Signal Processing Commons^™

Design, Analysis, Implementation And Evaluation Of Real-Time Opportunistic Spectrum Access In Cloud-Based Cognitive Radio Networks, Nimish Sharma

College of Graduate Studies: Theses & Dissertations

Opportunistic spectrum access in cognitive radio network is proposed for remediation of spectrum under-utilization caused by exclusive licensing for service providers that are intermittently utilizing spectrum at any given geolocation and time. The unlicensed secondary users (SUs) rely on opportunistic spectrum access to maximize spectrum utilization by sensing/identifying the idle bands without causing harmful interference to licensed primary users (PUs). In this thesis, Real-time Opportunistic Spectrum Access in Cloud-based Cognitive Radio Networks (ROAR) architecture is presented where cloud computing is used for processing and storage of idle channels. Software-defined radios (SDRs) are used as SUs and PUs that identify, report, …

Performance Analysis Of Secondary Users In Heterogeneous Cognitive Radio Network, Tanjil Amin

College of Graduate Studies: Theses & Dissertations

Continuous increase in wireless subscriptions and static allocation of wireless frequency bands to the primary users (PUs) are fueling the radio frequency (RF) shortage problem. Cognitive radio network (CRN) is regarded as a solution to this problem as it utilizes the scarce RF in an opportunisticmanner to increase the spectrumefficiency. InCRN, secondary users (SUs) are allowed to access idle frequency bands opportunistically without causing harmful interference to the PUs. In CRN, the SUs determine the presence of PUs through spectrum sensing and access idle bands by means of dynamic spectrum access. Spectrum sensing techniques available in the literature do not …

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 …

High Resolution Time-Of-Arrival Ranging Of Wireless Sensor Nodes In Non-Homogenous Environments, Mohsen Jamalabdollahi

Dissertations, Master's Theses and Master's Reports

Wireless Sensor Networks (WSN) have emerging applications in homogeneous environments such as free space. In addition, WSNs are finding new applications in non-homogeneous (NH) media. All referred applications entail location information of measured data or observed event. Localization in WSNs is considered as the leading remedy, which refers to the procedure of obtaining the sensor nodes relative location utilizing range measurements. Localization via Time-of-Arrival (ToA) estimation has received considerable attention because of high precision and low complexity implementation, however, the traditional techniques are not feasible in NH media due to frequency dispersion of transmitted ranging waveform.

In this work, a …

Machine Learning Through Mimicry And Association, Nickolas S. Holcomb

Honors College Theses

Adaptability is a key missing features that has impeded the growth of assistive robotics. In the traditional model, all actions must be explicitly coded by a skilled programmer familiar with the hardware. This project explores a method of teaching a webcam equipped arm type robot new primitive movement using visual demonstrations.

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 …

Information-Theoretic Secure Outsourced Computation In Distributed Systems, Zhaohong Wang

Theses and Dissertations--Electrical and Computer Engineering

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir's secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using …

Microstrip Patch Antenna Design And Analysis For Wireless Power Applications, Zachary Pinz

Summer Community of Scholars Posters (RCEU and HCR Combined Programs)

No abstract provided.

Analysis Of Vocal Fold Kinematics Using High Speed Video, Harikrishnan Unnikrishnan

Theses and Dissertations--Electrical and Computer Engineering

Vocal folds are the twin in-folding of the mucous membrane stretched horizontally across the larynx. They vibrate modulating the constant air flow initiated from the lungs. The pulsating pressure wave blowing through the glottis is thus the source for voiced speech production. Study of vocal fold dynamics during voicing are critical for the treatment of voice pathologies. Since the vocal folds move at 100 - 350 cycles per second, their visual inspection is currently done by strobosocopy which merges information from multiple cycles to present an apparent motion. High Speed Digital Laryngeal Imaging(HSDLI) with a temporal resolution of up to …

Robust Background Subtraction For Moving Cameras And Their Applications In Ego-Vision Systems, Hasan Sajid

Theses and Dissertations--Electrical and Computer Engineering

Background subtraction is the algorithmic process that segments out the region of interest often known as foreground from the background. Extensive literature and numerous algorithms exist in this domain, but most research have focused on videos captured by static cameras. The proliferation of portable platforms equipped with cameras has resulted in a large amount of video data being generated from moving cameras. This motivates the need for foundational algorithms for foreground/background segmentation in videos from moving cameras. In this dissertation, I propose three new types of background subtraction algorithms for moving cameras based on appearance, motion, and a combination of …

