Doctor of Philosophy, Case Western Reserve University, 2022, Physics

In the last two decades, advances in experimental techniques have opened up new vistas for understanding bio-molecules and their complex networks of interactions in the cell. In this thesis, we use theoretical modeling and machine learning to explore two surprising aspects that have been revealed by recent experiments: (i) the discovery that many different types of cellular signaling networks, in both prokaryotes and eukaryotes, can transmit at most 1 to 3 bits of information; (ii) the observation that single bio-molecules can exhibit multiple, stable conformational states with extremely heterogeneous functional properties. The first part of the thesis investigates how the energetic costs of signaling in biological networks constrain the amount of information that can be transferred through them. The focus is specifically on the kinase-phosphatase enzymatic network, one of the basic elements of cellular signaling pathways. We find a remarkably simple analytical relationship for the minimum rate of ATP consumption necessary to achieve a certain signal fidelity across a range of frequencies. This defines a fundamental performance limit for such enzymatic systems, and we find evidence that a component of the yeast osmotic shock pathway may be close to this optimality line. By quantifying the evolutionary pressures that operate on these networks, we argue that this is not a coincidence: natural selection is capable of pushing signaling systems toward optimality, particularly in unicellular organisms. Our theoretical framework is directly verifiable using existing experimental techniques, and predicts that many more examples of such optimality should exist in nature. In the second part of the thesis, we develop two machine learning methods to analyze data from single-molecule AFM pulling experiments: a supervised (deep learning) and an unsupervised (non-parametric Bayesian) algorithm. These experiments involve applying an increasing force on a bio-molecul (open full item for complete abstract)

Committee: Michael Hinczewski (Committee Chair); Peter Thomas (Committee Member); Harsh Mathur (Committee Member); Lydia Kisley (Committee Member) Subjects: Biophysics; Physics

Master of Science in Electrical Engineering (MSEE), Wright State University, 2018, Electrical Engineering

Synthetic aperture ladar (SAL) is similar to synthetic aperture radar (SAR) in that it can create range/cross-range slant plane images of the illuminated scatters; however, SAL has wavelengths 10,000x smaller than SAR enabling a relatively narrow real aperture, diffraction limited beam widths. The relatively narrow real aperture resolutions allow for multiple slant planes to be created for a single target with reasonable range/aperture combinations. These multiple slant planes can be projected into a single slant plane projections (as in SAR). It can also be displayed as a 3-D image with asymmetric resolutions, diffraction limited in the dimension orthogonal to the SAL baseline. Multiple images with diversity in angle orthogonal to SAL baselines can be used to synthesize resolution with tomographic techniques and enhance the diffraction limited resolution. The goal of this research is to explore methods to enhance the diffraction limited resolutions with multiple observations and/or multiple slant plane imaging with SAL systems. Specifically, metrics associated with the information content of the tomographic based 3 dimensional reconstructions of SAL intensity imagery will be investigated to see how it changes as a function of number of slant planes in the SAL images and number of elevation observations are varied. Approved for public release, distribution unlimited (APRS-RY-18-0785)

Committee: Arnab Shaw Ph.D. (Advisor); Lawrence Barnes M.S. (Committee Member); Joshua Ash Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Sciences, Case Western Reserve University, 2018, Biology

Signal transduction is a process by which living cells detect and respond to changes in their environment, including signaling, reception, transduction, and response reactions. Information theory as introduced by Shannon uses mutual information, the difference between entropy of output and entropy of output given input, to estimate uncertainty in message transmission processes. Neurotransmission is a typical signal transduction process in which the neurotransmitter works as a signaling molecule, carrying information from the source to an associated receptor. Viewing the input and output signals as point processes, we apply information theory to analyze its communication capabilities. This thesis provides a detailed glutamate simulation procedure and its mutual information analysis in MATLAB, and describes how information theory applies to a neurotransmission simulation.

