Engineering Commons^™

Low-Cost Stereo Vision On An Fpga, Chris A. Murphy, Daniel Lindquist, Ann Marie Rynning, Thomas Cecil, Sarah Leavitt, Mark L. Chang

Mark L. Chang

We present a low-cost stereo vision implementation suitable for use in autonomous vehicle applications and designed with agricultural applications in mind. This implementation utilizes the Census transform algorithm to calculate depth maps from a stereo pair of automotive-grade CMOS cameras. The final prototype utilizes commodity hardware, including a Xilinx Spartan-3 FPGA, to process 320times240 pixel images at greater than 150 frames per second and deliver them via a USB 2.0 interface.

Interactionless Calendar-Based Training For 802.11 Localization, Mark Chang, Andrew J. Barry, Noah L. Tye

Mark L. Chang

This paper presents our work in solving one of the weakest links in 802.11-based indoor-localization: the training of ground-truth received signal strength data. While crowdsourcing this information has been demonstrated to be a viable alternative to the time consuming and accuracy-limited process of manual training, one of the chief drawbacks is the rate at which a system can be trained. We demonstrate an approach that utilizes users' calendar and appointment information to perform interactionless training of an 802.11-based indoor localization system. Our system automatically determines if a user attended a calendar event, resulting in accuracy comparable to our previously published …

Multi-Tier Exploration Concept Demonstration Mission, Jeremy Straub

Jeremy Straub

A multi-tier, multi-craft mission architecture has been proposed but, despite its apparent promise, limited use and testing of the architecture has been conducted. This paper proposes and details a mission concept and its implementation for testing this architecture in the terrestrial environment. It is expected that this testing will allow significant refinement of the proposed architecture as well as providing data on its suitability for use in both terrestrial and extra-terrestrial applications. Logistical and technical challenges with this testing are discussed.

Error Estimation Techniques To Refine Overlapping Aerial Image Mosaic Processes Via Detected Parameters, William Glenn Bond

Dissertations

In this paper, I propose to demonstrate a means of error estimation preprocessing in the assembly of overlapping aerial image mosaics. The mosaic program automatically assembles several hundred aerial images from a data set by aligning them, via image registration using a pattern search method, onto a GIS grid.

The method presented first locates the images from a data set that it predicts will not align well via the mosaic process, then it uses a correlation function, optimized by a modified Hooke and Jeeves algorithm, to provide a more optimal transformation function input to the mosaic program. Using this improved …

Framework Developmant For Construction Safety Visialization, Kishor Shrestha

College of Engineering: Graduate Celebration Programs

Throughout the history of the construction industry, many fatalities and injuries have occurred in construction sites. One of the major causes of accidents is unsafe site conditions: basically, this is due to inadequate supervision. To improve upon the traditional supervision approach, this study proposes a 'Framework Development for Construction Safety Visualization' approach. In addition to this, a computer vision Edge Detection Algorithm was developed and tested to convert construction site still images into edges of the objects in the images. The framework development of this study uses computer vision, robot vision, image compression, pattern recognition, internet transmission, network communication, and …

Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas

George J. Pappas

We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …

Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas

George J. Pappas

We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …

Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas

George J. Pappas

We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …

Extreme Learning Machine Terrain-Based Navigation For Unmanned Aerial Vehicles, Ee May Kan, Meng Hiot Lim, Yew Soon Ong, Ah-Hwee Tan, Swee Ping Yeo

Research Collection School Of Computing and Information Systems

Unmanned aerial vehicles (UAVs) rely on global positioning system (GPS) information to ascertain its position for navigation during mission execution. In the absence of GPS information, the capability of a UAV to carry out its intended mission is hindered. In this paper, we learn alternative means for UAVs to derive real-time positional reference information so as to ensure the continuity of the mission. We present extreme learning machine as a mechanism for learning the stored digital elevation information so as to aid UAVs to navigate through terrain without the need for GPS. The proposed algorithm accommodates the need of the …

Fusion Of Visual And Thermal Images Using Genetic Algorithms, Sertan Erkanli, Jiang Li, Ender Oguslu, Shangce Gao (Ed.)

Electrical & Computer Engineering Faculty Publications

No abstract provided.

