Browsing by Author "Azimi-Sadjadi, Mahmood R., committee member"

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Open Access
Automated identification of objects in aerial imagery using a CNN: oil/gas sites to Martian volcanoes
(Colorado State University. Libraries, 2021) Dileep, Sonu, author; Beveridge, Ross, advisor; Azimi-Sadjadi, Mahmood R., committee member; Kirby, Michael, committee member
The recent advancements in Deep Learning techniques have revolutionized the field of computer vision. Deep Learning has received massive attention in remote sensing. The availability of open-source satellite imagery has opened up lots of remote sensing applications, from object detection to disaster assessment. In this work, I explore the application of deep learning for automated identification of oil/gas sites in DJ Basin, Colorado and detection of volcanoes on Mars. Oil and gas production sites are one of the significant sources of methane emissions all over the world. Methane emission studies from oil/gas sites require a count of major equipment in a site. However, these counts are not properly documented, and manual annotation of each piece of equipment in a site takes a lot of time and effort. To solve this challenge, an end-to-end deep learning model is developed that finds the well sites from satellite imagery and returns a count of major equipment at each site. Second, an end-to-end deep learning approach is used to detect volcanoes on Mars. Volcanic constructs are fundamental in studying the potential for past and future habitable environment on Mars. Even though large volcanic constructs are well documented, there is no proper documentation for smaller volcanoes. Manually finding all the smaller volcanoes will be a tedious task. In the second part of my work, I explore the potential of deep learning approaches for Martian volcano detection.
Open Access
Continuity of object tracking
(Colorado State University. Libraries, 2022) Williams, Haney W., author; Simske, Steven J., advisor; Azimi-Sadjadi, Mahmood R., committee member; Chong, Edwin K. P., committee member; Beveridge, J. Ross, committee member
The demand for object tracking (OT) applications has been increasing for the past few decades in many areas of interest: security, surveillance, intelligence gathering, and reconnaissance. Lately, newly-defined requirements for unmanned vehicles have enhanced the interest in OT. Advancements in machine learning, data analytics, and deep learning have facilitated the recognition and tracking of objects of interest; however, continuous tracking is currently a problem of interest to many research projects. This dissertation presents a system implementing a means to continuously track an object and predict its trajectory based on its previous pathway, even when the object is partially or fully concealed for a period of time. The system is divided into two phases: The first phase exploits a single fixed camera system and the second phase is composed of a mesh of multiple fixed cameras. The first phase system is composed of six main subsystems: Image Processing, Detection Algorithm, Image Subtractor, Image Tracking, Tracking Predictor, and the Feedback Analyzer. The second phase of the system adds two main subsystems: Coordination Manager and Camera Controller Manager. Combined, these systems allow for reasonable object continuity in the face of object concealment.
Open Access
Deep learning for short-term prediction of wildfire using geostationary satellite observations
(Colorado State University. Libraries, 2024) Saqer, Yousef, author; Chen, Haonan, advisor; Azimi-Sadjadi, Mahmood R., committee member; Wei, Yu, committee member
The aim of this thesis is to utilize the Geostationary Operational Environmental Satellite (GOES) data for predictions regarding the intensity and potential path of wildfires. Using GOES to identify wildfires and extracting data from those events to help train a deep learning model. Three fires were selected for training the deep learning model: the Sequoia, Calwood, and Maui fires. The GOES data of the fires was obtained from band 7 which operates in the Shortwave Window or 3.9μm wavelength, band 7 is able to capture hotspots which is beneficial for wildfire prediction. The radiance data from band 7 is pulled from an Amazon Web Service (AWS) and becomes part of a dataset of 2513 samples. The data is then stacked to form a time series of approximately two hours and converted into a compressed h5 file. The pipeline distributes the dataset by taking in twenty five minutes of input data and feeding four different models to predict seventy five minutes, one hundred minutes, and one hundred and twenty five minutes of data. The data is then fed into a deep learning model utilizing a model known as Self Attention Gated Recurrent Unit (SaGRU). The SaGRU is tested four times, once for predicting seventy five minutes, once for predicting one hundred minutes, and twice for one hundred and twenty five minutes. The models were then compared against each other regarding Mean Squared Error (MSE) and Mean Absolute Error (MAE) along with the Normalized Mean Squared Error (NME) and the Normalized Mean Absolute Error (NMAE). Each metric was taken along multiple thresholds comparing the performance when hotspots are present and when hotspots are absent. The resultant showed that regardless of the sequence length, there was minimal negative impact on early predictions, but as the predicted sequence increased significant loss could be seen on the later predicted frames.