Committee: Peter Thomas (Advisor) Subjects: Biology; Information Systems; Mathematics

Master of Science, University of Akron, 2017, Applied Mathematics

The use of wireless technologies has grown significantly in the past decade. According to Cisco Global Mobile Data Traffic Forecast , global mobile data communication and usage will increase seven-times within 2016 and 2021. Over the last few years, innovative wireless communication paradigms have been introduced to overcome the spectrum crunch due to limited wireless spectrum resources. Examples include multi-antenna applications, where a large number of antennas can be used at the transmitter and/or the receiver. This thesis contributes towards the analysis and design of multi-antenna wire-less communication systems from an information-theoretical aspect. Specifically, this thesis investigates the information rate between the input and output of wireless channels having multiple transmit antennas and a single receive antenna under both per-antenna power constraints as well as channels under joint per-antenna and sum power constraints. We consider a dynamic wireless environment where neither the transmitter nor the receiver knows the channel state information (CSI). First, an expression of the information rate of the considered channel achieved by a given discrete input vector is established. The expression involves a single integral, which can be calculated via numerical integrations. For a more effective and accurate calculation of the information rate, a novel technique based on piece-wise linear curve fitting (PWLCF) is then proposed. Since a PWLCF-based method is only applicable to integrals having finite limits, our approach is divide the domain into two separate regions. In the finite-range region, we apply the PWLCF. For the other region, we establish a lower bound on the integrand in the information rate, and show that the information rate can be estimated to achieve any accuracy level. By combining the two regions, it is shown that the information rate can be estimated with a predetermined accuracy. The proposed method provides a simple way to calcu (open full item for complete abstract)

Committee: Nghi Tran (Advisor); Truyen Nguyen (Advisor); Patrick Wilber (Committee Chair) Subjects: Computer Engineering; Electrical Engineering; Mathematics

Doctor of Philosophy, Case Western Reserve University, 2015, EECS - System and Control Engineering

This thesis consists of various studies in information theory, including its connection with control theory and the computational aspects of information measures. The first part of the research investigates the connection between control theory and information theory. This part extends previous results that mainly focused on this connection in the context of state estimation and feedback control. For linear systems, mutual information, along with the concepts of controllability and observability, is used to derive a tight connection between control theory and information theory. For nonlinear systems, a weaker statement of this connection is established. Some explicit calculations for linear systems and interesting observations about these calculations are presented. The second part investigates the computation of mutual information. An innovative method to compute the mutual information between two collections of time series data based on a Hidden Markov Model (HMM) is proposed. For continuous-valued data, a HMM with Gaussian emission is used to estimate the underlying dynamics of the original data. Mutual information is computed based on the approximate dynamics provided by the HMM. This work improves the estimation of the upper and lower bounds of entropy for Gaussian mixtures, which is one of the key components in this proposed method. This improvement of these bounds are shown to be robust compared to existing methods in all of the synthetic data experiments conducted. In addition, this research includes the study of the computation of Shannon mutual information in which the strong assumptions of independence and identical distribution (i.i.d.) are imposed. This research shows that even if this assumption is violated, the results process a meaningful interpretation. The study of the computation of Shannon mutual information for continuous-valued random variables is included in this research. Three coupled chaotic systems are used as exemplars to show that the c (open full item for complete abstract)

Committee: Kenneth Loparo (Advisor); Vira Chankong (Committee Member); Marc Buchner (Committee Member); Richard Kolacinski (Committee Member) Subjects: Engineering; Mathematics

Master of Science in Engineering, University of Akron, 2014, Electrical Engineering

Cooperative relaying is a promising technique to enhance the reliability and data-rate of wireless networks. Among different cooperative relaying schemes, the half-duplex non-orthogonal amplify-and-forward (NAF) protocol is popular due to its low implementation complexity and performance advantages. This thesis investigates precoder design for a cooperative half-duplex single-relay NAF system from an information theoretic point of view using a novel mutual information-based design criterion. The first part of the thesis considers the design of a 2x2 precoder for the NAF half duplex single relay network in the presence of the direct link using mutual information (MI) as the main performance metric. Different from precoder design methods using pairwise error probability (PEP) analysis, which are valid only at high signal-to-noise ratios (SNR), the proposed precoder design can apply to any SNR region, which is of more interest from both information-theoretic and practical points of view. A MI-based criterion is developed for a cooperative frame length of 2, which corresponds to the case of using a 2x2 precoder. The design criterion is established in a closed-form, which can be helpful in finding an optimal precoder. Then it is analytically shown that a good precoder should have all entries that are equal in magnitude, which is different from the optimal precoders obtained thus far using the conventional PEP criterion. Simulation results indicate that the proposed class of precoders outperform the existing precoders in terms of the mutual information performance. The second part of the thesis extends the precoder design to an arbitrary block length of 2T. In general, for this case, a precoder of size 2Tx2T needs to be considered to optimize the MI performance. Similar to the 2 × 2 precoder design, a MI-based design criterion is first established. While the criterion can be expressed in closed form, the design of optimal precoders in this case is not possible, (open full item for complete abstract)