Mathematical Model Development Of Super-Resolution Image Wiener Restoration, Amr H. Yousef, Jiang Li, Mohammad A. Karim

Electrical & Computer Engineering Faculty Publications

In super-resolution (SR), a set of degraded low-resolution (LR) images are used to reconstruct a higher-resolution image that suffers from acquisition degradations. One way to boost SR images visual quality is to use restoration filters to remove reconstructed images artifacts. We propose an efficient method to optimally allocate the LR pixels on the high-resolution grid and introduce a mathematical derivation of a stochastic Wiener filter. It relies on the continuous-discrete-continuous model and is constrained by the periodic and nonperiodic interrelationships between the different frequency components of the proposed SR system. We analyze an end-to-end model and formulate the Wiener filter …

Fast Stochastic Wiener Filter For Super-Resolution Image Restoration With Information Theoretic Visual Quality Assessment, Amr Hussein Yousef, Jiang Li, Mohammad Karim, Mark Allen Neifeld (Ed.), Amit Ashok (Ed.)

Electrical & Computer Engineering Faculty Publications

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. The reconstructed image suffers from degradations such as blur, aliasing, photo-detector noise and registration and fusion error. Wiener filter can be used to remove artifacts and enhance the visual quality of the reconstructed images. In this paper, we introduce a new fast stochastic Wiener filter for SR reconstruction and restoration that can be implemented efficiently in the frequency domain. Our derivation depends on the continuous-discrete-continuous (CDC) model that represents most of the degradations encountered during the image-gathering …

Toward Automatic Subpixel Registration Of Unmanned Airborne Vehicle Images, Amr Hussein Yousef, Jiang Li, Mohammad Karim, Mark Allen Neifeld (Ed.), Amit Ashok (Ed.)

Electrical & Computer Engineering Faculty Publications

Many applications require to register images within subpixel accuracy like computer vision especially super-resolution (SR) where the estimated subpixel shifts are very crucial in the reconstruction and restoration of SR images. In our work we have an optical sensor that is mounted on an unmanned airborne vehicle (UAV) and captures a set of images that contain sufficient overlapped area required to reconstruct a SR image. Due to the wind, The UAV may encounter rotational effects such as yaw, pitch and roll which can distort the acquired as well as processed images with shear, tilt or perspective distortions. In this paper …

Real-Time Anomaly Detection In Full Motion Video, Glenn Konowicz,, Jiang Li, Donnie Self (Ed.)

Electrical & Computer Engineering Faculty Publications

Improvement in sensor technology such as charge-coupled devices (CCD) as well as constant incremental improvements in storage space has enabled the recording and storage of video more prevalent and lower cost than ever before. However, the improvements in the ability to capture and store a wide array of video have required additional manpower to translate these raw data sources into useful information. We propose an algorithm for automatically detecting anomalous movement patterns within full motion video thus reducing the amount of human intervention required to make use of these new data sources. The proposed algorithm tracks all of the objects …

Evolved Design Of A Nonlinear Proportional Integral Derivative (Npid) Controller, Shubham Chopra

Dissertations and Theses

This research presents a solution to the problem of tuning a PID controller for a nonlinear system. Many systems in industrial applications use a PID controller to control a plant or the process. Conventional PID controllers work in linear systems but are less effective when the plant or the process is nonlinear because PID controllers cannot adapt the gain parameters as needed. In this research we design a Nonlinear PID (NPID) controller using a fuzzy logic system based on the Mamdani type Fuzzy Inference System to control three different DC motor systems. This fuzzy system is responsible for adapting the …

A Hybrid Tabu Search And Constraint Programming Algorithm For The Dynamic Dial-A-Ride Problem, Gerardo Berbeglia

Gerardo Berbeglia

No abstract provided.

Quantification Of Stochastic Uncertainty Propagation For Monte Carlo Depletion Methods In Reactor Analysis, Quentin Thomas Newell

UNLV Theses, Dissertations, Professional Papers, and Capstones

The Monte Carlo method provides powerful geometric modeling capabilities for large problem domains in 3-D; therefore, the Monte Carlo method is becoming popular for 3-D fuel depletion analyses to compute quantities of interest in spent nuclear fuel including isotopic compositions. The Monte Carlo approach has not been fully embraced due to unresolved issues concerning the effect of Monte Carlo uncertainties on the predicted results.

