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Title: Artificial lateral line systems for feedback control of underwater robots
Creator: AbdulSadda, Ahmad
Date: 2012
Collection: Electronic Theses & Dissertations
Description: A lateral line system, consisting of arrays of flow-sensing neuromasts, allows fish and amphibians to probe their ambient environment and plays a vital role in their behaviors spanning predator/prey detection, schooling, rheotaxis, courtship and communication. The feats of biological lateral lines have inspired an increasing interest in engineering a similar sensing module for underwater robots and vehicles. Often known as artificial lateral lines, these sensors could potentially enable an...
Show moreA lateral line system, consisting of arrays of flow-sensing neuromasts, allows fish and amphibians to probe their ambient environment and plays a vital role in their behaviors spanning predator/prey detection, schooling, rheotaxis, courtship and communication. The feats of biological lateral lines have inspired an increasing interest in engineering a similar sensing module for underwater robots and vehicles. Often known as artificial lateral lines, these sensors could potentially enable an underwater robot to detect and identify moving or stationary objects, and exploit ambient flow energy for efficient locomotion. Despite the advances made in this area, realizing a practical artificial lateral line still faces significant challenges in both signal processing and flow sensor fabrication.In this dissertation we describe our effort in developing signal processing methods for hydrodynamic object localization and tracking using an artificial lateral line (ALL). We consider two types of objects, a vibrating sphere and a non-vibrating cylinder, both of which are of interest in underwater applications. A vibrating sphere, known as a dipole source, is widely used in the study of biological lateral lines and it emulates the rhythmic body or fin movement. A non-vibrating cylinder (with unknown cross-section shape), on the other hand, represents a general moving or stationary object underwater with a 2D flow profile. First, a novel bio-inspired artificial lateral line system is proposed for underwater robots and vehicles by exploiting the inherent sensing capability of ionic polymer-metal composites (IPMCs). Analogous to its biological counterpart, the IPMC-based lateral line processes the sensor signals through a neural network, and we demonstrate the performance of this approach in the localization of a dipole source. Second, with an assumption of potential flows, we formulate nonlinear estimation problems for the localization and tracking of a dipole source based on analytical flow models, and propose and compare several algorithms for solving the problem. For the case of a moving cylinder, we use conformal mapping to represent a general cross-section profile, and explore the use of Kalman-filtering-type techniques in the tracking and size/shape estimation of the object.We have conducted extensive experiments to validate the developed algorithms with an artificial lateral line prototype made of millimeter-scale IPMC sensors, with sensor-to-sensor separation of 2 cm, which is determined through an optimization process based on the Cramer-Rao bound (CRB) analysis. Finally, we experimentally explore the use of IPMC sensors for estimating the hydrodynamic parameters involved in a Karman vortex street that is created by a stationary cylinder in a flow. We validate that the vortex shedding frequency, which can be extracted from the sensor signal, shows clear correlation with the flow speed and the obstacle size.
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Title: Artificial neural networks for constrained and unconstrained optimization
Creator: Chen, Jiahan
Date: 1992
Collection: Electronic Theses & Dissertations

Title: Efficient extended Kalman filter learning for feedforward layered neural networks
Creator: Benromdhane, Saida
Date: 1996
Collection: Electronic Theses & Dissertations

Title: Methods for neural signal processing and analysis
Creator: Suhail, Yasir
Date: 2005
Collection: Electronic Theses & Dissertations

Title: Apple sorting using neural networks, statistical classifiers and spectral reflectance imaging
Creator: Kavdir, Ismail
Date: 2000
Collection: Electronic Theses & Dissertations

Title: Adaptive on-device deep learning systems
Creator: Fang, Biyi
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Mobile systems such as smartphones, drones, and augmented-reality headsets are revolutionizing our lives. On-device deep learning is regarded as the key enabling technology for realizing their full potential. This is because communication with cloud adds additional latency or cost, or the applications must operate even with intermittent internet connectivity.The key to achieving the full promise of these mobile vision systems is effectively analyzing the streaming video frames. However,...
Show more"Mobile systems such as smartphones, drones, and augmented-reality headsets are revolutionizing our lives. On-device deep learning is regarded as the key enabling technology for realizing their full potential. This is because communication with cloud adds additional latency or cost, or the applications must operate even with intermittent internet connectivity.The key to achieving the full promise of these mobile vision systems is effectively analyzing the streaming video frames. However, processing streaming video frames taken in mobile settings is challenging in two folds. First, the processing usually involves multiple computer vision tasks. This multi-tenant characteristic requires mobile vision systems to concurrently run multiple applications that target different vision tasks. Second, the context in mobile settings can be frequently changed. This requires mobile vision systems to be able to switch applications to execute new vision tasks encountered in the new context.In this article, we fill this critical gap by proposing NestDNN, a framework that enables resource-aware multi-tenant on-device deep learning for continuous mobile vision. NestDNN enables each deep learning model to offer flexible resource-accuracy trade-offs. At runtime,it dynamically selects the optimal resource-accuracy trade-off for each deep learning model to fit the model's resource demand to the system's available runtime resources. In doing so, NestDNN efficiently utilizes the limited resources in mobile vision systems to jointly maximize the performance of all the concurrently running applications.Although NestDNN is able to efficiently utilize the resource by being resource-aware, it essentially treats the content of each input image equally and hence does not realize the full potential of such pipelines. To realize its full potential, we further propose FlexDNN, a novel content-adaptive framework that enables computation-efficient DNN-based on-device video stream analytics based on early exit mechanism. Compared to state-of-the-art earlyexit-based solutions, FlexDNN addresses their key limitations and pushes the state-of-the-artforward through its innovative fine-grained design and automatic approach for generating the optimal network architecture."--Pages ii-iii.
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Title: Modeling travel time in urban arterial networks with time-variant turning movements using state-space neural networks
Creator: Likens, Timothy Joseph
Date: 2007
Collection: Electronic Theses & Dissertations