Committee: Nghi H Tran Dr. (Advisor); Alexis De Abreu Garcia Dr. (Committee Member); Arjuna Madanayake Dr. (Committee Member) Subjects: Computer Engineering; Design; Electrical Engineering; Engineering; Literature; Systems Design; Systems Science; Theoretical Mathematics

PHD, Kent State University, 2007, College of Arts and Sciences / Department of Computer Science

A fundamental problem in medical image analysis is image registration, which is the task of finding geometric relationships between corresponding points in multiple images of the same scene. Various registration methods have been proposed over recent years, among which registration strategies based on maximization of mutual information have been widely used in multi-modality image registration. However, applying mutual information (MI) to original intensities only takes statistical information into consideration, while spatial information is completely neglected. In the first part of this dissertation, a novel approach is proposed to incorporate spatial information into MI through gradient vector flow (GVF). With this approach, MI now is calculated from the GVF-intensity (GVFI) map of the original images instead of their intensity values. The algorithm is implemented and applied to multi-modality brain image registration to test the accuracy and robustness of the proposed method. Experimental results show that the success rate of our method is higher than that of traditional MI-based registration. In many applications, a rigid transformation is insufficient to describe the spatial relationship between two images. Thus, elastic transformations, or non-rigid transformations are often required in image registration. In the second part of this dissertation, we present a generalized gradient-guided non-rigid registration strategy. The derivation procedure is similar to that by Lucas and Kanade, but in a more general manner. In experiments, we compare the proposed method and other gradient-guided methods in the literature, using both synthetic and real images. It is shown that methods combining gradients from both source and target images usually perform better. In the third part, we apply previously described registration methods to atlas-based brain magnetic resonance (MR) image segmentation. A pre-labeled image or atlas is first registered to the subject image to be se (open full item for complete abstract)

Committee: Cheng-Chang Lu (Advisor) Subjects:

Doctor of Philosophy (PhD), Ohio University, 2024, Physics and Astronomy (Arts and Sciences)

Synchronization is ubiquitous in natural and engineered systems and can be (un)favorable in the brain. For example, reduced coherent oscillations in the gamma band characterize Alzheimer's disease, while strong synchronization in the basal ganglia is a hallmark of Parkinson's disease. Even a single sensory neuron may possess a complex dendritic network with many interacting active elements, resulting in collective synchronous activity. This work studies the effect of network structure and its variability on steady states of coupled nonlinear excitable and oscillatory elements and the collective network response to external stimulation. First, we study the determinants of information processing in sensory neurons with myelinated dendrites, e.g., touch receptors and muscle spindles, using a tree network model of sensory neurons. In particular, we show that in the strong coupling limit, the statistics of the number of nodes and leaf nodes fully determine the network response, quantified by mutual information, regardless of the stimulus distribution among leaf nodes. However, the mutual information may strongly depend on the stimulus distribution among leaf nodes for intermediate coupling. Second, we study plastic networks of oscillatory neurons to address how synchronized and incoherent activities can spontaneously emerge and be controlled by stimulation. We develop models of neuronal networks with synaptic weight and structural plasticity to study the co-evolution of network activity and structure. We show that structural plasticity may enable the networks to optimize their structure for enhanced synchrony with reduced connectivity, rendering networks more robust against desynchronizing stimuli. The rewiring reduces the network randomness, leading to specific correlations in the number of incoming and outgoing synaptic contacts of neurons.

