Defense Notices

All students and faculty are welcome to attend the final defense of EECS graduate students completing their M.S. or Ph.D. degrees. Defense notices for M.S./Ph.D. presentations for this year and several previous years are listed below in reverse chronological order.

Students who are nearing the completion of their M.S./Ph.D. research should schedule their final defenses through the EECS graduate office at least THREE WEEKS PRIOR to their presentation date so that there is time to complete the degree requirements check, and post the presentation announcement online.

Upcoming Defense Notices

David Felton

Optimization and Evaluation of Physical Complementary Radar Waveforms

When & Where:


Nichols Hall, Room 129 (Apollo Auditorium)

Committee Members:

Shannon Blunt, Chair
Rachel Jarvis
Patrick McCormick
James Stiles
Zsolt Talata

Abstract

The RF spectrum is a precious, finite resource with ever-increasing demand. Consequently, the mandate to be a "good spectral neighbor" is in direct conflict with the requirements for high-performance sensing where correlation error is fundamentally limited. As such, matched-filter radar performance is often sidelobe-limited with estimation error being constrained by the time-bandwidth (TB) of the collective emission. The methods developed here seek to bridge this gap between idealized radar performance and practical utility via waveform design.    

Estimation error becomes more complex when employing pulse-agility. In doing so, range-sidelobe modulation (RSM) spreads energy across Doppler, rendering traditional methods ineffective. To address this, the gradient-based complementary-FM framework was developed to produce complementary sidelobe cancellation (CSC) after coherently combining subsets within a pulse-agile emission. In contrast to the majority of complementary signals, explored via phase-coding, these Comp-FM waveform subsets achieve CSC while preserving hardware-compatibility since they are FM (though design distortion is never completely avoided). Although Comp-FM addressed practicality via hardware amenability, CSC was localized to zero-Doppler. This work expands the Comp-FM notion to a Doppler-generalized (DG) framework, extending the cancellation condition to an arbitrary span. The same framework can likewise be employed to jointly optimize an entire coherent processing interval (CPI) to minimize RSM within the radar point-spread-function (PSF), thereby generalizing the notion of complementarity and introducing the potential for cognitive operation if sufficient scattering knowledge is available a-priori.          

Sensing with a single emitter is limited by self-inflicted error alone (e.g., clutter, sidelobes), while MIMO systems must additionally contend with the cross-responses from emitters operating concurrently (e.g., simultaneously, spatially proximate, in a shared spectrum), further degrading radar sensitivity. Now, total correlation error is dictated by the overlapping TB (i.e., how coincident are the signals) and number of operating emitters, compounding difficulty to estimate if left unaddressed. As such, the determination of "orthogonal waveforms" comprises a large portion of MIMO literature, though remains a phenomenological misnomer for pulsed emissions. Here, the notion of complementary-FM is applied to a multi-emitter context in which transmitter-amenable quasi-orthogonal subsets, occupying the same spectral band, are produced via a similar gradient-based approach. To further practicalize these MIMO-Comp-FM waveform subsets, the same "DG" approach described above, addressing the otherwise-default Doppler-induced degradation of complementary signals, is applied. In doing so, Doppler-independent separability and complementarity greatly improves estimation sensitivity for multi-emitter systems. 

This MIMO-Comp-FM framework is developed for standard matched filter processing. Coupling this framework with a "DG" form of the previously explored MIMO-MiCRFt is also investigated, illustrating the added benefit of pairing optimized subsets with similarly calibrated processing. 

Each of these methods is developed to address unique and increasingly complex sources of estimation error. All approaches are initially developed and evaluated via simulated analysis where ground-truth is known. Then, despite hardware-induced distortion being unavoidable, the MIMO-Comp-FM framework is confirmed via loopback measurements to preserve the majority of CSC that was observed in simulation. Finally, open-air demonstration of each approach validates practical utility on a radar system.

