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Articles 1 - 29 of 29
Full-Text Articles in Computer Sciences
Towards A Robust Defense: A Multifaceted Approach To The Detection And Mitigation Of Neural Backdoor Attacks Through Feature Space Exploration And Analysis, Liuwan Zhu
Electrical & Computer Engineering Theses & Dissertations
From voice assistants to self-driving vehicles, machine learning(ML), especially deep learning, revolutionizes the way we work and live, through the wide adoption in a broad range of applications. Unfortunately, this widespread use makes deep learning-based systems a desirable target for cyberattacks, such as generating adversarial examples to fool a deep learning system to make wrong decisions. In particular, many recent studies have revealed that attackers can corrupt the training of a deep learning model, e.g., through data poisoning, or distribute a deep learning model they created with “backdoors” planted, e.g., distributed as part of a software library, so that the …
Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque
Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque
Electrical & Computer Engineering Theses & Dissertations
Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover …
Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray
Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray
Electrical & Computer Engineering Theses & Dissertations
Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …
Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw
Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw
Electrical & Computer Engineering Theses & Dissertations
Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …
Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne
Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne
Electrical & Computer Engineering Theses & Dissertations
Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in …
Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson
Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson
Electrical & Computer Engineering Theses & Dissertations
Raman spectroscopy is a powerful analysis technique that has found applications in fields such as analytical chemistry, planetary sciences, and medical diagnostics. Recent studies have shown that analysis of Raman spectral profiles can be greatly assisted by use of computational models with achievements including high accuracy pure sample classification with imbalanced data sets and detection of ideal sample deviations for pharmaceutical quality control. The adoption of automated methods is a necessary step in streamlining the analysis process as Raman hardware becomes more advanced. Due to limits in the architectures of current machine learning based Raman classification models, transfer from pure …
Non-Destructive Evaluation For Composite Material, Desalegn Temesgen Delelegn
Non-Destructive Evaluation For Composite Material, Desalegn Temesgen Delelegn
Electrical & Computer Engineering Theses & Dissertations
The Nondestructive Evaluation Sciences Branch (NESB) at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has conducted impact damage experiments over the past few years with the goal of understanding structural defects in composite materials. The Data Science Team within the NASA LaRC Office of the Chief Information Officer (OCIO) has been working with the Non-Destructive Evaluation (NDE) subject matter experts (SMEs), Dr. Cheryl Rose, from the Structural Mechanics & Concepts Branch and Dr. William Winfree, from the Research Directorate, to develop computer vision solutions using digital image processing and machine learning techniques that can help identify …
Speech Based Machine Learning Models For Emotional State Recognition And Ptsd Detection, Debrup Banerjee
Speech Based Machine Learning Models For Emotional State Recognition And Ptsd Detection, Debrup Banerjee
Electrical & Computer Engineering Theses & Dissertations
Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a …
Idpal – A Partially-Adiabatic Energy-Efficient Logic Family: Theory And Applications To Secure Computing, Mihail T. Cutitaru
Idpal – A Partially-Adiabatic Energy-Efficient Logic Family: Theory And Applications To Secure Computing, Mihail T. Cutitaru
Electrical & Computer Engineering Theses & Dissertations
Low-power circuits and issues associated with them have gained a significant amount of attention in recent years due to the boom in portable electronic devices. Historically, low-power operation relied heavily on technology scaling and reduced operating voltage, however this trend has been slowing down recently due to the increased power density on chips. This dissertation introduces a new very-low power partially-adiabatic logic family called Input-Decoupled Partially-Adiabatic Logic (IDPAL) with applications in low-power circuits. Experimental results show that IDPAL reduces energy usage by 79% compared to equivalent CMOS implementations and by 25% when compared to the best adiabatic implementation. Experiments ranging …
Transparent Spectrum Co-Access In Cognitive Radio Networks, Jonathan Daniel Backens
Transparent Spectrum Co-Access In Cognitive Radio Networks, Jonathan Daniel Backens
Electrical & Computer Engineering Theses & Dissertations
The licensed wireless spectrum is currently under-utilized by as much as 85%. Cognitive radio networks have been proposed to employ dynamic spectrum access to share this under-utilized spectrum between licensed primary user transmissions and unlicensed secondary user transmissions. Current secondary user opportunistic spectrum access methods, however, remain limited in their ability to provide enough incentive to convince primary users to share the licensed spectrum, and they rely on primary user absence to guarantee secondary user performance. These challenges are addressed by developing a Dynamic Spectrum Co-Access Architecture (DSCA) that allows secondary user transmissions to co-access transparently and concurrently with primary …
