Engineering Commons^™

Wifi Sensing At The Edge Towards Scalable On-Device Wireless Sensing Systems, Steven M. Hernandez

Theses and Dissertations

WiFi sensing offers a powerful method for tracking physical activities using the radio-frequency signals already found throughout our homes and offices. This novel sensing modality offers continuous and non-intrusive activity tracking since sensing can be performed (i) without requiring wearable sensors, (ii) outside the line-of-sight, and even (iii) through the wall. Furthermore, WiFi has become a ubiquitous technology in our computers, our smartphones, and even in low-cost Internet of Things devices. In this work, we consider how the ubiquity of these low-cost WiFi devices offer an unparalleled opportunity for improving the scalability of wireless sensing systems. Thus far, WiFi sensing …

Deep Learning Assisted Intelligent Visual And Vehicle Tracking Systems, Liang Xu

Theses and Dissertations

Sensor fusion and tracking is the ability to bring together measurements from multiple sensors of the current and past time to estimate the current state of a system. The resulting state estimate is more accurate compared with the direct sensor measurement because it balances between the state prediction based on the assumed motion model and the noisy sensor measurement. Systems can then use the information provided by the sensor fusion and tracking process to support more-intelligent actions and achieve autonomy in a system like an autonomous vehicle. In the past, widely used sensor data are structured, which can be directly …

Sensor Placement For Damage Localization In Sensor Networks, Fereshteh Firouzi

Theses and Dissertations

The objective of this thesis is to formulate and solve the sensor placement problem for damage localization in a sensor network. A Bayesian estimation problem is formulated with the time-of-flight (ToF) measurements. In this model, ToF of lamb waves, which are generated and received by piezoelectric sensors, is the total time for each wave to be transmitted, reflected by the target, and received by the sensor. The ToF of the scattered lamb wave has characteristic information about the target location. By using the measurement model and prior information, the target location is estimated in a centralized sensor network with a …

Respiratory Prediction And Image Quality Improvement Of 4d Cone Beam Ct And Mri For Lung Tumor Treatments, Seonyeong Park

Theses and Dissertations

Identification of accurate tumor location and shape is highly important in lung cancer radiotherapy, to improve the treatment quality by reducing dose delivery errors. Because a lung tumor moves with the patient's respiration, breathing motion should be correctly analyzed and predicted during the treatment for prevention of tumor miss or undesirable treatment toxicity. Besides, in Image-Guided Radiation Therapy (IGRT), the tumor motion causes difficulties not only in delivering accurate dose, but also in assuring superior quality of imaging techniques such as four-dimensional (4D) Cone Beam Computed Tomography (CBCT) and 4D Magnetic Resonance Imaging (MRI). Specifically, 4D CBCT used in CBCT …

Accurate Acoustic Ranging System Using Android Smartphones, Mohammadbagher Fotouhi

Theses and Dissertations

ACCURATE ACOUSTIC RANGING SYSTEM USING ANDROID SMARTPHONES

By Mohammadbagher Fotouhi, Master of Science

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University

Virginia Commonwealth University 2017

Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering

In this thesis, we present the design, implementation, and evaluation of an android ranging system, a high-accuracy acoustic-based ranging system which allows two android mobile phones to learn their physical distance from each other.

In this system we propose a practical solution for accurate ranging based on acoustic communication …

Pattern Recognition In Class Imbalanced Datasets, Nahian A. Siddique

Theses and Dissertations

Class imbalanced datasets constitute a significant portion of the machine learning problems of interest, where recog­nizing the ‘rare class’ is the primary objective for most applications. Traditional linear machine learning algorithms are often not effective in recognizing the rare class. In this research work, a specifically optimized feed-forward artificial neural network (ANN) is proposed and developed to train from moderate to highly imbalanced datasets.

The proposed methodology deals with the difficulty in classification task in multiple stages—by optimizing the training dataset, modifying kernel function to generate the gram matrix and optimizing the NN structure. First, the training dataset is extracted …

Resilient Dynamic State Estimation In The Presence Of False Information Injection Attacks, Jingyang Lu

Theses and Dissertations

The impact of false information injection is investigated for linear dynamic systems with multiple sensors. First, it is assumed that the system is unaware of the existence of false information and the adversary is trying to maximize the negative effect of the false information on Kalman filter's estimation performance under a power constraint. The false information attack under different conditions is mathematically characterized. For the adversary, many closed-form results for the optimal attack strategies that maximize the Kalman filter's estimation error are theoretically derived. It is shown that by choosing the optimal correlation coefficients among the false information and allocating …

Distributed Sparse Signal Recovery In Networked Systems, Puxiao Han

Theses and Dissertations

In this dissertation, two classes of distributed algorithms are developed for sparse signal recovery in large sensor networks. All the proposed approaches consist of local computation (LC) and global computation (GC) steps carried out by a group of distributed local sensors, and do not require the local sensors to know the global sensing matrix. These algorithms are based on the original approximate message passing (AMP) and iterative hard thresholding (IHT) algorithms in the area of compressed sensing (CS), also known as sparse signal recovery. For distributed AMP (DiAMP), we develop a communication-efficient algorithm GCAMP. Numerical results demonstrate that it outperforms …

Joint Detection-State Estimation And Secure Signal Processing, Mengqi Ren

Theses and Dissertations

In this dissertation, joint detection-state estimation and secure signal processing are studied. Detection and state estimation are two important research topics in surveillance systems. The detection problems investigated in this dissertation include object detection and fault detection. The goal of object detection is to determine the presence or absence of an object under measurement uncertainty. The aim of fault detection is to determine whether or not the measurements are provided by faulty sensors. State estimation is to estimate the states of moving objects from measurements with random measurement noise or disturbance, which typically consist of their positions and velocities over …

Censoring And Fusion In Non-Linear Distributed Tracking Systems With Application To 2d Radar, Armond S. Conte Ii

Theses and Dissertations

The objective of this research is to study various methods for censoring state estimate updates generated from radar measurements. The generated 2-D radar data are sent to a fusion center using the J-Divergence metric as the means to assess the quality of the data. Three different distributed sensor network architectures are considered which include different levels of feedback. The Extended Kalman Filter (EKF) and the Gaussian Particle Filter (GPF) were used in order to test the censoring methods in scenarios which vary in their degrees of non-linearity. A derivation for the direct calculation of the J-Divergence using a particle filter …

Mobile Indoor Positioning For Augmented Reality Systems, Robert B. Glass

Theses and Dissertations

This thesis explores the creation and setup of a prototype that allows users of the device to interact within an indoor real world environment and a virtual environment simultaneously using high-tech common technology. The prototype is comprised of a small mobile device such as a cellular mobile phone, Raspberry Pi computer, a battery powered handheld Pico projector, and software developed for the Android OS. The software can easily be ported to other mobile and non-mobile operating systems. The mobile device must contain accelerometer, magnetometer, and gyroscope embedded sensors as well as 802.11 wireless network chip. The prototype software implements an …