Browsing by Subject "Localization"

The Slow-time Costas or "SLO-CO" Continuous Active Sonar (CAS) waveform shows promise for enabling high range and velocity revisit rates and wideband processing gains while suppressing range ambiguities. SLO-CO is made up of non-recurrent wideband linear FM chirps that are frequency staggered according to a Costas code across the pulse repetition interval. SLO-CO is shown to provide a near-thumbtack ambiguity functions with controllable sidelobes, good Doppler and range resolution at high revisit rates. The performance of the SLO-CO waveform was tested using the Sonar Simulation Toolset (SST) as well as in the shallow water Target and Reverberation Experiment 2013 (TREX13). For both the real and simulated results, the performance of the SLO-CO is compared to the conventional CAS waveform. Amplitude-Range-Velocity (ARV) processing of SLO-CO experimental trials reveal that relatively high direct blast sidelobes mask the target peak. Methods of suppressing the direct blast are discussed including adaptive filtering and re-designing the waveform.

Purpose: Radiation doses from computed tomography (CT) examinations have come under public and governmental scrutiny because of several recent misadministrations of radiation across the country. Current CT dosimetry methods in the clinic use standardized cylindrical water phantoms to measure radiation dose across various scanning protocols and different scanner manufacturers. These methods and equipment are too generalized to provide accurate risk assessment for patients of varying ages, genders, and anatomies. The advent of computer models based on real CT imaged anatomy has made patient specific and organ specific dosimetry achievable.

With a population of both pediatric and adult patient models comprised of a wide range of anatomies, Monte Carlo based dose calculations can be cataloged. A patient can receive a prospective dose estimation from a phantom within our population that best exhibits the patient's age and anatomical characteristics. Knowledge of organ size and location is essential to finding a proper match between the patient and the computer model. To this end, very little information is currently available regarding organ size and location across a diverse human population. The purpose of this study was to develop a predictive model to ascertain organ locations and volumes for pediatric and adult patients.

Methods: This study included 51 adults and 40 pediatrics from which Extended NURBS-based Cardiac-Torso (XCAT) phantoms were generated. Large organs were manually segmented from clinical CT data. The remaining organs and other anatomical structures were created by transforming an existing human model template to fit the framework of the segmented structures. The maximum and minimum points of the organs were recorded with respect to the axial distance from the tip of the sacrum. The axial width and midpoint for each organ were then determined. The organ volumes were also calculated. All three organ parameters were plotted as functions of patient age and weight for adults and patient age for pediatrics.

Results: The adult patients showed no statistically significant correlation between organ parameters and age and BMI. There were slight, positive linear trends with organ midpoint (max r2=0.365, mean r2=0.185) and organ volume (max r2=0.510, mean r2=0.183) versus adult patient weight. The height correlations were also positive for midpoint (r2=0.485, mean r2=0.271). Gaussian fits performed on probability density functions of adult organs resulted in r2-values ranging from 0.945 to 0.996. Pediatric patients demonstrated strong cube root relationships with organ midpoints (max r2=0.857, mean r2=0.790) and organ widths (max r2=0.905 , mean r2=0.564) versus age. Pediatric organ volumes showed positive linear relationships versus age (max r2=0.983, mean r2=0.701).

Conclusions: Adult patients exhibited small variations in organ volume and location with respect to weight, but no meaningful correlation existed between these parameters and age. Once adulthood is reached, organ morphology and positioning seems to remain static; however, clear trends are evident between pediatric age and organ volumes and locations. Such information can aid in the selection of an appropriate computer model that has the highest probability of mirroring the anatomy of a patient undergoing a clinical exam. Applications could also extend into comparing PET versus CT determination of organ volume and location.

Wireless and mobile systems play an increasingly important role in our lives. Fueled by an array of innovative services and applications, mobile data traffic is surging rapidly. Traditionally, wireless traffic growth is met by acquiring new spectrum. However, wireless spectrum demand is soon going to surpass it's availability. Thus, there is an urgent need for major innovations in wireless network architecture, so that our spectrum utilization can achieve its full potential. Motivated by this problem, we explore an alternative design of physical layer aware wireless systems.

Typical approaches towards improving wireless performance is confined within the physical (PHY) or link layers of the networking stack, providing only partial so- lutions. In this thesis, we advocate to consider the entire network architecture holis- tically. We show how rich PHY layer information can be utilized to address existing challenges in wireless networking - contention resolution, rate control, interference management, etc. We design, implement, and experimentally evaluate protocols to understand network-wide implications of PHY-aware systems. We also pursue the observation that PHY layer not only encode bits but also contain rich information about the ambience, and hence can be viewed as a sensor. This sensing informa- tion can be further coupled with other phone sensors, thereby benefitting pervasive mobile services and applications. We demonstrate how this synergy can contribute towards designing precise indoor localization systems, an important building block for next generation mobile applications.