Wearable Privacy Protection With Visual Bubble, Shaoqian Wang

Theses and Dissertations--Electrical and Computer Engineering

Wearable cameras are increasingly used in many different applications such as entertainment, security, law enforcement and healthcare. In this thesis, we focus on the application of the police worn body camera and behavioral recording using a wearable camera for one-on-one therapy with a child in a classroom or clinic. To protect the privacy of other individuals in the same environment, we introduce a new visual privacy protection technique called visual bubble. Visual bubble is a virtual zone centered around the camera for observation whereas the rest of the environment and people are obfuscated. In contrast to most existing visual privacy …

Design, Analysis And Evaluation Of Unmanned Aerial Vehicle Ad Hoc Network For Emergency Response Communications, Robin D. Grodi

College of Graduate Studies: Theses & Dissertations

In any emergency situation, it is paramount that communication be established between those affected by an emergency and the emergency responders. This communication is typically initiated by contacting an emergency service number such as 9-1-1 which will then notify the appropriate responders. The communication link relies heavily on the use of the public telephone network. If an emergency situation causes damage to, or otherwise interrupts, the public telephone network then those affected by the emergency are unable to call for help or warn others. A backup emergency response communication system is required to restore communication in areas where the public …

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 …

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 …

Monitoring Voip Speech Quality For Chopped And Clipped Speech, Andrew Hines, Jan Skoglund, Anil C. Kokaram, Naomi Harte

Articles

No abstract provided.

Real-Time Digital Effects Processing Using Ios, Jonah W. Clinard

Computer Engineering

In today’s society, we are seeing incredible improvements in terms of creating smaller technological devices that behave more and more like the personal computers of yesterday. Mobile “Smart” devices, in particular, are becoming incredibly powerful not just in terms of processing power, but in the fact that they are able to provide assistance to users in their everyday lives. Application developers are now able utilize the power and size of these devices, to create and realize ideas that would have been previously viewed as impossible. This project applies the fields of digital signal processing, music, and mobile application development, to …

Hvdc Systems Fault Analysis Using Various Signal Processing Techniques, Benish Paily

Doctoral

The detection and fast clearance of faults are important for the safe and optimal operation of HVDC systems. In HVDC systems, various types of AC faults (rectifier & inverter side) and DC faults can occur. It is therefore necessary to detect the faults and classify them for better protection and diagnostics purposes. Various techniques for fault detection and classification in HVDC systems using signal processing techniques are presented and investigated in this research work. In this research work, it is shown that the wavelet transformation can effectively detect abrupt changes in system signals which are indicative of a fault. This …

Medical Image Registration Using Artificial Neural Network, Hyunjong Choi

Master's Theses

Image registration is the transformation of different sets of images into one coordinate system in order to align and overlay multiple images. Image registration is used in many fields such as medical imaging, remote sensing, and computer vision. It is very important in medical research, where multiple images are acquired from different sensors at various points in time. This allows doctors to monitor the effects of treatments on patients in a certain region of interest over time. In this thesis, artificial neural networks with curvelet keypoints are used to estimate the parameters of registration. Simulations show that the curvelet keypoints …

Scaled Synthetic Aperture Radar System Development, Ryan K. Green

Master's Theses

Synthetic Aperture Radar (SAR) systems generate two dimensional images of a target area using RF energy as opposed to light waves used by cameras. When cloud cover or other optical obstructions prevent camera imaging over a target area, SAR can be substituted to generate high resolution images. Linear frequency modulated signals are transmitted and received while a moving imaging platform traverses a target area to develop high resolution images through modern digital signal processing (DSP) techniques. The motivation for this joint thesis project is to design and construct a scaled SAR system to support Cal Poly radar projects. Objectives include …

Gaussian Nonlinear Line Attractor For Learning Multidimensional Data, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla

Electrical and Computer Engineering Faculty Publications

The human brain’s ability to extract information from multidimensional data modeled by the Nonlinear Line Attractor (NLA), where nodes are connected by polynomial weight sets. Neuron connections in this architecture assumes complete connectivity with all other neurons, thus creating a huge web of connections. We envision that each neuron should be connected to a group of surrounding neurons with weighted connection strengths that reduces with proximity to the neuron. To develop the weighted NLA architecture, we use a Gaussian weighting strategy to model the proximity, which will also reduce the computation times significantly.

Once all data has been trained in …