Use of the Monte Carlo method to solve the neutron transport equation introduces stochastic uncertainty in the computed fluxes. These fluxes are used to collapse cross sections, estimate power distributions, and deplete the fuel within depletion …

Improved Algorithms For Ear-Clipping Triangulation, Bartosz Kajak

UNLV Theses, Dissertations, Professional Papers, and Capstones

We consider the problem of improving ear-slicing algorithm for triangulating a simple polygon. We propose two variations of ear-slicing technique for generating “good-quality” triangulation. The first approach is based on searching for the best triangle along the boundary. The second approach considers polygon partitioning on a pre-process before applying the ear-slicing. Experimental investigation reveals that both approaches yield better quality triangulation than the standard ear-slicing method.

Resizable, Scalable, Concurrent Hash Tables, Josh Triplett, Paul E. Mckenney, Jonathan Walpole

Computer Science Faculty Publications and Presentations

We present algorithms for shrinking and expanding a hash table while allowing concurrent, wait-free, linearly scalable lookups. These resize algorithms allow the hash table to maintain constant-time performance as the number of entries grows, and reclaim memory as the number of entries decreases, without delaying or disrupting readers.

We implemented our algorithms in the Linux kernel, to test their performance and scalability. Benchmarks show lookup scalability improved 125x over readerwriter locking, and 56% over the current state-of-the-art for Linux, with no performance degradation for lookups during a resize.

To achieve this performance, this hash table implementation uses a new concurrent …

A Feature Based Frequency Domain Analysis Algorithm For Fault Detection Of Induction Motors, Zhaoxia Wang, C. S. Chang, Zhang Yifan

Research Collection School Of Computing and Information Systems

This paper studies the stator currents collected from several inverter-fed laboratory induction motors and proposes a new feature based frequency domain analysis method for performing the detection of induction motor faults, such as the broken rotor-bar or bearing fault. The mathematical formulation is presented to calculate the features, which are called FFT-ICA features in this paper. The obtained FFT-ICA features are normalized by using healthy motor as benchmarks to establish a feature database for fault detection. Compare with conventional frequency-domain analysis method, no prior knowledge of the motor parameters or other measurements are required for calculating features. Only one phase …

Adaptive Decision Support For Structured Organizations: A Case For Orgpomdps, Pradeep Reddy Varakantham, Nathan Schurr, Alan Carlin, Christopher Amato

Research Collection School Of Computing and Information Systems

In today's world, organizations are faced with increasingly large and complex problems that require decision-making under uncertainty. Current methods for optimizing such decisions fall short of handling the problem scale and time constraints. We argue that this is due to existing methods not exploiting the inherent structure of the organizations which solve these problems. We propose a new model called the OrgPOMDP (Organizational POMDP), which is based on the partially observable Markov decision process (POMDP). This new model combines two powerful representations for modeling large scale problems: hierarchical modeling and factored representations. In this paper we make three key contributions: …

Unmanned Aerial Vehicles Collision Avoidance From Moving Obstacles, Atila Ozdemir

Electrical & Computer Engineering Theses & Dissertations

The usage of the unmanned aerial vehicles (UAV) is becoming more diverse day by day in both military and civil applications. Specifically in military applications, they are becoming a "must-have" component in every arsenal. Low cost, and especially the importance of human life, makes these vehicles desired in any military situation. But in order to take advantage of the UAVs, these vehicles should be able to navigate safely and not collide with other air vehicles, especially ones that are manned and carry personnel. As a result, the importance of a collision avoidance system of the UA V arises and begins …

A Probabilistic Analysis Of Misparking In Reservation Based Parking Garages, Vikas G. Ashok

Computer Science Theses & Dissertations

Parking in major cities is an expensive and annoying affair, the reason ascribed to the limited availability of parking space. Modern parking garages provide parking reservation facility, thereby ensuring availability to prospective customers. Misparking in such reservation based parking garages creates confusion and aggravates driver frustration. The general conception about misparking is that it tends to completely cripple the normal functioning of the system leading to chaos and confusion. A single mispark tends to have a ripple effect and therefore spawns a chain of misparks. The chain terminates when the last mispark occurs at the parking slot reserved by the …

