discussion of various means to estimate a robot's pose, (3) a method for increasing reliability of RSSI measurements for wireless ranging/localization systems by averaging RSSI measurements over both time and space, (4) a process for developing an in-field model to be used for estimating the location of a robot by leveraging the existing wireless communication system.

This work presents two approaches for integrating data from a low cost GPS receiver in a Monte Carlo localization algorithm. Firstly, an easily applicable method based on data in the standard NMEA protocol is shown. Secondly, an original algorithm utilizing lower level pseudorange measurements accessed in binary receiver-specific format is presented. In addition, a set of tools for analysis of GPS measurement errors on receivers with SiRF III chipset was implemented

Digitized by Kansas Correctional Industries

Messenger RNA localization is a widespread mechanism of posttranscriptional regulation of gene expression in multicellular organisms ranging from yeast to mammals. In Drosophila oocytes, gurken (grk) mRNA is transported by Dynein to produce a local secreted signal to the overlying follicle cells. This signal is responsible for setting up the primary axes in the oocyte. grk mRNA is transcribed in nurse cells and transported into the oocyte where it localizes at two distinct stages of oogenesis, thus targeting the translation of Grk/TGFalpha protein, first to the posterior and later to the dorso-anterior (DA) corner where it is translated. Gurken protein signals to the overlying follicle cells to establish the dorsal fate of the oocyte. grk transcripts are transported by Dynein in EM-dense particles on microtubules. These particles are not associated with vesicles nor membrane-bound and contain many copies of grk mRNA, Dynein and hnRNP Squid. At the DA corner transport particles assemble into large EM-dense cytoplasmic anchoring complexes called Sponge Bodies. In this thesis I present evidence that at their dorso-anterior destination, grk transcripts are statically anchored by Dynein, independently of functional Egalitarian and Bicaudal D, which are required for Dynein transport. I show by the disrupting the protein’s function after it has fulfilled its role in transport that hnRNP Squid is involved in the formation and maintenance of these Sponge Bodies. I provide evidence by EM and fluorescent microscopy that Sponge Bodies share many of components of translational regulation pathways found in Processing Bodies. I show by small RNA interference experiments and by genetic analysis that the structural role of Dynein heavy chain is a unique feature of the Sponge Bodies and that such a function does not occur in Processing Bodies in Drosophila. I show that the localization and anchoring of RNA in Sponge Bodies is not a unique feature of grk mRNA but that I factor RNA is also localized to Sponge bodies. The work presented tries to elucidate the function of Sponge Bodies in translational control of grk mRNA and illustrates by EM the dynamic nature of the Sponge Body structure during oogenesis. My results suggest that Sponge Bodies are RNA granules that are similar to Processing Bodies in a way that they are involved in translational regulation but unlike Processing Bodies depend on Dynein for their structural integrity. I propose that Sponge Bodies are RNA dependent granules that form by the recruitment of proteins involved in the anchoring and translational regulation.

Object localization refers to the detection, matching and segmentation of objects in images. The localization model presented in this paper relies on deformable templates to match objects based on shape alone. The shape structure is captured by a prototype template consisting of hand-drawn edges and contours representing the object to be localized. A multistage, multiresolution algorithm is utilized to reduce the computational intensity of the search. The first stage reduces the physical search space dimensions using correlation to determine the regions of interest where a match it likely to occur. The second stage finds approximate matches between the template and target image at progressively finer resolutions, by attracting the template to salient image features using Edge Potential Fields. The third stage entails the use of evolutionary optimization to determine control point placement for a Local Weighted Mean warp, which deforms the template to fit the object boundaries. Results are presented for a number of applications, showing the successful localization of various objects. The algorithm’s invariance to rotation, scale, translation and moderate shape variation of the target objects is clearly illustrated.

Leishmania chagasi is the causative agent of visceral leishmaniasis in South America. The most abundant glycoprotein on the surface of L. chagasi promastigotes is the glycosylphosphatidylinositol (GPI) anchored protease MSP (major surface protease), also called GP63. MSP is encoded by more than 18 tandem MSP genes on a single chromosome. MSP genes are classified according to unique sequences at their 3' ends and distinct expression patterns. The five MSPS genes (MSPS1, MSPS2, etc.) express 3.0 kb RNAs in stationary phase of promastigote growth in vitro in culture. The > twelve MSPL genes express 2.7 kb RNAs in logarithmic phase of promastigote growth, and the single MSPC RNA is constitutively expressed as two RNA species (2.6 and 3.1 kb) throughout promastigote growth. The progression from logarithmic to stationary phase is accompanied by an increase in parasite virulence for a mammalian host, and a 16-fold increase in the total MSP protein associated with the cell. As such, MSP has been called a virulence factor of leishmania. Little is known about the differences between isoforms of MSP proteins encoded by the three MSP gene classes, because they have a very similar amino acid sequences. The purpose of this thesis was to study the protein expression and localization of MSPS, MSPL, and MSPC in the promastigote and amastigote stages of the L. chagasi. We took three approaches to this problem. First, we produced constructs in which the fluorescent marker GFP was flanked by putative targeting sequences of the MSPs. Second, we generated Leishmania transfectants expressing Myc-tagged full-length MSPs and studied their localization in promastigote cells. Third, we generated antibodies to immunogenic peptides in the few regions with unique sequences that allowed us to distinguish between some of the MSP classes. One monoclonal anti-peptide antibody, named C51, recognized only MSPS1 and MSPL1. Data indicated that the product of the MSPC gene runs at a higher molecular size than products of the MSPL and MSPS genes, both of which localize to the promastigote surface. Overall the data set the stage for future studies of the properties and functions of specific MSP gene products.