Matching Items (206)
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description
A numerical study of incremental spin-up and spin-up from rest of a thermally- stratified fluid enclosed within a right circular cylinder with rigid bottom and side walls and stress-free upper surface is presented. Thermally stratified spin-up is a typical example of baroclinity, which is initiated by a sudden increase in rotation rate and the tilting of isotherms gives rise to baroclinic source of vorticity. Research by (Smirnov et al. [2010a]) showed the differences in evolution of instabilities when Dirichlet and Neumann thermal boundary conditions were applied at top and bottom walls. Study of parametric variations carried out in this dissertation confirmed the instability patterns observed by them for given aspect ratio and Rossby number values greater than 0.5. Also results reveal that flow maintained axisymmetry and stability for short aspect ratio containers independent of amount of rotational increment imparted. Investigation on vorticity components provides framework for baroclinic vorticity feedback mechanism which plays important role in delayed rise of instabilities when Dirichlet thermal Boundary Conditions are applied.
ContributorsKher, Aditya Deepak (Author) / Chen, Kangping (Thesis advisor) / Huang, Huei-Ping (Committee member) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2011
Description
Image processing in canals, rivers and other bodies of water has been a very important concern. This research using Image Processing was performed to obtain a photographic evidence of the data of the site which helps in monitoring the conditions of the water body and the surroundings. Images are captured using a digital camera and the images are stored onto a datalogger, these images are retrieved using a cellular/ satellite modem. A MATLAB program was designed to obtain the level of water by just entering the file name into to the program, a curve fit model was created to determine the contrast parameters. The contrast parameters were obtained using the data obtained from the gray scale image mainly the mean and variance of the intensity values. The enhanced images are used to determine the level of water by taking pixel intensity plots along the region of interest. The level of water obtained is accurate to less than 2% of the actual level of water observed from the image. High speed imaging in micro channels have various application in industrial field, medical field etc. In medical field it is tested by using blood samples. The experimental procedure proposed determines the flow duration and the defects observed in these channel using a fluid introduced into the micro channel the fluid being water based dye and whole milk. The viscosity of the fluid shows different types of flow patterns and defects in the micro channel. The defects observed vary from a small effect to the flow pattern to an extreme defect in the channel such as obstruction of flow or deformation in the channel. The sample needs to be further analyzed by SEM to get a better insight on the defects.
ContributorsShasedhara, Abhijeet Bangalore (Author) / Lee, Taewoo (Thesis advisor) / Huang, Huei-Ping (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2011
Description
A new method of adaptive mesh generation for the computation of fluid flows is investigated. The method utilizes gradients of the flow solution to adapt the size and stretching of elements or volumes in the computational mesh as is commonly done in the conventional Hessian approach. However, in the new method, higher-order gradients are used in place of the Hessian. The method is applied to the finite element solution of the incompressible Navier-Stokes equations on model problems. Results indicate that a significant efficiency benefit is realized.
ContributorsShortridge, Randall (Author) / Chen, Kang Ping (Thesis advisor) / Herrmann, Marcus (Thesis advisor) / Wells, Valana (Committee member) / Huang, Huei-Ping (Committee member) / Mittelmann, Hans (Committee member) / Arizona State University (Publisher)
Created2011
Description
The evolution of single hairpin vortices and multiple interacting hairpin vortices are studied in direct numerical simulations of channel flow at Re-tau=395. The purpose of this study is to observe the effects of increased Reynolds number and varying initial conditions on the growth of hairpins and the conditions under which single hairpins autogenerate hairpin packets. The hairpin vortices are believed to provide a unified picture of wall turbulence and play an important role in the production of Reynolds shear stress which is directly related to turbulent drag. The structures of the initial three-dimensional vortices are extracted from the two-point spatial correlation of the fully turbulent direct numerical simulation of the velocity field by linear stochastic estimation and embedded in a mean flow having the profile of the fully turbulent flow. The Reynolds number of the present simulation is more than twice that of the Re-tau=180 flow from earlier literature and the conditional events used to define the stochastically estimated single vortex initial conditions include a number of new types of events such as quasi-streamwise vorticity and Q4 events. The effects of parameters like strength, asymmetry and position are evaluated and compared with existing results in the literature. This study then attempts to answer questions concerning how vortex mergers produce larger scale structures, a process that may contribute to the growth of length scale with increasing distance from the wall in turbulent wall flows. Multiple vortex interactions are studied in detail.