A Vision-Based Automatic Safe Landing-Site Detection System, Yufei Shen
A Vision-Based Automatic Safe Landing-Site Detection System, Yufei Shen
Electrical & Computer Engineering Theses & Dissertations
An automatic safe landing-site detection system is proposed for aircraft emergency landing, based on visible information acquired by aircraft-mounted cameras. Emergency landing is an unplanned event in response to emergency situations. If, as is unfortunately usually the case, there is no airstrip or airfield that can be reached by the un-powered aircraft, a crash landing or ditching has to be carried out. Identifying a safe landing-site is critical to the survival of passengers and crew. Conventionally, the pilot chooses the landing-site visually by looking at the terrain through the cockpit. The success of this vital decision greatly depends on the …
Learning Local Features Using Boosted Trees For Face Recognition, Rajkiran Gottumukkal
Learning Local Features Using Boosted Trees For Face Recognition, Rajkiran Gottumukkal
Electrical & Computer Engineering Theses & Dissertations
Face recognition is fundamental to a number of significant applications that include but not limited to video surveillance and content based image retrieval. Some of the challenges which make this task difficult are variations in faces due to changes in pose, illumination and deformation. This dissertation proposes a face recognition system to overcome these difficulties. We propose methods for different stages of face recognition which will make the system more robust to these variations. We propose a novel method to perform skin segmentation which is fast and able to perform well under different illumination conditions. We also propose a method …
Fusion Of Visual And Thermal Images Using Genetic Algorithms, Sertan Erkanli
Fusion Of Visual And Thermal Images Using Genetic Algorithms, Sertan Erkanli
Electrical & Computer Engineering Theses & Dissertations
Demands for reliable person identification systems have increased significantly due to highly security risks in our daily life. Recently, person identification systems are built upon the biometrics techniques such as face recognition. Although face recognition systems have reached a certain level of maturity, their accomplishments in practical applications are restricted by some challenges, such as illumination variations. Current visual face recognition systems perform relatively well under controlled illumination conditions while thermal face recognition systems are more advantageous for detecting disguised faces or when there is no illumination control. A hybrid system utilizing both visual and thermal images for face recognition …
Tree-D-Seek: A Framework For Retrieving Three-Dimensional Scenes, Saurav Mazumdar
Tree-D-Seek: A Framework For Retrieving Three-Dimensional Scenes, Saurav Mazumdar
Electrical & Computer Engineering Theses & Dissertations
In this dissertation, a strategy and framework for retrieving 3D scenes is proposed. The strategy is to retrieve 3D scenes based on a unified approach for indexing content from disparate information sources and information levels. The TREE-D-SEEK framework implements the proposed strategy for retrieving 3D scenes and is capable of indexing content from a variety of corpora at distinct information levels. A semantic annotation model for indexing 3D scenes in the TREE-D-SEEK framework is also proposed. The semantic annotation model is based on an ontology for rapid prototyping of 3D virtual worlds.
With ongoing improvements in computer hardware and 3D …
A Subspace Projection Methodology For Nonlinear Manifold Based Face Recognition, Praveen Sankaran
A Subspace Projection Methodology For Nonlinear Manifold Based Face Recognition, Praveen Sankaran
Electrical & Computer Engineering Theses & Dissertations
A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this dissertation. Feature extraction methods aim to find compact representations of data that are easy to classify. Measurements with similar values are grouped to same category, while those with differing values are deemed to be of separate categories. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this feature …
High-Performance Broadcast And Multicast Protocols For Multi-Radio Multi-Channel Wireless Mesh Networks, Jun Wang
Electrical & Computer Engineering Theses & Dissertations
Recently, wireless mesh networks (WMNs) have attracted much attention. A vast amount of unicast, multicast and broadcast protocols has been developed for WMNs or mobile ad hoc networks (MANETs). First of all, broadcast and multicast in wireless networks are fundamentally different from the way in which wired networks function due to the well-known wireless broadcast/multicast advantage. Moreover, most broadcast and multicast protocols in wireless networks assume a single-radio single-channel and single-rate network model, or a generalized physical model, which does not take into account the impact of interference. This dissertation focuses on high-performance broadcast and multicast protocols designed for multi-radio …
Rapid Prototyping For Virtual Environments, Emre Baydogan
Rapid Prototyping For Virtual Environments, Emre Baydogan
Electrical & Computer Engineering Theses & Dissertations