Open Access
Grassmann, Flag, and Schubert varieties in applications
(Colorado State University. Libraries, 2017) Marrinan, Timothy P., author; Kirby, Michael, advisor; Peterson, Chris, advisor; Azimi-Sadjadi, Mahmood R., committee member; Bates, Dan, committee member; Draper, Bruce, committee member
This dissertation develops mathematical tools for signal processing and pattern recognition tasks where data with the same identity is assumed to vary linearly. We build on the growing canon of techniques for analyzing and optimizing over data on Grassmann manifolds. Specifically we expand on a recently developed method referred to as the flag mean that finds an average representation for a collection data that consists of linear subspaces of possibly different dimensions. When prior knowledge exists about relationships between these data, we show that a point analogous to the flag mean can be found as an element of a Schubert variety to incorporates this theoretical information. This domain restriction relates closely to a recent result regarding point-to-set functions. This restricted average along with a property of the flag mean that prioritizes weak but common information, leads to practical applications of the flag mean such as chemical plume detection in long-wave infrared hyperspectral videos, and a modification of the well-known diffusion map for adaptively visualizing data relationships in 2-dimensions.
Open Access
Reliable and energy-efficient cooperative OFDM communications over underwater acoustic channels
(Colorado State University. Libraries, 2015) Cheng, Xilin, author; Yang, Liuqing, advisor; Azimi-Sadjadi, Mahmood R., committee member; Luo, J. Rockey, committee member; Wang, Haonan, committee member
Underwater acoustic sensor networks (UWASN) have been attracting growing research interests in recent decades due to various promising applications. Underwater acoustic communications (UAC), which adopts acoustic waves as the information carrier, is one of the key communication techniques to realize UWASN. However, UAC is very challenging due to low carrier frequency, distance-dependent bandwidth, large delay spread, long and variable propagation delay, and doubly-selective fading. In this research, we will consider cooperative communications to improve the reliability and energy efficiency of dual-hop UAC. OFDM is adopted as the physical-layer transmission technique. First, we will examine power allocation issues. Two transmission scenarios are considered, namely short-range transmission and medium-long range transmission. For the former scenario, an adaptive system is developed based on instantaneous channel state information (CSI); for the latter scenario, an selective relaying protocol is designed based on statistical CSI. Secondly, we will focus on the decomposed fountain codes design to enable reliable communications with higher energy efficiency. Finally, to improve the packet transmission reliability, data repetition within one or two consecutive OFDM symbols is implemented according to the mirror-mapping rules. Theoretical analyses and simulation results demonstrate that the reliability and energy efficiency of dual-hop UAC can be substantially improved using the aforementioned techniques.
Open Access
Resource management in QoS-aware wireless cellular networks
(Colorado State University. Libraries, 2011) Zhang, Zhi, author; Chong, Edwin K. P., advisor; Azimi-Sadjadi, Mahmood R., committee member; Young, Peter M., committee member; Duff, William S., committee member
Emerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study two types of resource allocation problems in QoS-aware wireless cellular networks. First, we develop a rigorous framework to study opportunistic scheduling in multiuser OFDM systems. We derive optimal opportunistic scheduling policies under three common QoS/fairness constraints for multiuser OFDM systems--temporal fairness, utilitarian fairness, and minimum-performance guarantees. To implement these optimal policies efficiently, we provide a modified Hungarian algorithm and a simple suboptimal algorithm. We then propose a generalized opportunistic scheduling framework that incorporates multiple mixed QoS/fairness constraints, including providing both lower and upper bound constraints. Next, taking input queues and channel memory into consideration, we reformulate the transmission scheduling problem as a new class of Markov decision processes (MDPs) with fairness constraints. We investigate the throughput maximization and the delay minimization problems in this context. We study two categories of fairness constraints, namely temporal fairness and utilitarian fairness. We consider two criteria: infinite horizon expected total discounted reward and expected average reward. We derive and prove explicit dynamic programming equations for the above constrained MDPs, and characterize optimal scheduling policies based on those equations. An attractive feature of our proposed schemes is that they can easily be extended to fit different objective functions and other fairness measures. Although we only focus on uplink scheduling, the scheme is equally applicable to the downlink case. Furthermore, we develop an efficient approximation method--temporal fair rollout--to reduce the computational cost.
Open Access
Signal design for active sensing
(Colorado State University. Libraries, 2014) Dang, Wenbing, author; Pezeshki, Ali, advisor; Azimi-Sadjadi, Mahmood R., committee member; Chong, Edwin K. P., committee member; Peterson, Chris, committee member
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