Computational Engineering Commons^™

Object Detection And Classification In The Visible And Infrared Spectrums, Domenick D. Poster

Graduate Theses, Dissertations, and Problem Reports

The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark …

Dynamic Response Of Elastic Two-Story Steel Moment Frame Scaled Structure Equipped With Viscous Dampers, Garrett L. Barker, Alexander L. Poirier

Architectural Engineering

The authors of this report are Architectural Engineering undergraduate students at California Polytechnic State University, San Luis Obispo. Damping is a complex, experimentally derived value that is affected by many structural properties and has a profound effect on the dynamic response of structures. Deducing the inherent damping of a steel moment frame and affecting the damping ratio with viscous dampers are two topics explored in this paper. Dampers are commonly implemented in resilient structures that perform better in a design basis earthquake, reducing the seismic cost and downtime. Undergraduate coursework does not delve into the factors that affect damping and …

Speaker Diarization And Identification From Single-Channel Classroom Audio Recording Using Virtual Microphones, Antonio Gomez

Electrical and Computer Engineering ETDs

Speaker identification in noisy audio recordings, specifically those from collaborative learning environments, can be extremely challenging. There is a need to identify individual students talking in small groups from other students talking at the same time. To solve the problem, we assume the use of a single microphone per student group without any access to previous large datasets for training.

This dissertation proposes a method of speaker identification using cross-correlation patterns associated to an array of virtual microphones, centered around the physical microphone. The virtual microphones are simulated by using approximate speaker geometry observed from a video recording. The patterns …

Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano

Electrical and Computer Engineering ETDs

Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …

Context-Aware Sensing And Fusion For Structural Health Monitoring And Night Time Traffic Surveillance, Xinxiang Zhang

Electrical Engineering Theses and Dissertations

Rapid developments in computer vision technologies have been transforming many traditional fields in engineering and science in the last few decades, especially in terms of diagnosing problems from visual images. Leveraging computer vision technologies to inspect, monitor, assess infrastructure conditions, and analyze traffic dynamics, has gained significant increase in both effectiveness and efficiency, compared to the cost of traditional instrumentation arrays to monitor, and manually inspect civil infrastructures and traffic conditions. Therefore, to construct the next-generation intelligent civil and transportation infrastructures, this dissertation develops a comprehensive computer-vision based sensing and fusion framework for structural health monitoring and intelligent transportation systems. …

Energy Considerations In Blockchain-Enabled Applications, Cesar Enrique Castellon Escobar

UNF Graduate Theses and Dissertations

Blockchain-powered smart systems deployed in different industrial applications promise operational efficiencies and improved yields, while mitigating significant cybersecurity risks pertaining to the main application. Associated tradeoffs between availability and security arise at implementation, however, triggered by the additional resources (e.g., memory, computation) required by each blockchain-enabled host. This thesis applies an energy-reducing algorithmic engineering technique for Merkle Tree root and Proof of Work calculations, two principal elements of blockchain computations, as a means to preserve the promised security benefits but with less compromise to system availability. Using pyRAPL, a python library to measure computational energy, we experiment with both the …

Receptive Fields Optimization In Deep Learning For Enhanced Interpretability, Diversity, And Resource Efficiency., Babajide Odunitan Ayinde

Electronic Theses and Dissertations

In both supervised and unsupervised learning settings, deep neural networks (DNNs) are known to perform hierarchical and discriminative representation of data. They are capable of automatically extracting excellent hierarchy of features from raw data without the need for manual feature engineering. Over the past few years, the general trend has been that DNNs have grown deeper and larger, amounting to huge number of final parameters and highly nonlinear cascade of features, thus improving the flexibility and accuracy of resulting models. In order to account for the scale, diversity and the difficulty of data DNNs learn from, the architectural complexity and …

Image Processing Applications In Real Life: 2d Fragmented Image And Document Reassembly And Frequency Division Multiplexed Imaging, Houman Kamran Habibkhani

LSU Doctoral Dissertations

In this era of modern technology, image processing is one the most studied disciplines of signal processing and its applications can be found in every aspect of our daily life. In this work three main applications for image processing has been studied.

In chapter 1, frequency division multiplexed imaging (FDMI), a novel idea in the field of computational photography, has been introduced. Using FDMI, multiple images are captured simultaneously in a single shot and can later be extracted from the multiplexed image. This is achieved by spatially modulating the images so that they are placed at different locations in the …

An Exact Analysis For Four-Order Acousto-Optic Bragg Diffraction Which Incorporates Both Incident Light Angle And Sound Frequency Dependencies, Adeyinka Sunday Ademola

Electrical Engineering Theses

This thesis extends the prior work which produced an exact solution to the four-order acousto-optic (AO) Bragg cell with assumed fixed center frequency and with exact Bragg angle incident light. The extension predicts the model that incorporates the dependencies of both the input angle of light and the sound frequency. Specifically, a generalized 4^th order linear differential equation (DE), is developed from a simultaneous analysis of four coupled AO system of DEs. Through standard methods, the characteristic roots, which requires solving a quartic equation, is produced. Subsequently, a derived system of homogeneous solutions, which absorbs the roots obtained using …

Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan

Masters Theses

Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.