Hao Xuan

Toward an Integrated Computational Framework for Metagenomics: From Sequence Alignment to Automated Knowledge Discovery

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Committee Members:

Cuncong Zhong, Chair
Fengjun Li
Suzanne Shontz
Hongyang Sun
Liang Xu

Abstract

Metagenomic sequencing has become a central paradigm for studying complex microbial communities and their interactions with the host, with emerging applications in clinical prediction and disease modeling. In this work, we first investigate two representative application scenarios: predicting immune checkpoint inhibitor response in non-small cell lung cancer using gut microbial signatures, and characterizing host–microbiome interactions in neonatal systems. The proposed reference-free neural network captures both compositional and functional signals without reliance on reference genomes, while the neonatal study demonstrates how environmental and genetic factors reshape microbial communities and how probiotic intervention can mitigate pathogen-induced immune activation.

These studies highlight both the promise and the inherent difficulty of metagenomic analysis: transforming raw sequencing data into clinically actionable insights remains an algorithmically fragmented and computationally intensive process. This challenge arises from two key limitations: the lack of a unified algorithmic foundation for sequence alignment and the absence of systematic approaches for selecting and organizing analytical tools. Motivated by these challenges, we present a unified computational framework for metagenomic analysis that integrates complementary algorithmic and systems-level solutions.

First, to resolve fragmentation at the alignment level, we develop the Versatile Alignment Toolkit (VAT), a unified algorithmic system for biological sequence alignment across diverse applications. VAT introduces an asymmetric multi-view k-mer indexing scheme that integrates multiple seeding strategies within a single architecture and enables dynamic seed-length adjustment via longest common prefix (LCP)–based inference without re-indexing. A flexible seed-chaining mechanism further supports diverse alignment scenarios, including collinear, rearranged, and split alignments. Combined with a hardware-efficient in-register bitonic sorting algorithm and dynamic index-loading strategy, VAT achieves high efficiency and broad applicability across read mapping, homology search, and whole-genome alignment. Second, to address the challenge of tool selection and pipeline construction, we develop SNAIL, a natural language processing system for automated recognition of bioinformatics tools from large-scale and rapidly growing scientific literature. By integrating XGBoost and Transformer-based models such as SciBERT, SNAIL enables structured extraction of analytical tools and supports automated, reproducible pipeline construction.

Together, this work establishes a unified framework that is grounded in real-world applications and addresses key bottlenecks in metagenomic analysis, enabling more efficient, scalable, and clinically actionable workflows.

Pramil Paudel

Learning Without Seeing: Privacy-Preserving and Adversarial Perspectives in Lensless Imaging

When & Where:


Eaton Hall, Room 2001B

Committee Members:

Fengjun Li, Chair
Alex Bardas
Bo Luo
Cuncong Zhong
Haiyang Chao

Abstract

Conventional computer vision relies on spatially resolved, human-interpretable images, which inherently expose sensitive information and raise privacy concerns. In this study, we explore an alternative paradigm based on lensless imaging, where scenes are captured as diffraction patterns governed by the point spread function (PSF). Although unintelligible to humans, these measurements encode structured, distributed information that remains useful for computational inference. 

We propose a unified framework for privacy-preserving vision that operates directly on lensless sensor measurements by leveraging their frequency-domain and phase-encoded properties. The framework is developed along two complementary directions. First, we enable reconstruction-free inference by exploiting the intrinsic obfuscation of lensless data. We show that semantic tasks such as classification can be performed directly on diffraction patterns using models tailored to non-local, phase-scrambled representations. We further design lensless-aware architectures and integrate them into practical pipelines, including a Swin Transformer-based steganographic framework (DiffHide) for secure and imperceptible information embedding. To assess robustness, we formalize adversarial threat models and develop defenses against learning-based reconstruction attacks, particularly GAN-driven inversion. Second, we investigate the limits of privacy by studying the reconstructability of lensless measurements without explicit knowledge of the forward model. We develop learning-based reconstruction methods that approximate the inverse mapping and analyze conditions under which sensitive information can be recovered. Our results demonstrate that lensless measurements enable effective vision tasks without reconstruction, while providing a principled framework to evaluate and mitigate privacy risks. 