Committee: Alexander Neiman (Advisor); David Tees (Committee Member); Mitchell Day (Committee Member); Horacio Castillo (Committee Member) Subjects: Biophysics; Physics

Master of Computer Science, Miami University, 2021, Computer Science and Software Engineering

Materials characterization, specifically with polymers, involves the simulation of a model representing the chemical composition of a material. The goals of materials characterization include: (1) investigate behaviors that are hard to detect in physical experiments, (2) predict thermo-mechanical properties, and (3) predict causal relationships between behaviors and material properties. Traditional methods for this investigation use domain knowledge to investigate likely trends, taking days-to-weeks of analysis. In this work, a Bayesian Network for Polymer Prediction (BNPP) is proposed to estimate these causal relationships. The thesis proposes a software framework to streamline training and use of BNPP. BNPP separates nodes into 3 fully-connected layers - (1) polymer parameters, (2) behaviors/quantities, and (3) polymer properties. BNPP trained to an average prediction accuracy of 62% on a dataset of 200 systems in 11 minutes, compared to traditional days-to-weeks of analysis. Experiments demonstrated that the average prediction accuracy can be improved to 67% by reducing extraneous variables and pruning edges. Mutual information-based analysis of the trained network provides insight into relationships among the parameters, behaviors, and properties. The key contributions are: (1) BNPP - an open-source software framework, (2) a trained instance of BNPP for poly-ε-caprolactone, and (3) assessment of the effectiveness of BNPP on Polycaprolactone.

Committee: Dhananjai Rao PhD (Advisor); Khodakhast Bibak PhD (Committee Member); James Moller PhD (Committee Member); Rajiv Berry PhD (Committee Member) Subjects: Computer Science

Doctor of Philosophy (Ph.D.), University of Dayton, 2019, Electro-Optics

This research demonstrates registration algorithms specific to multi-pixel imaging Inverse Synthetic Aperture LiDAR (ISAL) complex data volumes. Two registration approaches are considered, a mutual information registration algorithm (MIRA) and an enhanced, range bin-summed cross-correlation algorithm. For implementing these in the context of an ISAL signal, a theoretical mapping of the reflected target plane field to an aperture plane for multi-pixel detection is done. The theory for implementing both MIRA and cross-correlation enhancements is detailed and applied to a simulated sensitivity analysis that compares algorithm convergence and performance for different SNR, sub-aperture shift distances, and low pixel supports. To the best of the authors' knowledge, this is the first application of 3D complex volume mutual information registration to LiDAR aperture synthesis. The enhanced cross-correlation algorithm showed significant gain in registration operability with respect to SNR and sub-aperture shift, giving new options for potential ISAL system design. An experimental Flash LiDAR system was constructed utilizing a multi-pixel temporal heterodyne detection approach for simultaneous azimuth, elevation, range and phase ISAL imaging of a target and this system was used to benchmark registration sensitivity for real data volumes. This is the first known application of a fast focal plane array for low support flash temporal heterodyne LiDAR for aperture synthesis.

Committee: Matthew Dierking Ph.D. (Advisor); Partha Banerjee Ph.D. (Committee Member); David Rabb Ph.D. (Committee Member); Bryce Schumm Ph.D. (Committee Member); Edward Watson Ph.D. (Committee Member) Subjects: Electrical Engineering; Optics; Physics

MS, University of Cincinnati, 2016, Engineering and Applied Science: Computer Science

Malicious Cyber Adversaries may compromise the security of a system by denying access to legitimate users. This is often coupled with immeasurable loss of confidential data, which leads to hefty losses in both financial and trustworthiness aspects of a corporation. Malware exploits key vulnerabilities in applications presenting problems such as identity theft, unapproved software installations, etc. Abundance in malware detection and removal techniques in the ever evolving field of computers, presently exhibit a lower level of efficiency in detecting malicious softwares. Techniques available currently enable detection of softwares that are embedded with known signatures. No doubt these methods are efficient. However, most malware writers, aware of signature-based detection methods are working towards bypassing them. Machine learning based systems for malware classification and detection have been tested and proved to be more efficient than standard signature-based systems. A vital reason and justification providing a strong foothold for using machine learning techniques is that even unseen malware can be detected, thus eliminating malware detection failures and providing very high success rates. Our method uses efficient machine learning techniques for classification and detection of portable executable (PE) files of various malware classes commonly found in computers running Windows operating systems. For malicious files, computation of the distance between two files should yield an indication of their similarity. Using this as a basis, this thesis analyses the different approaches which can be employed for classifying malicious files using a method known as rank distance. This distance measure has been combined with a feature extraction method known as mutual information which analyses the opcodes n-gram sequences extracted from the PE files and segregates the most relevant opcodes from these. The most relevant opcodes, thus obtained, are used as features (open full item for complete abstract)