Title: Designing convolutional neural networks for face alignment and anti-spoofing
Creator: Jourabloo, Amin
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Face alignment is the process of detecting a set of fiducial points on a face image, such as mouth corners, nose tip, etc. Face alignment is a key module in the pipeline of most facial analysis tasks, normally after face detection and before subsequent feature extraction and classification. As a result, improving the face alignment accuracy is helpful for numerous facial analysis tasks. Recently, face alignment works are popular in top vision venues and achieve a lot of attention. In spite...
Show more"Face alignment is the process of detecting a set of fiducial points on a face image, such as mouth corners, nose tip, etc. Face alignment is a key module in the pipeline of most facial analysis tasks, normally after face detection and before subsequent feature extraction and classification. As a result, improving the face alignment accuracy is helpful for numerous facial analysis tasks. Recently, face alignment works are popular in top vision venues and achieve a lot of attention. In spite of the fruitful prior work and ongoing progress of face alignment, pose-invariant face alignment is still challenging. To address the inherent challenges associated with this problem, we propose pose-invariant face alignment by fitting a dense 3DMM, and integrating estimation of 3D shape and 2D facial landmarks from a single face image in a single CNN. We introduce a new layer, called visualization layer, which is differentiable and allows backpropagation of an error from a later block to an earlier one. Another application of facial analysis is the face anti-spoofing, which has recently achieved a lot of attention. While face recognition systems serve as a verification portal for various devices (i.e., phone unlock, access control, and transportation security), attackers present face spoofs (i.e., presentation attacks, PA) to the system and attempt to be authenticated as the genuine user. We present our proposed deep models for face anti-spoofing that use the supervision from both the spatial and temporal auxiliary information, for the purpose of robustly detecting face PA from a face video."--Page ii.
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Title: Learning paradigms for the identification of elastic properties of composites using ultrasonic guided waves
Creator: Gopalakrishnan, Karthik
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Identification of elastic properties of composites is relevant for both nondestructive materials characterization as well as for in-situ condition monitoring to assess and predict any possible material degradation. Learning paradigms have been well explored when it comes to detection and characterization of defects in safety-critical structures, but are relatively unexplored when it comes to structural materials characterization. In this thesis we propose a learning paradigm that includes the...
Show moreIdentification of elastic properties of composites is relevant for both nondestructive materials characterization as well as for in-situ condition monitoring to assess and predict any possible material degradation. Learning paradigms have been well explored when it comes to detection and characterization of defects in safety-critical structures, but are relatively unexplored when it comes to structural materials characterization. In this thesis we propose a learning paradigm that includes the potential use of Machine Learning (ML) and Deep Learning (DL) algorithms to solve the inverse problem of material properties identification using ultrasonic guided waves. The propagation of guided waves in a composite laminate is modelled using two different modelling techniques as part of the forward problem. Here, we use the two fundamental modes of guided waves, i.e. the anti-symmetric (A0) and the symmetric modes (S0) as features for the proposed learning models. As part of the inverse problem, different learning models are used to map feature space to target space that consists of the material properties of composites. The performance of the algorithms is evaluated based on different metrics and it is seen that the networks are able to learn the mapping and generalize well to unseen examples even in the presence of noise at various levels. Overall, we are able to develop a complete framework consisting of many interlinking data processing algorithms that can effectively estimate and predict the material properties of any given composite.
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Title: Scalable pulsed mode computation architecture using integrate and fire structure based on margin propagation
Creator: Hindo, Thamira
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Neuromorphic computing architectures mimic the brain to implement efficient computations for sensory applications in a different way from that of the traditional Von Neumann architecture. The goal of neuromorphic computing systems is to implement sensory devices and systems that operate as efficiently as their biological equivalents. Neuromorphic computing consists of several potential components including parallel processing instead of synchronous processing, hybrid (pulse) computation...
Show moreNeuromorphic computing architectures mimic the brain to implement efficient computations for sensory applications in a different way from that of the traditional Von Neumann architecture. The goal of neuromorphic computing systems is to implement sensory devices and systems that operate as efficiently as their biological equivalents. Neuromorphic computing consists of several potential components including parallel processing instead of synchronous processing, hybrid (pulse) computation instead of digital computation, neuron models as a basic core of the processing instead of the arithmetic logic units, and analog VLSI design instead of digital VLSI design. In this work a new neuromorphic computing architecture is proposed and investigated for the implementation of algorithms based on using the pulsed mode with a neuron-based circuit.The proposed architecture goal is to implement approximate non-linear functions that are important components of signal processing algorithms. Some of the most important signal processing algorithms are those that mimic biological systems such as hearing, sight and touch. The designed architecture is pulse mode and it maps the functions into an algorithm called margin propagation. The designed structure is a special network of integrate-and-fire neuron-based circuits that implement the margin propagation algorithm using integration and threshold operations embedded in the transfer function of the neuron model. The integrate-and-fire neuron units in the network are connected together through excitatory and inhibitory paths to impose constraints on the network firing-rate. The advantages of the pulse-based, integrate-and-fire margin propagation (IFMP) algorithmic unit are to implement complex non-linear and dynamic programming functions in a scalable way; to implement functions using cascaded design in parallel or serial architecture; to implement the modules in low power and small size circuits of analog VLSI; and to achieve a wide dynamic range since the input parameters of IFMP module are mapped in the logarithmic domain.The newly proposed IFMP algorithmic unit is investigated both on a theoretically basis and an experimental performance basis. The IFMP algorithmic unit is implemented with a low power analog circuit. The circuit is simulated using computer aided design tools and it is fabricated in a 0.5 micron CMOS process. The hardware performance of the fabricated IFMP algorithmic architecture is also measured. The application of the IFMP algorithmic architecture is investigate for three signal processing algorithms including sequence recognition, trace recognition using hidden Markov model and binary classification using a support vector machine. Additionally, the IFMP architecture is investigated for the application of the winner-take-all algorithm, which is important for hearing, sight and touch sensor systems.
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Title: Neural networks for nonlinear programming
Creator: Maa, Chia-Yiu
Date: 1991
Collection: Electronic Theses & Dissertations