Sharmila Raisa

Digital Coherent Optical System: Investigation and Monitoring

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Committee Members:

Rongqing Hui, Chair
Morteza Hashemi
Erik Perrins
Alessandro Salandrino
Jie Han

Abstract

Coherent wavelength-division multiplexed (WDM) optical fiber systems have become the primary transmission technology for high-capacity data networks, driven by the explosive bandwidth demand of cloud computing, streaming services, and large-scale artificial intelligence training infrastructure. This dissertation investigates two fundamental aspects of digital coherent fiber optic systems under the unifying theme of source and monitoring: the design of multi-wavelength optical sources compatible with high-order coherent detection, and the leveraging of fiber Kerr-effect nonlinearity at the coherent receiver to perform physical-layer link health monitoring and to assess inherent security vulnerabilities — both achieved through digital signal processing of the received complex optical field without dedicated hardware.

We begin by addressing the multi-wavelength transmitter challenge in WDM coherent systems. Existing quantum-dot, quantum-dash, and quantum-well based optical frequency comb (OFC) sources share a common limitation: individual comb line linewidths in the tens of MHz range caused by low output power levels of 1–20 mW, making them incompatible with high-order coherent detection. We demonstrate coherent system application of a single-section InGaAsP QW Fabry-Perot laser diode with greater than 120 mW optical power at the fiber pigtail and 36.14 GHz mode spacing. The high optical power per mode produces Lorentzian equivalent linewidths below 100 kHz — compatible with 16-QAM carrier phase recovery without optical phase locking. Experimental results obtained using a commercial Ciena WaveLogic-Ai coherent transceiver demonstrate 20-channel WDM transmission over 78.3 km of standard single-mode fiber with all channels below the HD-FEC threshold of 3.8 × 10⁻³ at 30 GBaud differential-coded 16-QAM, corresponding to an aggregate capacity of 2.15 Tb/s from a single laser device.

After investigating the QW Fabry-Perot laser as a multi-wavelength source for coherent WDM transmission, we leverage the coherent receiver DSP to exploit fiber Kerr-effect nonlinearity for longitudinal power profile estimation, enabling reconstruction of the signal power distribution P(z) along the full multi-span link without dedicated hardware or traffic interruption. We propose a modified enhanced regular perturbation (ERP) method that corrects two independent physical error sources of the standard RP1 least-squares baseline: the accumulated nonlinear phase rotation, and the dispersion-mediated phase-to-intensity conversion — a second bias source not addressed by prior methods. The RP1 method produces mean absolute error (MAE) that scales quadratically with span count, growing to 1.656 dB at 10 spans and 3 dBm. The modified ERP reduces this to 0.608 dB — an improvement that grows consistently with link length, confirming increasing advantage in the long-haul regime. Extension to WDM through an XPM-aware per-channel formulation achieves MAE of 0.113–0.419 dB across 150–500 km link lengths.

In addition to its role in enabling DSP-based longitudinal power profile estimation, the fiber Kerr-effect nonlinearity is shown to give rise to an inherent physical-layer security vulnerability in coherent WDM systems. We show that an eavesdropper co-tenanting a shared fiber — transmitting a continuous-wave probe at a wavelength adjacent to the legitimate signal — can capture the XPM-induced waveform at the fiber output and apply a bidirectional gated recurrent unit neural network, trained on split-step Fourier method simulation data, to reconstruct the transmitted symbol sequence without physical fiber access and without perturbing the legitimate signal. This eavesdropping mechanism is experimentally validated using a commercial Ciena WaveLogic-Ai coherent transceiver for ASK, BPSK, QPSK, and 16-QAM modulation formats at 4.26 GBaud and 8.56 GBaud over one- and two-span 75 km fiber systems, achieving zero symbol errors under high-OSNR conditions. Noise-aware training over OSNR from 20 to 60 dB maintains symbol error rate below 10⁻² for OSNR above 25–30 dB.

Together, these three contributions demonstrate that the coherent fiber optic system is a versatile physical instrument extending well beyond its role as a data transmission medium. The coherent receiver infrastructure — deployed for high-order modulation and data recovery — simultaneously enables the high-power OFC laser to serve as a practical multi-wavelength transmitter source, and provides the complex field measurement capability through which fiber Kerr-effect nonlinearity can be exploited constructively for distributed link monitoring and, as a direct consequence, reveals an inherent physical-layer security exposure in shared fiber infrastructure. This unified perspective on the coherent system as both a transmission platform and a general-purpose measurement instrument has direct relevance to the design of spectrally efficient, self-monitoring, and physically secure optical interconnects for next-generation AI computing networks.

