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- Adversarial Attacks (1)
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Articles 1 - 5 of 5
Full-Text Articles in Computer Engineering
Real-Time Analysis Of Aerosol Size Distributions With The Fast Integrated Mobility Spectrometer (Fims), Daisy Wang
Real-Time Analysis Of Aerosol Size Distributions With The Fast Integrated Mobility Spectrometer (Fims), Daisy Wang
McKelvey School of Engineering Theses & Dissertations
The Fast Integrated Mobility Spectrometer (FIMS) has emerged as an innovative instrument in the aerosol science domain. It employs a spatially varying electric field to separate charged aerosol particles by their electrical mobilities. These separated particles are then enlarged through vapor condensation and imaged in real time by a high-speed CCD camera. FIMS achieves near 100% detection efficiency for particles ranging from 10 nm to 600 nm with a temporal resolution of one second. However, FIMS’ real-time capabilities are limited by an offline data analysis process. Deferring analysis until hours or days after measurement makes FIMS' capabilities less valuable for …
Watch: A Distributed Clock Time Offset Estimation Tool On The Platform For Open Wireless Data-Driven Experimental Research, Cassie Jeng
McKelvey School of Engineering Theses & Dissertations
The synchronization of the clocks used at different devices across space is of critical importance in wireless communications networks. Each device’s local clock differs slightly, affecting the times at which packets are transmitted from different nodes in the network. This thesis provides experimentation and software development on POWDER, the Platform for Open, Wireless Data-driven Experimental Research, an open wireless testbed across the University of Utah campus. We build upon Shout, a suite of Python scripts that allow devices to iteratively transmit and receive with each other and save the collected data. We introduce WATCH, an experimental method to estimate clock …
Mirror Position Detection In A Catoptric Surface, Run Zhang
Mirror Position Detection In A Catoptric Surface, Run Zhang
McKelvey School of Engineering Theses & Dissertations
The Catoptric Surface research project is a pioneering exploration of controlling daylight effects within built environments. In this thesis, we focus on the mirror position detection problem, which plays a vital role in achieving dynamic control over the direction of reflected light within a space. To address the challenge of mirror position detection, we employ computer vision techniques, specifically edge detection and the RANdom SAmple Consensus (RANSAC) algorithm. Edge detection is utilized to identify significant changes in intensity or color, corresponding to object boundaries, while RANSAC is applied for ellipse fitting. By iteratively selecting minimal subsets of points and fitting …
Targeted Adversarial Attacks Against Neural Network Trajectory Predictors, Kaiyuan Tan
Targeted Adversarial Attacks Against Neural Network Trajectory Predictors, Kaiyuan Tan
McKelvey School of Engineering Theses & Dissertations
Trajectory prediction is an integral component of modern autonomous systems as it allows for envisioning future intentions of nearby moving agents. Due to the lack of other agents' dynamics and control policies, deep neural network (DNN) models are often employed for trajectory forecasting tasks. Although there exists an extensive literature on improving the accuracy of these models, there is a very limited number of works studying their robustness against adversarially crafted input trajectories. To bridge this gap, in this paper, we propose a targeted adversarial attack against DNN models for trajectory forecasting tasks. We call the proposed attack TA4TP for …
Adversarial Patch Attacks On Deep Reinforcement Learning Algorithms, Peizhen Tong
Adversarial Patch Attacks On Deep Reinforcement Learning Algorithms, Peizhen Tong
McKelvey School of Engineering Theses & Dissertations
Adversarial patch attack has demonstrated that it can cause the misclassification of deep neural networks to the target label when the size of patch is relatively small to the size of input image; however, the effectiveness of adversarial patch attack has never been experimented on deep reinforcement learning algorithms. We design algorithms to generate adversarial patches to attack two types of deep reinforcement learning algorithms, including deep Q-networks (DQN) and proximal policy optimization (PPO). Our algorithms of generating adversarial patch consist of two parts: choosing attack position and training adversarial patch on that position. Under the same bound of total …