Posttranscriptional gene expression is controlled and coordinated by RNA binding proteins (RBPs), many of which recognize specific RNAs through cis-regulatory RNA elements. One of the most highly studied classes of cis-regulatory RNA elements is the AU-rich elements (AREs). AREs are bound by a class of RBPs called ARE binding proteins (ARE-BPs), of which there are over a dozen in humans including HuR, tristetraprolin (TTP) and butyrate response factors 1 and 2 (BRF1 and BRF2). TTP, BRF1 and BRF2 belong to a family of tandem C3H zinc finger proteins that destabilize ARE-containing mRNAs. HuR acts to enhance the stability and translation of ARE-containing mRNAs, a function that is rare among ARE-BPs. While each of these ARE-BPs regulates the expression of ARE-containing mRNAs, some ARE-BPs themselves are also encoded by ARE-containing mRNAs, raising the possibility that each of these ARE-BPs may regulate one another's expression. In order to determine how these ARE-BPs influence each others expression and how this affects the regulation of global gene expression programs we have focused on three different aspects of these ARE-BP networks: control, response to stimuli, and global effects.

To address of network control of ARE-BPs we have focused on how HuR regulates a network of mRNAs including TTP, BRF1 and HuR's own mRNA. We demonstrate that HuR can bind to TTP's, BRF1's and its own mRNA. Furthermore, by employing overexpression and siRNA knockdown approaches we demonstrate that these mRNAs and their corresponding 3'UTR luciferase reporters are resilient to fluctuations in HuR levels and that the degree of this resiliency is cell type and condition specific.

To address the temporal responses within an ARE-BP network we focused on how each of the members of the TTP family of ARE-BPs reacts following the induction of the other family members by using epidermal growth factor (EGF) stimulation. Here we show that induction of TTP family member mRNAs during EGF stimulation is partially attributable to changes in mRNA stability. Furthermore, we also show that TTP and BRF1 are able to bind each of the TTP family member mRNAs and subsequently affect their expression by altering their mRNA degradation rates. In addition, we demonstrate that the unique temporal induction patterns of the TTP family member RBPs is correlated with the EGF stimulated induction of TTP-bound mRNAs, suggesting that a network comprised of TTP family members is able to influence the timing of complex gene expression patterns.

Finally, to address the influence of these networks on regulation of global gene expression programs we have focused on how HuR recognizes AREs and whether it can globally recognize multiple classes of ARE-containing mRNAs, including the canonical class of AREs recognized by the TTP family members. To investigate how the three RNA recognition motifs (RRMs) of HuR contribute to ARE recognition we generated a series of RRM point mutants and test their ability to disrupt RNA recognition of each of the RRMs. To identify different classes of ARE-containing mRNAs we examined these mutants with a global RNA binding site detection method called photoactivatable ribonucleoside crosslinking immunoprecipitation (PAR-CLIP). Together these techniques suggest that the RRMs of HuR cooperate to recognize mRNA targets and that HuR's ability to bind RNA is coupled to the cellular distribution of HuR, and thus, are important in its role for regulating expression of bound mRNAs.

Together these studies indicate that ARE-BP posttranscriptional networks are highly interconnected and display complex regulatory interactions depending on cell type and stimuli. Furthermore, these networks can create complex behaviors such as timing of expression events or resiliency to fluctuations in protein levels. Finally, the components of these ARE-BP networks target partially overlapping sets of mRNAs to impact global gene expression patterns that ultimately coordinate the cellular responses to external stimuli.

Localization is projected to play a critical role in mobile computing applications. Nevertheless, the state of the art is inadequate especially when operating on mobile devices. More specifically, the on-phone GPS sensor has an unacceptable energy consumption and does not operate indoors. Alternate localization techniques, based on WiFi or GSM, alleviate some of the GPS limitations but provide degraded accuracy and assume pre-installed infrastructure. As a result, these solutions need extensive war-driving for collecting location fingerprints and, in many instances, limit services to regions close to drivable paths. Moreover, when infrastructure is scarce or missing, the localization accuracy is poor. Lastly, relying on hardware deployments is costly and raises scalability concerns when targeting wide regions.

To address the shortcomings of current solutions, we propose four new localization systems: (1) CompAcc enables energy-efficient, war-driving-free localization using the phone inertial sensors and digital maps, (2) Escort provides indoor localization by exploiting the phone inertial sensors and social environments where people are mobile, (3) SurroundSense enables indoor logical localization (e.g., inside Target) by sensing the user ambiance through the phone sensors, and (4) EnLoc proposes energy-efficient localization via personalized mobility profiling and predictions.