Past Defense Notices

Dates

DAIN VERMAAK

Modeling, Visualizing, and Analyzing Student Progress on Learning Maps

When & Where:


2001 B Eaton Hall

Committee Members:

James Miller, Chair
Man Kong
Suzanne Shontz
Guanghui Wang
Bruce Frey

Abstract

A learning map is an unweighted directed graph containing relationships between discrete skills and concepts with edges defining the prerequisite hierarchy. They arose as a means of connecting student instruction directly to standards and curriculum and are designed to assist teachers in lesson planning and evaluating student response. As learning maps gain popularity there is an increasing need for teachers to quickly evaluate which nodes have been mastered by their students. Psychometrics is a field focused on measuring student performance and includes the development of processes used to link a student's response to multiple choice questions directly to their understanding of concepts. This dissertation focuses on developing modeling and visualization capabilities to enable efficient analysis of data pertaining to student understanding generated by psychometric techniques.

Such analysis naturally includes that done by classroom teachers. Visual solutions to this problem clearly indicate the current understanding of a student or classroom in such a way as to make suggestions that can guide future learning. In response to these requirements we present various experimental approaches which augment the original learning map design with targeted visual variables. Particular attention is given to variable selection and their effect on the usability of the resulting graphics.

As well as looking forward, we also consider methods by which data visualization can be used to evaluate and improve existing teaching methods. We present several graphics based on modelling student progression as information flow. These methods rely on conservation of data to increase edge information, reducing the load carried by the nodes and encouraging path comparison.

Finally, we propose a means of combining features of key experimental approaches to design a single graphic capable of meeting both the predictive and validation requirements. We also propose several methods to measure the effectiveness and correctness of the final design.

HAMID MAHMOUDI

Novel Predictive Control Strategies in Power Electronics Systems

When & Where:


2001 B Eaton Hall

Committee Members:

Reza Ahmadi, Chair
Christopher Allen
Alessandro Salandrino
James Stiles
Shawn Keshmiri

Abstract

This work proposes several advanced predictive switching algorithms and modulation methods for power electronics converters based on model predictive control (MPC) paradigm. The proposed methods retain the advantages of conventional MPC methods in programing the nonlinear effects of the converter into the design calculations to improve the overall dynamic performance and steady state operation of the system. Besides, the proposed methods provide a fixed switching frequency operation of the system, which results in regulating the system objectives with minimized ripple. In the first part of this work, a new modulation based MPC technique is proposed. The proposed method provides flexibility to prioritize different objectives of the system against each other using weighting factors. To further evaluate the merits of the proposed method, it has been used to control modular multilevel converters (MMCs) in voltage-source-converter high-voltage-DC (VSC-HVDC) systems. The proposed method minimizes the line total harmonic distortion (THD), circulating current ripple and steady-state error.  Furthermore, a new Finite-Control-Set MPC (FCS-MPC) method for controlling MMCs with minimized computational burden is proposed that doesn’t employ weighting factors to control different system objectives.

Furthermore, a Modulated MPC (MMPC) based control system for a Z-source Inverter (ZSI) based Permanent Magnet Synchronous Motor (PMSM) drive system is proposed. The Proposed method uses two separate MMPC loops for the Z-source network and PMSM control.  For the Z-source network, a cascaded MMPC control scheme has been proposed and for the PMSM, a novel MMPC controller is proposed that predicts the future value of PMSM current vectors, selects specific current vectors that minimize a certain cost function the most, and performs modulation between them.

Finally, a torque ripple minimization method for a PMSM drive system that utilizes a modified quasi-Z-source (qZS) inverter which provides a wider range of capabilities for inverter input voltage control is proposed. It also allows for modification of the traditional switching sequence selection scheme when using the Space Vector Modulation (SVM) for switching. The provided flexibilities are leveraged to develop a control system that minimizes the torque ripples during PMSM operation while satisfying conventional control objectives such as shaft speed control.

 

SALLY SAJADIAN

Model Predictive Control of Impedance Source Inverter for Photovoltaic Applications

When & Where:


2001B Eaton Hall

Committee Members:

Reza Ahmadi, Chair
Glenn Prescott
Alessandro Salandrino
Jim Stiles
Huazhen Fang

Abstract

A model predictive controlled power electronics interface (PEI) based on impedance source inverter for photovoltaic (PV) applications is proposed in this dissertation. The proposed system has the capability of operation in both grid-connected and islanded mode. Firstly, a model predictive based maximum power point tracking (MPPT) method is proposed for PV applications based on single stage grid-connected Z-source inverter (ZSI). This technique predicts the future behavior of the PV side voltage and current using a digital observer that estimates the parameters of the PV module. The proposed method adaptively updates the perturbation size in the PV voltage using the predicted model of the system to reduce oscillations and increase convergence speed. The experimental results demonstrate fast dynamic response to changes in solar irradiance level, small oscillations around maximum power point at steady-state, and high MPPT efficacy. 

The second part of this dissertation focuses on the dual-mode operation of the proposed PEI based on ZSI with capability to operate in islanded and grid-connected mode. The transition from islanded to grid-connected mode and vice versa can cause significant deviation in voltage and current due to mismatch in phase, frequency, and amplitude of voltages. The proposed controller using MPC offers seamless transition between the two modes of operations. The proposed direct decoupled active and reactive power control in grid‑connected mode enables the dual-mode ZSI to behave as a power conditioning unit for ancillary services.

The final part of this dissertation focuses on the low voltage ride through (LVRT) capability of the proposed PV systems during grid faults such as voltage sag.  In normal grid condition mode, the maximum available power from the PV panels is injected into the grid. In this mode, the system can provide reactive power compensation as a power conditioning unit for ancillary services from DG systems to main ac grid. In case of grid faults, the proposed system changes the behavior of reactive power injection into the grid for LVRT operation according to the grid requirements. Thus, the proposed controller for ZSI is taking into account both the power quality issues and reactive power injection under abnormal grid conditions. 

APOORV INGLE

QuB: A Resource Aware Functional Programming Language

When & Where:


2001B Eaton Hall

Committee Members:

Garrett Morris, Chair
Perry Alexander
Andy Gill
Prasad Kulkarni

Abstract

Modern programming languages treat resources as normal values. The static semantics of resources in such 

languages does not match their runtime semantics. In this thesis, we tackle the resource management problem 

by making resources first class citizens in the language, and concentrating on sharing or separation of resources.

 

We design and implement QuB (pronounced: cube), a Curry-Howard interpretation of logic of bunched implications (BI). 

We distinguish two kinds of values—restricted and unrestricted—and two kinds of function implications— sharing and separating. 

The restricted values model resources while the unrestricted values model program objects that do not contain any resources. 

Sharing functions denote that functions share resources with its arguments, while separating functions denote that functions do not 

share resources with its arguments. We show how the use of monads with sharing and separating functions helps in modeling 

patterns, such as exception handling, that are difficult to express in linear languages, .

MANJISH ADHIKARI

Basal Conditions of Petermann Glacier and Jakobshavn Isbrae derived from Airborne Ice Penetrating Radar Measurements

When & Where:


317 Nichols Hall

Committee Members:

Carl Leuschen, Chair
Jilu Li
Christopher Allen
John Paden

Abstract

Understanding ice dynamics and ice basal conditions is important because of their impacts on sea level rise. Radio echo sounding has been extensively used for characterizing the ice sheets. The radar reflectivity of the ice bed is of special importance because it can discriminate frozen and thawed ice beds. The knowledge of spatial distribution of basal water is crucial in explaining the flow velocity and stability of glaciers and ice sheets. Basal echo reflectivity used to identify the areas of basal melting can be calculated by compensating ice bed power for geometric losses, rough interface losses, system losses and englacial attenuation.

