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Articles 1 - 5 of 5
Full-Text Articles in Engineering
Secure Mobile Computing By Using Convolutional And Capsule Deep Neural Networks, Rui Ning
Secure Mobile Computing By Using Convolutional And Capsule Deep Neural Networks, Rui Ning
Electrical & Computer Engineering Theses & Dissertations
Mobile devices are becoming smarter to satisfy modern user's increasing needs better, which is achieved by equipping divers of sensors and integrating the most cutting-edge Deep Learning (DL) techniques. As a sophisticated system, it is often vulnerable to multiple attacks (side-channel attacks, neural backdoor, etc.). This dissertation proposes solutions to maintain the cyber-hygiene of the DL-Based smartphone system by exploring possible vulnerabilities and developing countermeasures.
First, I actively explore possible vulnerabilities on the DL-Based smartphone system to develop proactive defense mechanisms. I discover a new side-channel attack on smartphones using the unrestricted magnetic sensor data. I demonstrate that attackers can …
Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne
Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne
Electrical & Computer Engineering Theses & Dissertations
Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in …
Virtual Satcom, Long Range Broadband Digital Communications, Dennis George Watson
Virtual Satcom, Long Range Broadband Digital Communications, Dennis George Watson
Electrical & Computer Engineering Theses & Dissertations
The current naval strategy is based on a distributed force, networked together with high-speed communications that enable operations as an intelligent, fast maneuvering force. Satellites, the existing network connector, are weak and vulnerable to attack. HF is an alternative, but it does not have the information throughput to meet the distributed warfighting need. The US Navy does not have a solution to reduce dependency on space-based communication systems while providing the warfighter with the required information speed.
Virtual SATCOM is a solution that can match satellite communications (SATCOM) data speed without the vulnerable satellite. It is wireless communication on a …
Topology Control, Scheduling, And Spectrum Sensing In 5g Networks, Prosanta Paul
Topology Control, Scheduling, And Spectrum Sensing In 5g Networks, Prosanta Paul
Electrical & Computer Engineering Theses & Dissertations
The proliferation of intelligent wireless devices is remarkable. To address phenomenal traffic growth, a key objective of next-generation wireless networks such as 5G is to provide significantly larger bandwidth. To this end, the millimeter wave (mmWave) band (20 GHz -300 GHz) has been identified as a promising candidate for 5G and WiFi networks to support user data rates of multi-gigabits per second. However, path loss at mmWave is significantly higher than today's cellular bands. Fortunately, this higher path loss can be compensated through the antenna beamforming technique-a transmitter focuses a signal towards a specific direction to achieve high signal gain …
Longitudinal Brain Tumor Tracking, Tumor Grading, And Patient Survival Prediction Using Mri, Linmin Pei
Longitudinal Brain Tumor Tracking, Tumor Grading, And Patient Survival Prediction Using Mri, Linmin Pei
Electrical & Computer Engineering Theses & Dissertations
This work aims to develop novel methods for brain tumor classification, longitudinal brain tumor tracking, and patient survival prediction. Consequently, this dissertation proposes three tasks. First, we develop a framework for brain tumor segmentation prediction in longitudinal multimodal magnetic resonance imaging (mMRI) scans, comprising two methods: feature fusion and joint label fusion (JLF). The first method fuses stochastic multi-resolution texture features with tumor cell density features, in order to obtain tumor segmentation predictions in follow-up scans from a baseline pre-operative timepoint. The second method utilizes JLF to combine segmentation labels obtained from (i) the stochastic texture feature-based and Random Forest …