Electrical and Computer Engineering Commons^™

Multivalent Rechargeable Batteries, Sudhaprasanna Kumar Padigi

Dissertations and Theses

Li⁺ ion batteries have been the mainstay of high energy storage devices that have revolutionized the operating life time of consumer electronic devices for the past two decades. However, there is a steady increase in demand for energy storage devices with the ability to store more energy and deliver them at high power at low cost, without comprising safety and lifetime.

Li-ion batteries have had significant challenges in increasing the amount of stored energy without affecting the overall lifetime and the ability to deliver stored energy. In order to store and deliver more energy, more lithium ions need to …

Revealing Structural Organization With Liquid Crystal-Based Spectral Imaging Polarimetry, James Campbell Gladish

Dissertations and Theses

Structural organization refers to the particular ordering of scatterers. Probing structural organization by imaging polarized spectral scatter provides insight into the composition of a medium, and can aid in remote sensing, the identification of tissue pathologies, and material characterization and differentiation. The vector nature of polarized light enables it to interact with optical anisotropies within a medium, while the spectral aspect of polarization is sensitive to small-scale structure. However, many polarization studies have limitations, as they provide qualitative image analysis, incomplete anisotropy information, or both. The ability to image the effects of anisotropy and small-scale structure at multiple wavelengths is …

Chemical Reaction Network Control Systems For Agent-Based Foraging Tasks, Joshua Stephen Moles

Dissertations and Theses

Chemical reaction networks are an unconventional computing medium that could benefit from the ability to form basic control systems. In this work, we demonstrate the functionality of a chemical control system by evaluating classic genetic algorithm problems: Koza's Santa Fe trail, Jefferson's John Muir trail, and three Santa Fe trail segments. Both Jefferson and Koza found that memory, such as a recurrent neural network or memories in a genetic program, are required to solve the task. Our approach presents the first instance of a chemical system acting as a control system. We propose a delay line connected with an artificial …

Wavelet-Coupled Machine Learning Methods For Drought Forecast Utilizing Hybrid Meteorological And Remotely-Sensed Data, R. Tan, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

In this study, a statistical drought early warning method is proposed using novel machine learning algorithms, with the inclusion of multiple drought-related attributes from precipitation, satellite-derived land cover vegetation indices, and surface discharge. The forecast is made for the long-term hydrological drought in the region of Central Valley, California. The wavelet transform analysis is employed in combination with support vector regression and artificial neural network algorithms for improving the drought prediction effectiveness. The performance of the drought prediction is evaluated using three statistical metrics: Coefficient of Determination (R2 ), Root-Mean-Square Error (RMSE), and Mean-Absolute-Error (MAE). The results clearly indicate that …

Detection Of Variable Retention Time In Dram, Neraj Kumar

Dissertations and Theses

This thesis investigates a test method to detect the presence of Variable Retention Time (VRT) bits in manufactured DRAM. The VRT bits retention time is modeled as a 2-state random telegraph process that includes miscorrelation between test and use. The VRT defect is particularly sensitive to test and use conditions. A new test method is proposed to screen the VRT bits by simulating the use conditions during manufacturing test. Evaluation of the proposed test method required a bit-level VRT model to be parameterized as a function of temperature and voltage conditions. The complete 2-state VRT bit model combines models for …

Functional Verification Of High Performance Adders In Coq, Qian Wang, Xiaoyu Song, Ming Gu, Jiaguang Sun

Electrical and Computer Engineering Faculty Publications and Presentations

Addition arithmetic design plays a crucial role in high performance digital systems. The paper proposes a systematic method to formalize and verify adders in a formal proof assistant COQ. The proposed approach succeeds in formalizing the gate-level implementations and verifying the functional correctness of the most important adders of interest in industry, in a faithful, scalable, and modularized way. The methodology can be extended to other adder architectures as well.

Solar Data Analysis, Mike C. T. Ray

Dissertations and Theses

The solar industry has grown considerably in the last few years. This larger scale has introduced more problems as well as possibilities. One of those possibilities is analyzing the data coming from the sites that are now being monitored, and using the information to answer a variety of questions.

We have four questions which are of prime importance identified in this thesis:

1. Can data from customers be trusted?

2. Can we use data from existing sites to determine which sites need the most improvement?

3. Can we implement a location-based algorithm to reduce the amount of false positives for …

A Parabolic Equation Analysis Of The Underwater Noise Radiated By Impact Pile Driving, Nathan Laws

Dissertations and Theses

Impact pile driving can produce extremely high underwater sound levels, which are of increasing environmental concern due to their deleterious effects on marine wildlife. Prediction of underwater sound levels is important to the assessment and mitigation of the environmental impacts caused by pile driving. Current prediction methods are limited and do not account for the dynamic pile driving source, inhomogeneities in bathymetry and sediment, or physics-based sound wave propagation.