Two important outlet glaciers of Greenland, Petermann glacier and Jakobshavn isbrae have been losing a lot of ice mass in recent years, and are therefore studied to derive its basal conditions from airborne radar surveys in this thesis.

The ice surface and bed roughness of these glaciers are estimated using Radar Statistical Reconnaissance (RSR) method, and validated using roughness derived from NASA’s Airborne Topographic Mapper (ATM) and Ku band altimeter. Englacial attenuation is modelled using Schroeder’s variable attenuation method. After compensating for these losses, the basal reflectivity for the two glaciers is estimated, and validated using cross over analysis, geophysics, hydraulic potential, abruptive index and coherence index.

The areas of basal melting i.e. areas with higher reflectivity are identified. Petermann glacier is found to have alternate frozen and thawed regions explaining the process of ice movement by friction and freezing. Due to the lack of topographic pinning the glacier is subject to higher ice flow speed. Jakobshavn glacier has several areas of basal melting scattered in the catchment area with most concentration near the glacier front which is likely due to surface water infiltration into ice beds via moulins and sinks. The ice bed channels and retrograde slope of this glacier is also important in routing subglacial water and ice mass. The basal conditions of these two glaciers presented in this study can help in modelling the behavior of these glaciers in the future.

DIVYA CHALLA

Optimized Synthetic Aperture Radar (SAR) Processing for Airborne UWB FMCW Radar

When & Where:


317 Nichols Hall

Committee Members:

Carl Leuschen, Chair
John Paden
James Stiles


Abstract

Remote Sensing of snow covered sea ice in melting Polar Regions has become crucial in estimating the results of increased global warming and to overcome the Earth’s energy imbalance. And to accurately map the snow models over sea ice, it has become essential to build radar systems that has increased sensitivity and to use post processing techniques that enhance the performance. The Center for Remote Sensing of Ice Sheets (CReSIS) at KU has developed ultra-wideband snow radar system that operates over 2-18 GHz frequency range to effectively measure the snow thickness including very thin snow cover and map the snow-ice and snow-ice interfaces precisely. Synthetic Aperture Radar (SAR) processing is one of the post processing technique employed to further increase the sensitivity of the radar in terms of resolution and SNR. In this thesis, a time domain correlation SAR technique which is essentially a matched filter application is described and implemented. It is verified initially with an ideal simulated point target data and then with point target data collected by the snow radar system over sea-ice. It is also shown how noise is multiplied with increasing synthetic aperture length. The effect of aircraft motion non-linearities on SAR processing are also studied at different altitudes. To overcome the effect of non-linearities and multiplicative noise, a multilooking SAR processing is proposed and explained. This is then applied to the field data collected by the snow radar in 2016 and 2017 over sea ice and observed that the SNR and azimuth resolution are improved by 40 dB. The optimum parameters like SAR aperture length and the number of looks are extracted based on the results of SAR processing on various data sets. Finally, a comparison of SAR application to low and high altitude data sets collected in 2016 over the same region is also provided. 

GARRETT ZOOK

Applications of FM Noise Radar Waveforms: Spatial Modulation and Polarization Diversity

When & Where:


246 Nichols Hall

Committee Members:

Shannon Blunt, Chair
Christopher Allen
James Stiles


Abstract

Two possible radar application spaces are explored through the exploitation of high-dimensional nonrecurrent FM-noise waveforms. The first involving a simultaneous dual-polarized emission scheme that provides good separability with respect to co- and cross-polarized terms and the second mimicking the passive actuation of the human eye with a MIMO emission. A waveform optimization scheme denoted as pseudo-random optimized (PRO) FM has been shown to generate FM-noise radar waveforms that are amenable to high power transmitters. Each pulse is generated and optimized independently and possesses a non-repeating FM-noise modulation structure. Because of this the range sidelobes of each pulse are unique and thus are effectively suppressed given enough coherent integration.

The PRO-FM waveform generation scheme is used to create two independent sets of FM-noise waveforms to be incorporated into a simultaneous dual-polarized emission; whereby two independent PRO-FM waveforms will be transmitted simultaneously from orthogonal polarization channels. This effectively creates a polarization diverse emission. The random nature of these waveforms also reduce cross-correlation effects that occur during simultaneous transmission on both channels. This formulation is evaluated using experimental open-air measurements to demonstrate the effectiveness of this high-dimensional emission.