Committee: Anca Ralescu Ph.D. (Committee Chair); Chia Han Ph.D. (Committee Member); Dan Ralescu Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2004, Electrical Engineering

Real-time elastic image registration is potentially an enabling technology for the effective and efficient use of many image-guided diagnostic and treatment procedures relying on multimodality image fusion or serial image comparison. Mutual information is currently the best-known image similarity measure for multimodality image registration. A well-known problem with elastic registration algorithms is their high computational cost, with common elastic registration times in the order of hours. This complexity is due both to the large number of image similarity calculations required to converge to the optimal transformation and the time required to calculate image similarity. This dissertation presents an algorithm for elastic image registration that is optimized to minimize the execution time, and a hardware architecture for algorithm acceleration. Novel features of the algorithm include the use of a priori information to limit the search space for possible transformations, linear bound-based grid folding prevention, and adaptive optimization algorithm tolerance. The hardware architecture accelerates mutual information calculation, which is a memory-intensive task that does not benefit from cache-based memory architecture in standard software implementations, but can be efficiently implemented in a pipeline using parallel memory access techniques. Its calculation is performed in two steps, namely mutual histogram calculation and entropy accumulation. The main focus of acceleration is on mutual histogram calculation, which corresponds to about 99% of the overall mutual information calculation time. The architecture employs parallel, independent access to the image and mutual histogram memories and includes a mutual histogram partitioning scheme that allows multiple parallel accesses to the mutual histogram memory. Entropy calculation and accumulation is performed using a novel variable segment size piecewise polynomial approximation implemented using look-up tables. A (open full item for complete abstract)

Committee: Jogikal Jagadeesh (Advisor) Subjects:

Doctor of Philosophy, Case Western Reserve University, 2005, Computer Engineering

Real-time 3D medical imaging requires very high computational capability that is beyond most of the general computing platforms. Although application specific integrated circuits (ASIC) can provide solutions for a particular algorithm, they are too expensive to develop and most of them are not flexible enough to adapt to the evolution of existing algorithms or the emergence of new problems. FPGA-based reconfigurable architectures combined with general-purpose processors exhibit a good tradeoff in performance and flexibility, and are affordable for practical applications. To address the problems in designing such a system, including long designing and testing time, complex data manipulation and high performance requirement etc., we designed a new computing platform to accelerate a broad range of local operation-based 3D medical imaging algorithms. This platform is composed of a new data caching scheme, called brick caching scheme and a reconfigurable System-on-Chip (SoC) architecture targeted to Xilinx Virtex-II Pro FPGAs. The brick caching scheme exploits spatial locality of reference in three dimensions with 3D block caching; it enables data prefetching by obtaining input data block information through input-output space mapping; it also supports multiple data accesses with data duplication. An intelligent data caching system is built around a PowerPC processor core in the SoC architecture to support the brick caching scheme. A multiple pipeline execution unit that is reconfigurable to different algorithms is designed to perform vectorized computation. Two algorithms are implemented and tested on this platform, one is the FDK cone-beam CT reconstruction algorithm and the other is the mutual information-based 3D registration algorithm. Our simulation results demonstrate that a speed-up of about 30 can be achieved for both of the algorithms.

Committee: Christos Papachristou (Advisor) Subjects:

Doctor of Philosophy, University of Akron, 2006, Engineering-Applied Mathematics

In recent years, the demand for wireless communication systems with high data rates and improved link quality for a variety of applications has dramatically increased. To keep up with the demand, new concepts that could mitigate the channel impairments and optimally exploit the limited communication resources are necessary. One of such new concepts is the multiple input multiple output (MIMO) communication system, which has been found to be efficient in increasing the performance. To utilize the huge potential benefits of multiple antennas, it is necessary to develop new transceiver strategies. Hence, in this dissertation, we combine the concept of MIMO and code division multiple access (CDMA), and coding, into a robust communication system. We develop the analytical framework, architectures, algorithms and performance analyis. Both analytical and simulated results are presented. The performance of the MIMO-CDMA system is analyzed by taking into account advanced signal processing techniques, in the presence of mutual information coupling. The performance of the system is evaluated in terms of complexity, signal-to-noise ratio, bit error rate, outage probability and mutual information capacity.

Committee: Okechukwu Ugweje (Advisor) Subjects: