Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Using sparse coding as a preprocessing technique for insect detection in pulsed LIDAR data(Montana State University - Bozeman, College of Engineering, 2022) Zsidisin, Connor Reece; Chairperson, Graduate Committee: Brad WhitakerThis research proposes using sparse coding as a preprocessing technique on insect lidar based data. This preprocessing technique will be used in conjunction with the Adaptive Boosting (AdaBoost), Random UnderSampling Boosting (RUSBoost), and neural network algorithms to automatically detect insects. The project aims to increase the effectiveness of these algorithms by using new images created by sparse coding. The K-Singular Value Decomposition (KSVD) algorithm will be used to train a dictionary on images that contain the majority class (non-insects). This trained dictionary will be used along with Orthogonal Matching Pursuit (OMP) to reconstruct all lidar images. The difference between the original image and the reconstructed image will be taken and processed by the feature extraction function and then used to train and test the models. Using a complete and an overcomplete dictionary our results show that the algorithms are able to detect insects at a higher rate. Using an overcomplete dictionary we are able to classify 93.18% of insect containing images in the testing dataset. Using the complete dictionary we were able to maintain 99.70% of non-insect images while increasing the percentage of insects classified to 84.09%.Item Machine learning pipeline for rare-event detection in synthetic-aperture radar and LIDAR data(Montana State University - Bozeman, College of Engineering, 2021) Scofield, Trey Palmer; Chairperson, Graduate Committee: Brad WhitakerIn this work, we develop a machine learning pipeline to autonomously classify synthetic aperture radar (SAR) and lidar data in rare-event, remote sensing applications. Here, we are predicting the presence of volcanoes on the surface of Venus, fish in Yellowstone Lake, and select marine-life in the Gulf of Mexico. Given the efficiency of collecting SAR images in space and airborne lidar geographical surveys, the size of the datasets are immense. Immense training data is desirable for machine learning models; however, a large majority of the data we are using do not contain volcanoes or fish, respectively. Thus, the machine learning models must be formulated in such a way to place a high emphasis on the minority, target classes. The developed pipeline includes data preprocessing, unsupervised clustering, feature extraction, and classification. For each collection of data, sub-images are initially fed through the pipeline to capture fine detail characteristics until they are mapped back to their original image to identify overall region behavior and the location of the target class(es). For both sub-images and original images, results were quantified and the most effective algorithm combinations and parameters were assigned. In this analysis, we determined the classification results are not sufficient enough to propel a completely autonomous system, rather, some manual observing of the data will need to be performed. Nonetheless, the pipeline serves as an effective tool to reduce costs associated with electronic storage and transmission of the data, as well as human labor in manually inspecting the data. It does this by removing a majority of the unimportant, non-target data in some cases while successfully retaining a high percentage of the important images.Item Digitally automated alignment of a phase-shifting point diffraction interferometer(Montana State University - Bozeman, College of Engineering, 2020) Field, Nathaniel James; Chairperson, Graduate Committee: Joseph A. ShawReal-time sensing of wavefront error in laser instruments is an exceptionally useful tool for fine-tuning of laser systems during fabrication. Measurement and correction for potential wavefront aberrations are especially important for high-energy laser system applications, such as defense and industrial manufacturing. The self-referencing Mach-Zehnder interferometer and the Shack-Hartmann wavefront sensor are two common methods used to achieve real-time wavefront aberration measurements for laser system output quality; however, the former requires a precise and arduous alignment procedure for each operation and the latter exchanges spatial resolution for phase resolution and is highly sensitive to global tilt. The use of electronically controlled spatial light modulators has been shown as a method of quickly retrieving wavefront reconstructions from phase-shifting point diffraction interferometers. In this paper, the development of an algorithm that automates the selection of the point diffractor position and size was added to the phase-shifting point diffraction method with a purely reflective spatial light modulator. Computer simulations and laboratory tests were conducted as proofs of concept using a few simple optical elements. The results of these simulations and lab measurements show promise for continually automated alignment of a point diffraction interferometer to greatly reduce alignment time and almost entirely remove sensitivity to global tilt. With further development, this method can be applied to increase the efficiency of a wide variety of optical system measurement scenarios.Item Development of a smart camera system using a system on module FPGA(Montana State University - Bozeman, College of Engineering, 2017) Dack, Connor Aquila; Chairperson, Graduate Committee: Ross K. SniderImaging systems can now produce more data than conventional PCs with frame grabbers can process in real-time. Moving real-time custom computation as close as possible to the image sensor will alleviate the bandwidth bottle-neck of moving data multiple times through buffers in conventional PC systems, which are also computation bottlenecks. An example of a high bandwidth, high computation application is the use of hyperspectral imagers for sorting applications. Hyperspectral imagers capture hundreds of colors ranging from the visible spectrum to the infrared. This masters thesis continues the development of