In this thesis, a computational model is presented that analyzes and predicts the underwater noise radiated by pile driving and is suitable for shallow, inhomogeneous environments and long propagation ranges. The …

Computer Aided Design Of Permutation, Linear, And Affine-Linear Reversible Circuits In The General And Linear Nearest-Neighbor Models, Ben Schaeffer

Dissertations and Theses

With the probable end of Moore's Law in the near future, and with advances in nanotechnology, new forms of computing are likely to become available. Reversible computing is one of these possible future technologies, and it employs reversible circuits. Reversible circuits in a classical form have the potential for lower power consumption than existing technology, and in a quantum form permit new types of encryption and computation.

One fundamental challenge in synthesizing the most general type of reversible circuit is that the storage space for fully specifying input-output descriptions becomes exponentially large as the number of inputs increases linearly. Certain …

Methods For Efficient Synthesis Of Large Reversible Binary And Ternary Quantum Circuits And Applications Of Linear Nearest Neighbor Model, Maher Mofeid Hawash

Dissertations and Theses

This dissertation describes the development of automated synthesis algorithms that construct reversible quantum circuits for reversible functions with large number of variables. Specifically, the research area is focused on reversible, permutative and fully specified binary and ternary specifications and the applicability of the resulting circuit to the physical limitations of existing quantum technologies.

Automated synthesis of arbitrary reversible specifications is an NP hard, multiobjective optimization problem, where 1) the amount of time and computational resources required to synthesize the specification, 2) the number of primitive quantum gates in the resulting circuit (quantum cost), and 3) the number of ancillary qubits …

A Survey Of Systems For Predicting Stock Market Movements, Combining Market Indicators And Machine Learning Classifiers, Jeffrey Allan Caley

Dissertations and Theses

In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were explored. Ten stocks, 9 companies and 1 index, were used to evaluate each iteration of the trading method. The classification …

Joint Angle Tracking With Inertial Sensors, Mahmoud Ahmed El-Gohary

Dissertations and Theses

The need to characterize normal and pathological human movement has consistently driven researchers to develop new tracking devices and to improve movement analysis systems. Movement has traditionally been captured by either optical, magnetic, mechanical, structured light, or acoustic systems. All of these systems have inherent limitations. Optical systems are costly, require fixed cameras in a controlled environment, and suffer from problems of occlusion. Similarly, acoustic and structured light systems suffer from the occlusion problem. Magnetic and radio frequency systems suffer from electromagnetic disturbances, noise and multipath problems. Mechanical systems have physical constraints that limit the natural body movement. Recently, the …

Evolved Design Of A Nonlinear Proportional Integral Derivative (Npid) Controller, Shubham Chopra

Dissertations and Theses

This research presents a solution to the problem of tuning a PID controller for a nonlinear system. Many systems in industrial applications use a PID controller to control a plant or the process. Conventional PID controllers work in linear systems but are less effective when the plant or the process is nonlinear because PID controllers cannot adapt the gain parameters as needed. In this research we design a Nonlinear PID (NPID) controller using a fuzzy logic system based on the Mamdani type Fuzzy Inference System to control three different DC motor systems. This fuzzy system is responsible for adapting the …

Material Characterization Of Zinc Oxide In Bulk And Nanowire Form At Terahertz Frequencies, Forest Emerson Kernan

Dissertations and Theses

Many new applications are being proposed and developed for use in the terahertz (THz) frequency region. Similarly, many new materials are being characterized for possible use in this area. Nanostructured forms are of particular interest since they may yield desirable properties, but they remain especially challenging to characterize. This work focuses on the characterization of zinc oxide (ZnO) in bulk and nanowire form. A method for characterizing nanostructures at THz by use of a parallel-plate waveguide (PPWG) is presented. This method is novel in that it is simple, both in theory and practice, and does not require the use of …

Optimal Block Encoding And Optimal Entropy For Lossless Image Compression, Larry Ray Dennis

Dissertations and Theses

In this thesis, a novel approach is designed using a quad-tree stack structure to encode the image to determine the optimal block size at the optimal and effective entropy in the lossless image compression method. Proof is given through encoding of the predictor and randomly constructed planes. There is a high degree of relationship between the placement of bits in the planes. Clearly results shows that use of the optimal entropy and encoding block sizes will increase the compression ratio using the lossless method. The cost of using the block size methods to encoding and entropy is discussed and proven. …

A Simplified Approach To Reduce Blocking And Ringing Artifacts In Transform-Coded Images, Jianping Hu

Dissertations and Theses

Presently Block-based Discrete Cosine Transform (BDCT) image coding techniques are widely used in image and video compression applications such as JPEG and MPEG. At a moderate bit rate, BDCT is usually a quite satisfactory solution to most of practical coding applications. However, for high rate compression it produces noticeable blocking and ringing artifacts in the decompressed image. It has been an active research area for a decade for reducing these artifacts. In this thesis, a novel post-processing algorithm is proposed to remove the blocking and ringing artifacts at low bit rate. It is non-iterative and uses both spatial and transform …