Development of Virtual Environment (VE) applications is challenging where application developers are required to have expertise in the target VE technologies along with the problem domain expertise. New VE technologies impose a significant learning curve to even the most experienced VE developer. The proposed solution relies on synthesis to automate the migration of a VE application to a new unfamiliar VE platform/technology. To solve the problem, the Common Scene Definition Framework (CSDF) is developed, that serves as a superset/model representation of the target virtual world. Input modules are developed to populate the framework with the capabilities of the virtual world …
Neighborhood Defined Feature Selection Strategy For Improved Face Recognition In Different Sensor Modalitie, Satyanadh Gundimada
Neighborhood Defined Feature Selection Strategy For Improved Face Recognition In Different Sensor Modalitie, Satyanadh Gundimada
Electrical & Computer Engineering Theses & Dissertations
A novel feature selection strategy for improved face recognition in images with variations due to illumination conditions, facial expressions, and partial occlusions is presented in this dissertation. A hybrid face recognition system that uses feature maps of phase congruency and modular kernel spaces is developed. Phase congruency provides a measure that is independent of the overall magnitude of a signal, making it invariant to variations in image illumination and contrast. A novel modular kernel spaces approach is developed and implemented on the phase congruency feature maps. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training …
An Adaptive Algorithm To Identify Ambiguous Prostate Capsule Boundary Lines For Three-Dimensional Reconstruction And Quantitation, Rania Yousry Hussein
An Adaptive Algorithm To Identify Ambiguous Prostate Capsule Boundary Lines For Three-Dimensional Reconstruction And Quantitation, Rania Yousry Hussein
Electrical & Computer Engineering Theses & Dissertations
Currently there are few parameters that are used to compare the efficiency of different methods of cancerous prostate surgical removal. An accurate assessment of the percentage and depth of extra-capsular soft tissue removed with the prostate by the various surgical techniques can help surgeons determine the appropriateness of surgical approaches. Additionally, an objective assessment can allow a particular surgeon to compare individual performance against a standard. In order to facilitate 3D reconstruction and objective analysis and thus provide more accurate quantitation results when analyzing specimens, it is essential to automatically identify the capsule line that separates the prostate gland tissue …
Learning As A Nonlinear Line Of Attraction For Pattern Association, Classification And Recognition, Ming-Jung Seow
Learning As A Nonlinear Line Of Attraction For Pattern Association, Classification And Recognition, Ming-Jung Seow
Electrical & Computer Engineering Theses & Dissertations
Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns.
It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter …
Whole Word Phonetic Displays For Speech Articulation Training, Fansheng Meng
Whole Word Phonetic Displays For Speech Articulation Training, Fansheng Meng
Electrical & Computer Engineering Theses & Dissertations
The main objective of this dissertation is to investigate and develop speech recognition technologies for speech training for people with hearing impairments. During the course of this work, a computer aided speech training system for articulation speech training was also designed and implemented. The speech training system places emphasis on displays to improve children's pronunciation of isolated Consonant-Vowel-Consonant (CVC) words, with displays at both the phonetic level and whole word level. This dissertation presents two hybrid methods for combining Hidden Markov Models (HMMs) and Neural Networks (NNs) for speech recognition. The first method uses NN outputs as posterior probability estimators …
Robust Face Representation And Recognition Under Low Resolution And Difficult Lighting Conditions, Mohammad Moinul Islam
Robust Face Representation And Recognition Under Low Resolution And Difficult Lighting Conditions, Mohammad Moinul Islam
Electrical & Computer Engineering Theses & Dissertations
This dissertation focuses on different aspects of face image analysis for accurate face recognition under low resolution and poor lighting conditions. A novel resolution enhancement technique is proposed for enhancing a low resolution face image into a high resolution image for better visualization and improved feature extraction, especially in a video surveillance environment. This method performs kernel regression and component feature learning in local neighborhood of the face images. It uses directional Fourier phase feature component to adaptively lean the regression kernel based on local covariance to estimate the high resolution image. For each patch in the neighborhood, four directional …
A Formal Object Model For Layered Networks To Support Verification And Simulation, Rasha M. B. E. Morsi
A Formal Object Model For Layered Networks To Support Verification And Simulation, Rasha M. B. E. Morsi
Electrical & Computer Engineering Theses & Dissertations
This work presents an abstract formal model of the interconnection structure of the Open Systems Interconnection Reference Model (OSI-RM) developed using Object-Oriented modeling principles permitting it to serve as a re-usable platform in supporting the development of simulations and formal methods applied to layered network protocols. A simulation of the object model using MODSIM III was developed and Prototype Verification System (PVS) was used to show the applicability of the object model to formal methods by formally specifying and verifying a Global Systems for Mobile communications (GSM) protocol. This application has proved to be successful in two aspects. The first …