the hyperspectral smart camera by integrating the image sensor with a field programmable gate array (FPGA) and by developing an object tracking algorithm for use during the sorting process, with the goal of creating a single compact embedded solution. An FPGA is a hardware programmable integrated circuit that can be reprogrammed depending on the application. The prototype integration involves the development of a custom printed circuit board to connect the data and control lines between the sensor, the FPGA, and the control code to read data from the sensor. The hyperspectral data is processed on the FPGA and is combined with the object edges to make a decision on the quality of the object. The object edges are determined using a line scan camera, which provides data via the Camera Link interface, and a custom object tracking algorithm. The object tracking algorithm determines the horizontal edges and center of the object while also tracking the vertical edges and center of the object. The object information is then passed to the air jet sorting subsystem which ejects bad objects. The solution is to integrate the hyperspectral image sensor, the two processing algorithms, and Camera Link interface into a single, compact unit by implementing the design on the Intel Arria 10 System on Module with custom printed circuit boards.Item Algorithms for nearly time-optimal and insensitive computer control of systems(Montana State University - Bozeman, College of Engineering, 1971) Trieu, Kenneth Lieng-QuacItem System identification methods for adaptive control(Montana State University - Bozeman, College of Engineering, 1989) Sadighi, IrajItem Dynamic programming using region-limiting strategies for optimization of multidimensional nonlinear processes(Montana State University - Bozeman, College of Engineering, 1971) Arora, Jagdish KumarItem Implementation of null steering algorithms in a compact analog array(Montana State University - Bozeman, College of Engineering, 2014) Condori Quispe, Hugo Orlando; Chairperson, Graduate Committee: Richard WolffIn this thesis, the implementation of null steering algorithms in a compact analog array is demonstrated and validated. The performance of the null steering algorithms is validated through extensive simulation and hardware implementation. The results of the techniques of null steering, including controlling the complex weights, usually have to rely on simulations to study system performances, design trade-offs, and system optimization, which by itself can be quite complex and a time-consuming task. Even after extensive simulations, it is not easy to get insights as to what parameters determine system performance in different system parameters, and the interactions on system parameters. Therefore, experimentation and deployment on a real system is required. Few studies have proposed null steering algorithms studies using real implementations. With this motivation, this work presents comprehensive performance comparison of some of the available null steering techniques using an analog array. The contributions of this thesis are: optimize the performance of null steering algorithms taking into account realistic considerations in the simulations and demonstrating the benefits through extensive simulations; and verify the performance of the null steering system through experimental implementation using a simple, compact, lightweight, low cost, high gain, high throughput analog antenna array.Item High resolution direction of arrival estimation analysis and implementation in a smart antenna system(Montana State University - Bozeman, College of Engineering, 2010) Khallaayoun, Ahmed; Chairperson, Graduate Committee: Richard WolffThe goal of this research is to equip the smart antenna system designed by the telecommunication group at the department of Electrical and Computer Engineering at Montana State University with high resolution direction of arrival estimation (DOA) capabilities; the DOA block should provide accurate estimates of emitters' DOAs while being computationally efficient. Intensive study on DOA estimation algorithms was carried out to pinpoint the most suitable algorithm for the application of interest, and the spectral methods were chosen for this study. The outcome of the study consisted of generating a novel algorithm, spatial selective MUSIC, which is comparable in accuracy to other high resolution algorithms but does not require the intensive computational burden that is typical of high resolution spectral methods. Spatial selective MUSIC is compared in terms of bias, resolution, robustness and computational efficiency against the most widely used DOA estimation algorithms, namely, Bartlett, Capon, MUSIC, and beamspace MUSIC. The design, troubleshooting, and implementation of the hardware needed to implement the DOA estimation in a real case scenario was achieved. Two design phases were necessary to implement the center piece of the hardware needed to achieve DOA estimation. The 5.8 GHz 8 channel receiver board along with a casing that egg crates the RF channels for channel-to-channel isolation was designed and built. A National Instrument data acquisition card was used to simultaneously sample all the 8 channels at 2.5 MSPS, the data was processed using the PC interface built in LabView. Phase calibration that accounts for the overall system magnitude and phase differences along with a novel calibration method to mitigate the effects of magnitude and phase variations along with mutual coupling was produced during this research and was imperative to achieving high resolution DOA estimation in the lab. The DOA estimation capabilities of the built system was tested within the overall smart antenna system and showed promising results. The overall performance enhancement that the DOA estimation block can provide cannot however be fully realized until the beamforming block is revised to provide accurate and deep null placing along with a narrower beam width. This cannot be achieved with the current system due to limitations in the number of the array elements used and the granularity in the phase shifters and attenuators used in the analog beamformer.