State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to …

A Comprehensive Analysis On Eeg Signal Classification Using Advanced Computational Analysis, Kaushik Bhimraj

Electronic Theses and Dissertations

Electroencephalogram (EEG) has been used in a wide array of applications to study mental disorders. Due to its non-invasive and low-cost features, EEG has become a viable instrument in Brain-Computer Interfaces (BCI). These BCI systems integrate user's neural features with robotic machines to perform tasks. However, due to EEG signals being highly dynamic in nature, BCI systems are still unstable and prone to unanticipated noise interference. An important application of this technology is to help facilitate the lives of the tetraplegic through assimilating human brain impulses and converting them into mechanical motion. However, BCI systems are remarkably challenging to implement …

Optimizing Harris Corner Detection On Gpgpus Using Cuda, Justin Loundagin

Master's Theses

ABSTRACT

Optimizing Harris Corner Detection on GPGPUs Using CUDA

The objective of this thesis is to optimize the Harris corner detection algorithm implementation on NVIDIA GPGPUs using the CUDA software platform and measure the performance benefit. The Harris corner detection algorithm—developed by C. Harris and M. Stephens—discovers well defined corner points within an image. The corner detection implementation has been proven to be computationally intensive, thus realtime performance is difficult with a sequential software implementation. This thesis decomposes the Harris corner detection algorithm into a set of parallel stages, each of which are implemented and optimized on the CUDA platform. …

Nuclei/Cell Detection In Microscopic Skeletal Muscle Fiber Images And Histopathological Brain Tumor Images Using Sparse Optimizations, Hai Su

Theses and Dissertations--Computer Science

Nuclei/Cell detection is usually a prerequisite procedure in many computer-aided biomedical image analysis tasks. In this thesis we propose two automatic nuclei/cell detection frameworks. One is for nuclei detection in skeletal muscle fiber images and the other is for brain tumor histopathological images.

For skeletal muscle fiber images, the major challenges include: i) shape and size variations of the nuclei, ii) overlapping nuclear clumps, and iii) a series of z-stack images with out-of-focus regions. We propose a novel automatic detection algorithm consisting of the following components: 1) The original z-stack images are first converted into one all-in-focus image. 2) A …

Bayesian Dictionary Learning For Single And Coupled Feature Spaces, Li He

Doctoral Dissertations

Over-complete bases offer the flexibility to represent much wider range of signals with more elementary basis atoms than signal dimension. The use of over-complete dictionaries for sparse representation has been a new trend recently and has increasingly become recognized as providing high performance for applications such as denoise, image super-resolution, inpaiting, compression, blind source separation and linear unmixing. This dissertation studies the dictionary learning for single or coupled feature spaces and its application in image restoration tasks. A Bayesian strategy using a beta process prior is applied to solve both problems.

Firstly, we illustrate how to generalize the existing beta …

Real-Time Musical Analysis Of Polyphonic Guitar Audio, John E. Hartquist

Master's Theses

In this thesis, we analyze the audio signal of a guitar to extract musical data in real-time. Specifically, the pitch and octave of notes and chords are displayed over time. Previous work has shown that non-negative matrix factorization is an effective method for classifying the pitches of simultaneous notes. We explore the effect of window size, hop length, and other parameters to maximize the resolution and accuracy of the output.

Other groups have required prerecorded note samples to build a library of note templates to search for. We automate this step and compute the library at run-time, tuning it specifically …

Solving The Vehicle Re-Identification Problem By Using Neural Networks, Tanweer Rashid

Computational Modeling & Simulation Engineering Theses & Dissertations

Vehicle re-identification is the process by which vehicle attributes measured at one point on a road network are compared to vehicle attributes measured at another point in an effort to match vehicles without using any unique identifiers such as license plate numbers. A match is made if the two measurements are estimated to belong to the same vehicle. Vehicle attributes can be sensor readings such as loop induction signatures, or they can also be actual vehicle characteristics such as length, weight, number of axles, etc. This research makes use of vehicle length, travel time, axle spacing and axle weights for …

Design Of Efficient Algorithms Through Minimization Of Data Transfers, Yong Mo Chong

Electrical & Computer Engineering Theses & Dissertations

This thesis explores the time optimal implementation of computational graphs on a finite register machine. The implementation fully exploits the machine architecture, especially, the number of registers. The derived algorithms allow one to obtain time efficient implementations of a given graph in machines with a known number of registers.

These optimization procedures are applied to digital signal processing graphs. It is shown that the regular structure of these graphs allows one to identify computational kernels which, when used repeatedly, can cover the entire graph. The l- and r-register implementations of Hadamard and Fast Fourier Transforms using various computational kernels are …