The Cluster Multipole Algorithm For Far-Field Computations, Rakesh R. Patel
The Cluster Multipole Algorithm For Far-Field Computations, Rakesh R. Patel
Electrical & Computer Engineering Theses & Dissertations
Computer simulations of N-body systems are beneficial to study the overall behavior of a number of physical systems in fields such as astrophysics, molecular dynamics, and computational fluid dynamics. A new approach for computer simulations of N-body systems is proposed in this research. The new algorithm is called the Cluster Multipole Algorithm (CMA). The goals of the new algorithm are to improve the applicability to non-point sources and to provide more control on the accuracy over current algorithms. The algorithm is targeted to applications that do not require rebuilding the data structure about the system every time step due to …
Recognition Of Quadric Surfaces From Range Data: An Analytical Approach, Ivan X. D. D'Cunha
Recognition Of Quadric Surfaces From Range Data: An Analytical Approach, Ivan X. D. D'Cunha
Electrical & Computer Engineering Theses & Dissertations
In this dissertation, a new technique based on analytic geometry for the recognition and description of three-dimensional quadric surfaces from range images is presented. Beginning with the explicit representation of quadrics, a set of ten coefficients are determined for various three-dimensional surfaces. For each quadric surface, a unique set of two-dimensional curves which serve as a feature set is obtained from the various angles at which the object is intersected with a plane. Based on a discriminant method, each of the curves is classified as a parabola, circle, ellipse, hyperbola, or a line. Each quadric surface is shown to be …
A Performance Prediction Model For A Fault-Tolerant Computer During Recovery And Restoration, Rodrigo A. Obando
A Performance Prediction Model For A Fault-Tolerant Computer During Recovery And Restoration, Rodrigo A. Obando
Electrical & Computer Engineering Theses & Dissertations
The modeling and design of a fault-tolerant multiprocessor system is addressed in this dissertation. In particular, the behavior of the system during recovery and restoration after a fault has occurred is investigated. Given that a multicomputer system is designed using the Algorithm to Architecture To Mapping Model (ATAMM) model, and that a fault (death of a computing resource) occurs during its normal steady-state operation, a model is presented as a viable research tool for predicting the performance bounds of the system during its recovery and restoration phases. Furthermore, the bounds of the performance behavior of the system during this transient …
Text-Independent Automatic Speaker Identification Using Partitioned Neural Networks, Laszlo Rudasi
Text-Independent Automatic Speaker Identification Using Partitioned Neural Networks, Laszlo Rudasi
Electrical & Computer Engineering Theses & Dissertations
This dissertation introduces a binary partitioned approach to statistical pattern classification which is applied to talker identification using neural networks. In recent years artificial neural networks have been shown to work exceptionally well for small but difficult pattern classification tasks. However, their application to large tasks (i.e., having more than ten to 20 categories) is limited by a dramatic increase in required training time. The time required to train a single network to perform N-way classification is nearly proportional to the exponential of N. In contrast, the binary partitioned approach requires training times on the order of N2. …
Bounds On Constraint Weight Parameters Of Hopfield Networks For Stability Of Optimization Problem Solutions, Gursel Serpen
Bounds On Constraint Weight Parameters Of Hopfield Networks For Stability Of Optimization Problem Solutions, Gursel Serpen
Electrical & Computer Engineering Theses & Dissertations
The purpose of the presented research is to study the convergence characteristics of Hopfield network dynamics. The relation between constraint weight parameter values and the stability of solutions of constraint satisfaction and optimization problems mapped to Hopfield networks is investigated. A theoretical development relating constraint weight parameter values to solution stability is presented. The dependency of solution stability on constraint weight parameter values is shown employing an abstract optimization problem. A theorem defining bounds on the constraint weight parameter magnitudes for solution stability of constraint satisfaction and optimization problems is proved. Simulation analysis on a set of optimization and constraint …
Performance Modeling And Enhancement For The Atamm Data Flow Architecture, Sukhamoy Som
Performance Modeling And Enhancement For The Atamm Data Flow Architecture, Sukhamoy Som
Electrical & Computer Engineering Theses & Dissertations
Algorithm To Architecture Mapping Model (ATAMM) is a new marked graph model from which the rules for data and control flow in a homogeneous, multicomputer, data flow architecture may be defined. This research is concerned with performance modeling and performance enhancement for periodic execution of large-grain, decision-free algorithms in such an ATAMM defined architecture. Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of computing element requirements. Operating strategies are developed for optimum time performance and for sub-optimum time performance under limited availability of computing elements. An ATAMM simulator is used to test …