Title: Convolutional neural networks for automated cell detection in magnetic resonance imaging data
Creator: Afridi, Muhammad Jamal
Date: 2017
Collection: Electronic Theses & Dissertations
Description: Cell-based therapy (CBT) is emerging as a promising solution for a large number of serious health issues such as brain injuries and cancer. Recent advances in CBT, has heightened interest in the non-invasive monitoring of transplanted cells in in vivo MRI (Magnetic Resonance Imaging) data. These cells appear as dark spots in MRI scans. However, to date, these spots are manually labeled by experts, which is an extremely tedious and a time consuming process. This limits the ability to conduct...
Show moreCell-based therapy (CBT) is emerging as a promising solution for a large number of serious health issues such as brain injuries and cancer. Recent advances in CBT, has heightened interest in the non-invasive monitoring of transplanted cells in in vivo MRI (Magnetic Resonance Imaging) data. These cells appear as dark spots in MRI scans. However, to date, these spots are manually labeled by experts, which is an extremely tedious and a time consuming process. This limits the ability to conduct large scale spot analysis that is necessary for the long term success of CBT. To address this gap, we develop methods to automate the spot detection task. In this regard we (a) assemble an annotated MRI database for spot detection in MRI; (b) present a superpixel based strategy to extract regions of interest from MRI; (c) design a convolutional neural network (CNN) architecture for automatically characterizing and classifying spots in MRI; (d) propose a transfer learning approach to circumvent the issue of limited training data, and (e) propose a new CNN framework that exploits labeling behavior of the expert in the learning process. Extensive experiments convey the benefits of the proposed methods.
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Title: Adaptive control of non-linear systems using neural networks
Creator: Chen, Fu-Chuang
Date: 1990
Collection: Electronic Theses & Dissertations

Title: Artificial neural networks for branch prediction
Creator: Dazsi, Brian Adam
Date: 2001
Collection: Electronic Theses & Dissertations

Title: Sewer pipeline condition prediction using neural network models
Creator: Kulandaivel, Guruprakash
Date: 2004
Collection: Electronic Theses & Dissertations

Title: Implementation of feedforward artificial neural networks with learning using standard CMOS technology
Creator: Choi, Myung-Ryul
Date: 1991
Collection: Electronic Theses & Dissertations

Title: Analog CMOS implementation of artificial neural networks for temporal signal learning
Creator: Oh, Hwa-Joon
Date: 1996
Collection: Electronic Theses & Dissertations

Title: Design and analysis of neural networks for pattern recognition
Creator: Mao, Jianchang
Date: 1994
Collection: Electronic Theses & Dissertations

Title: Low power analog chips for the computation of the maximal principal component
Creator: Vedula, Shanti Swarup
Date: 1995
Collection: Electronic Theses & Dissertations

Title: Exploring joint-level control in evolutionary robotics
Creator: Moore, Jared M.
Date: 2015
Collection: Electronic Theses & Dissertations
Description: "In this dissertation, we use computational evolution and physics simulations to explore both control and morphology in robotic systems. Specifically we investigate joint-level control strategies and their interaction with morphological elements." - Abstract.

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