ContributorsParthasarathy, Praveen Kumar (Author) / Adrian, Ronald (Thesis advisor) / Huang, Huei-Ping (Committee member) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2011
Description
The exhaust system is an integral part of any internal combustion engine. A well- designed exhaust system efficiently removes exhaust gasses expelled from the cylinders. If tuned for performance purposes, the exhaust system can also exhibit scavenging and supercharging characteristics. This project reviews the major components of an exhaust system and discusses the proper design techniques necessary to utilize the performance boosting potential of a tuned exhaust system for a four-stroke engine. These design considerations are then applied to Arizona State University's Formula SAE vehicle by comparing the existing system to a properly tuned system. An inexpensive testing method, developed specifically for this project, is used to test the effectiveness of the current design. The results of the test determined that the current design is ineffective at scavenging neighboring pipes of exhaust gasses and should be redesigned for better performance.
ContributorsKnutsen, Jeffrey Scott (Author) / Huang, Huei-Ping (Thesis director) / Steele, Bruce (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2014-05
Description
Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices for real-time data analysis and decision-making in digital signal processing (DSP) applications. Most of the current efforts in DSP education focus on building tools to facilitate understanding of the mathematical principles. However, there is a disconnect between real-world data processing problems and the material presented in a DSP course. Sophisticated mobile interfaces and apps can potentially play a crucial role in providing a hands-on-experience with modern DSP applications to students. In this work, a new paradigm of DSP learning is explored by building an interactive easy-to-use health monitoring application for use in DSP courses. This is motivated by the increasing commercial interest in employing mobile phones for real-time health monitoring tasks. The idea is to exploit the computational abilities of the Android platform to build m-Health modules with sensor interfaces. In particular, appropriate sensing modalities have been identified, and a suite of software functionalities have been developed. Within the existing framework of the AJDSP app, a graphical programming environment, interfaces to on-board and external sensor hardware have also been developed to acquire and process physiological data. The set of sensor signals that can be monitored include electrocardiogram (ECG), photoplethysmogram (PPG), accelerometer signal, and galvanic skin response (GSR). The proposed m-Health modules can be used to estimate parameters such as heart rate, oxygen saturation, step count, and heart rate variability. A set of laboratory exercises have been designed to demonstrate the use of these modules in DSP courses. The app was evaluated through several workshops involving graduate and undergraduate students in signal processing majors at Arizona State University. The usefulness of the software modules in enhancing student understanding of signals, sensors and DSP systems were analyzed. Student opinions about the app and the proposed m-health modules evidenced the merits of integrating tools for mobile sensing and processing in a DSP curriculum, and familiarizing students with challenges in modern data-driven applications.
ContributorsRajan, Deepta (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
With robots being used extensively in various areas, a certain degree of robot autonomy has always been found desirable. In applications like planetary exploration, autonomous path planning and navigation are considered essential. But every now and then, a need to modify the robot's operation arises, a need for a human to provide it some supervisory parameters that modify the degree of autonomy or allocate extra tasks to the robot. In this regard, this thesis presents an approach to include a provision to accept and incorporate such human inputs and modify the navigation functions of the robot accordingly. Concepts such as applying kinematical constraints while planning paths, traversing of unknown areas with an intent of maximizing field of view, performing complex tasks on command etc. have been examined and implemented. The approaches have been tested in Robot Operating System (ROS), using robots such as the iRobot Create, Personal Robotics (PR2) etc. Simulations and experimental demonstrations have proved that this approach is feasible for solving some of the existing problems and that it certainly can pave way to further research for enhancing functionality.
ContributorsVemprala, Sai Hemachandra (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
In contemporary society, sustainability and public well-being have been pressing challenges. Some of the important questions are:how can sustainable practices, such as reducing carbon emission, be encouraged? , How can a healthy lifestyle be maintained?Even though individuals are interested, they are unable to adopt these behaviors due to resource constraints. Developing a framework to enable cooperative behavior adoption and to sustain it for a long period of time is a major challenge. As a part of developing this framework, I am focusing on methods to understand behavior diffusion over time. Facilitating behavior diffusion with resource constraints in a large population is qualitatively different from promoting cooperation in small groups. Previous work in social sciences has derived conditions for sustainable cooperative behavior in small homogeneous groups. However, how groups of individuals having resource constraint co-operate over extended periods of time is not well understood, and is the focus of my thesis. I develop models to analyze behavior diffusion over time through the lens of epidemic models with the condition that individuals have resource constraint. I introduce an epidemic model SVRS ( Susceptible-Volatile-Recovered-Susceptible) to accommodate multiple behavior adoption. I investigate the longitudinal effects of behavior diffusion by varying different properties of an individual such as resources,threshold and cost of behavior adoption. I also consider how behavior adoption of an individual varies with her knowledge of global adoption. I evaluate my models on several synthetic topologies like complete regular graph, preferential attachment and small-world and make some interesting observations. Periodic injection of early adopters can help in boosting the spread of behaviors and sustain it for a longer period of time. Also, behavior propagation for the classical epidemic model SIRS (Susceptible-Infected-Recovered-Susceptible) does not continue for an infinite period of time as per conventional wisdom. One interesting future direction is to investigate how behavior adoption is affected when number of individuals in a network changes. The affects on behavior adoption when availability of behavior changes with time can also be examined.
ContributorsDey, Anindita (Author) / Sundaram, Hari (Thesis advisor) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Ten regional climate models (RCMs) and atmosphere-ocean generalized model parings from the North America Regional Climate Change Assessment Program were used to estimate the shift of extreme precipitation due to climate change using present-day and future-day climate scenarios. RCMs emulate winter storms and one-day duration events at the sub-regional level. Annual maximum series were derived for each model pairing, each modeling period; and for annual and winter seasons. The reliability ensemble average (REA) method was used to qualify each RCM annual maximum series to reproduce historical records and approximate average predictions, because there are no future records. These series determined (a) shifts in extreme precipitation frequencies and magnitudes, and (b) shifts in parameters during modeling periods. The REA method demonstrated that the winter season had lower REA factors than the annual season. For the winter season the RCM pairing of the Hadley regional Model 3 and the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model had the lowest REA factors. However, in replicating present-day climate, the pairing of the Abdus Salam International Center for Theoretical Physics' Regional Climate Model Version 3 with the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model was superior. Shifts of extreme precipitation in the 24-hour event were measured using precipitation magnitude for each frequency in the annual maximum series, and the difference frequency curve in the generalized extreme-value-function parameters. The average trend of all RCM pairings implied no significant shift in the winter annual maximum series, however the REA-selected models showed an increase in annual-season precipitation extremes: 0.37 inches for the 100-year return period and for the winter season suggested approximately 0.57 inches for the same return period. Shifts of extreme precipitation were estimated using predictions 70 years into the future based on RCMs. Although these models do not provide climate information for the intervening 70 year period, the models provide an assertion on the behavior of future climate. The shift in extreme precipitation may be significant in the frequency distribution function, and will vary depending on each model-pairing condition. The proposed methodology addresses the many uncertainties associated with the current methodologies dealing with extreme precipitation.
ContributorsRiaño, Alejandro (Author) / Mays, Larry W. (Thesis advisor) / Vivoni, Enrique (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2013