This research aims to build upon previous work that has demonstrated the ability to mimic fixational eye movements (FEM) employed by the human eye. To implement FEM on a radar system, a MIMO capable digital array must be utilized in conjunction with spatial modulation beamforming. Successful imitation of FEM will require randomized fast-time beamsteering from a two-dimensional array. The inherent randomness associated with FEM will be paired with the PRO-FM waveforms to create an emission possessing randomness in the space and frequency domains, called the FEM radar (FEMR). Unlike traditional MIMO, FEMR emits a coherent and time-varying beam. Simulations will show the inherent enhancement to spatial resolution in two-dimensional space (azimuth and elevation) relative to standard beamforming using only the matched filter to process returns.

SAI SANDEEP BHOOSHI

MANET Routing Protocol Simulations Using Different Mobility Models

When & Where:


246 Nichols Hall

Committee Members:

James Sterbenz , Chair
Victor Frost
Fengjun Li


Abstract

Mobile Ad-hoc Networks (MANETs) due to their highly dynamic nature pose a great challenge in designing new protocols. Because these networks are infrastructure independent, routing protocol design and efficiency becomes essential in the functioning of these networks. There are many protocols proposed in the past and many are under development now. But the new or existing protocols are to be compared against each other and analyzed under realistic conditions including, but not limited to transmission range, mobility patterns, of the nodes in the network. This project is an endeavor to provide an unbiased comparison of AODV, DSDV, DSR, and OLSR under different mobility models with varying densities and dynamicity. The mobility models compared in this work include steady-state random waypoint, Gauss-Markov, and Levy walk.

RENISH THOMAS

Design and development of Ultra wide-band Microwave Components for snow–probing radars

When & Where:


317 Nichols Hall

Committee Members:

Carl Leuschen, Chair
Fernando Rodriguez-Morales
Rongqing Hui


Abstract

This thesis describes the design and development of two different ultra-wideband circuits for snow-probing radars. First, a broadband, low-loss planar quadrature hybrid coupler for the 2-20 GHz range is presented. The coupler offers better performance than commercially available options in terms of phase/amplitude imbalance and form factor.  Next, a broadband, high-power T/R module with fast switching and integrated LNA is demonstrated to enable high altitude and multi-channel modes of operations of the CReSIS airborne snow radar along with automated surface tracking ability. The modules include a custom medium-power switch with an overall order of magnitude performance increase compared to commercially available duplexers/SPDT switch solutions.

Pulse mode operations at peak power levels exceeding 100 Watts
(conservatively) can be supported with these devices and a demonstrated switching speed of less than 600 ns.

 

LUMUMBA HARNETT

Post Pulse Compression & Partially Adaptive Multi-Waveform Space-Time Adaptive Processing for Heterogeneous Clutter

When & Where:


246 Nichols Hall

Committee Members:

Shannon Blunt, Chair
Christopher Allen
James Stiles


Abstract

A new form of multi-waveform space-time adaptive processing (MuW-STAP) is presented. The formulation provides additional training data for adaptive clutter cancellation for ground moving target indication after pulse compression. The pulse compression response is homogenized using stochastic phase filters to produce a smeared response that approximates identically distribution assumed by covariance estimation. Post pulse compression MuW-STAP (PMuW-STAP) is proposed to address clutter heterogeneity that causes degradation in detection performance of STAP similar to single-input multi-output MuW-STAP. Furthermore, the family of MuW-STAP algorithms are computationally expensive due to estimation of multiple covariance matrices and inversion of a single covariance for every range sample. Well-known partially adaptive techniques, previously implemented in STAP, are implemented with PMuW-STAP. Partial adaptation in element-space post-Doppler, beam-space pre-Doppler, and beam-space post-Doppler are presented. Each of these are examined on several simulated, controlled clutter scenarios. Fully adaptive PMuW-STAP is further evaluated on the high-fidelity knowledge aided adaptive radar architecture: knowledge-aided sensor signal processing and expert reasoning (KASSPER) dataset.