Fusion Of Visual And Thermal Images Using Genetic Algorithms, Sertan Erkanli

Electrical & Computer Engineering Theses & Dissertations

Demands for reliable person identification systems have increased significantly due to highly security risks in our daily life. Recently, person identification systems are built upon the biometrics techniques such as face recognition. Although face recognition systems have reached a certain level of maturity, their accomplishments in practical applications are restricted by some challenges, such as illumination variations. Current visual face recognition systems perform relatively well under controlled illumination conditions while thermal face recognition systems are more advantageous for detecting disguised faces or when there is no illumination control. A hybrid system utilizing both visual and thermal images for face recognition …

Video Stabilization Based On Speeded-Up Robust Features, Minqi Zhou

Electrical & Computer Engineering Theses & Dissertations

With the advancement of science and technology, portable devices using video cameras are becoming more popular. Videos captured by ordinary commercial cameras always suffer from undesired motion which results from human hand shaking and mobile platform vibration. The undesired motion would not only blur the image degrading the image quality leading to inaccurate results in automatic object recognition and tracking, but also make it difficult for people to focus on specific object regions. It is also a possibility that one may feel dizzy while watching shaky video for a long time. Many hardware and software methodologies have been developed by …

Fine-Tuning Algorithm Parameters Using The Design Of Experiments Approach, Aldy Gunawan, Hoong Chuin Lau, Linda Lindawati

Research Collection School Of Computing and Information Systems

Optimizing parameter settings is an important task in algorithm design. Several automated parameter tuning procedures/configurators have been proposed in the literature, most of which work effectively when given a good initial range for the parameter values. In the Design of Experiments (DOE), a good initial range is known to lead to an optimum parameter setting. In this paper, we present a framework based on DOE to find a good initial range of parameter values for automated tuning. We use a factorial experiment design to first screen and rank all the parameters thereby allowing us to then focus on the parameter …

Eeg Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen, John Musson, Jiang Li, Frederick Mckenzie, Guangfan Zhang, Roger Xu, Carl Richey, Tom Schnell, Thomas E. Pinelli (Ed.)

Electrical & Computer Engineering Faculty Publications

In this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We compared the WNN algorithm with the ICA technique and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

Histogram Analysis Of Adc In Brain Tumor Patients, Debrup Banerjee, Jihong Wang, Jiang Li, Norbert J. Pelc (Ed.), Ehsan Samei (Ed.), Robert M. Nishikawa (Ed.)

Electrical & Computer Engineering Faculty Publications

At various stage of progression, most brain tumors are not homogenous. In this presentation, we retrospectively studied the distribution of ADC values inside tumor volume during the course of tumor treatment and progression for a selective group of patients who underwent an anti-VEGF trial. Complete MRI studies were obtained for this selected group of patients including pre- and multiple follow-up, post-treatment imaging studies. In each MRI imaging study, multiple scan series were obtained as a standard protocol which includes T1, T2, T1-post contrast, FLAIR and DTI derived images (ADC, FA etc.) for each visit. All scan series (T1, T2, FLAIR, …

2d Face Database Diversification Based On 3d Face Modeling, Qun Wang, Jiang Li, Vijayan K. Asari, Mohammad A. Karim, Manuel Filipe Costa (Ed.)

Electrical & Computer Engineering Faculty Publications

Pose and illumination are identified as major problems in 2D face recognition (FR). It has been theoretically proven that the more diversified instances in the training phase, the more accurate and adaptable the FR system appears to be. Based on this common awareness, researchers have developed a large number of photographic face databases to meet the demand for data training purposes. In this paper, we propose a novel scheme for 2D face database diversification based on 3D face modeling and computer graphics techniques, which supplies augmented variances of pose and illumination. Based on the existing samples from identical individuals of …

Marine Buoy Detection Using Circular Hough Transform, Loc Tran, Justin Selfridge, Gene Hou, Jiang Li

Electrical & Computer Engineering Faculty Publications

A low cost method for buoy detection in maritime settings is presented using inexpensive digital cameras. In this method, the circular Hough transform is applied to an edge image to circular objects in the image. The center of these circles will signify the locations of each buoy. The known color information of the buoys is also used to enhance the performance by removing false detections. The algorithm is compared to an approach that locates buoys purely on color information. In order to validate the method, we test the approach synthetically and also with real images captured from a small surface …