Engineering Commons^™

Pixel-Level Deep Multi-Dimensional Embeddings For Homogeneous Multiple Object Tracking, Mateusz Mittek

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, …

Data Analytics And Machine Learning To Enhance The Operational Visibility And Situation Awareness Of Smart Grid High Penetration Photovoltaic Systems, Aditya Sundararajan

FIU Electronic Theses and Dissertations

Electric utilities have limited operational visibility and situation awareness over grid-tied distributed photovoltaic systems (PV). This will pose a risk to grid stability when the PV penetration into a given feeder exceeds 60% of its peak or minimum daytime load. Third-party service providers offer only real-time monitoring but not accurate insights into system performance and prediction of productions. PV systems also increase the attack surface of distribution networks since they are not under the direct supervision and control of the utility security analysts.

Six key objectives were successfully achieved to enhance PV operational visibility and situation awareness: (1) conceptual cybersecurity …

Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise, Mehdi Mafi

FIU Electronic Theses and Dissertations

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise.

In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, …

Machine Learning Methodology Review For Computational Electromagnetics, He Ming Yao, Lijun Jiang, Huan Huan Zhang, Wei E.I. Sha

Electrical and Computer Engineering Faculty Research & Creative Works

While machine learning is revolutionizing every corner of modern technologies, we have been attempting to explore whether machine learning methods could be used in computational electromagnetic (CEM). In this paper, five efforts in line with this direction are reviewed. They include forward methods such as the method of moments (MoM) solved by the artificial neural network training process, FDTD PML (perfectly matched layer) using the hyperbolic tangent basis function (HTBF), etc. There are also inverse problems that use the deep ConvNets for the effective source reconstruction and subwavelength imaging in the far-field. Benchmarks are provided to demonstrate the feasibility of …

Comparative Analysis Of Feature Selection Methods To Identify Biomarkers In A Stroke-Related Dataset, Thomas Clifford, Justin Bruce, Tayo Obafemi-Ajayi, John Matta

Electrical and Computer Engineering Faculty Research & Creative Works

This paper applies machine learning feature selection techniques to the REGARDS stroke-related dataset to identify health-related biomarkers. A data-driven methodological framework is presented to evaluate multiple feature selection methods. In applying the framework, three classifiers are chosen in conjunction with two wrappers, and their performance with diverse classification targets such as Current Smoker, Current Alcohol Use, and Deceased is evaluated. The performance across logistic regression, random forest and naïve Bayes classifier methods, as quantified by the ROC Area Under Curve metric and selected features, was similar. However, significant differences were observed in running time. Performance of the selected features was …

A Hardware-Assisted Insider Threat Detection And Prevention Framework, Kyle W. Denney

FIU Electronic Theses and Dissertations

Today, the USB protocol is among the most widely used protocols. However, the mass-proliferation of USB has led to a threat vector wherein USB devices are assumed innocent, leaving computers open to an attack. Malicious USB devices can disguise themselves as benign devices to insert malicious commands to connected end devices. A rogue device appears benign to the average OS, requiring advanced detection schemes to identify malicious devices. However, using system-level hooks, advanced threats may subvert OS-reliant detection schemes. This thesis showcases USB-Watch, a hardware-based USB threat detection framework. The hardware can collect live USB traffic before the data can …

Data-Driven Integral Reinforcement Learning For Continuous-Time Non-Zero-Sum Games, Yongliang Yang, Liming Wang, Hamidreza Modares, Dawei Ding, Yixin Yin, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper develops an integral value iteration (VI) method to efficiently find online the Nash equilibrium solution of two-player non-zero-sum (NZS) differential games for linear systems with partially unknown dynamics. To guarantee the closed-loop stability about the Nash equilibrium, the explicit upper bound for the discounted factor is given. To show the efficacy of the presented online model-free solution, the integral VI method is compared with the model-based off-line policy iteration method. Moreover, the theoretical analysis of the integral VI algorithm in terms of three aspects, i.e., positive definiteness properties of the updated cost functions, the stability of the closed-loop …

Source Reconstruction Method Based On Machine Learning Algorithms, He Ming Yao, Lijun Jiang, Wei E.I. Sha

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a new source reconstruction method (SRM) based on deep learning. The conventional SRM usually requires oversampled measurements data to ensure higher accuracy. Thus, conventional SRM numerical system is usually highly singular. A deep convolutional neural network (ConvNet) is proposed to reconstruct the equivalent sources of the target to overcome difficulty. The deep ConvNet allows us to employ less data samples. Besides, the ill-conditioned numerical system can be effectively avoided. Numerical examples are presented to demonstrate the feasibility and accuracy of the proposed method. Its performance is also compared with the traditional neural network and interpolation method. Moreover, …

Qoe Enhancement In Next Generation Wireless Ecosystems: A Machine Learning Approach, Eva Ibarrola, Mark Davis, Camille Voisin, Ciara Close, Leire Cristobo

Articles

Next-generation wireless ecosystems are expected to comprise heterogeneous technologies and diverse deployment scenarios. Ensuring quality of service (QoS) will be one of the major challenges on account of a variety of factors that are beyond the control of network and service providers in these environments. In this context, ITU-T is working on defining new Recommendations related to QoS and users' quality of experience (QoE) for the 5G era. Considering the new ITU-T QoS framework, we propose a methodology to develop a global QoS management model for next generation wireless ecosystems taking advantage of big data and machine learning (ML). The …

Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a …

Predicting Cascading Failures In Power Grids Using Machine Learning Algorithms, Rezoan Ahmed Shuvro, Pankaz Das, Majeed M. Hayat, Mitun Talukder

Electrical and Computer Engineering Faculty Research and Publications

Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of …

Applying Deep Learning Approach To The Far-Field Subwavelength Imaging Based On Near-Field Resonant Metalens At Microwave Frequencies, He Ming Yao, Min Li, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we utilize the deep learning approach for the subwavelength imaging in far-field, which is realized by the near-field resonant metalens at microwave frequencies. The resonating metalens consisting of split-ring resonators (SRRs) are equipped with the strong magnetic coupling ability and can convert evanescent waves into propagating waves using the localized resonant modes. The propagating waves in the far-field are utilized as the input of a trained deep convolutional neural network (CNN) to realize the imaging. The training data for establishing the deep CNN are obtained by the EM simulation tool. Besides, the white Gaussian noise is added …

Machine-Learning-Based Pml For The Fdtd Method, He Ming Yao, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

In this letter, a novel absorbing boundary condition (ABC) computation method for finite-difference time-domain (FDTD) is proposed based on the machine learning approach. The hyperbolic tangent basis function (HTBF) neural network is introduced to replace traditional perfectly matched layer (PML) ABC during the FDTD solving process. The field data on the interface of conventional PML are employed to train HTBF-based PML model. Compared to the conventional approach, the novel method greatly decreases the size of a computation domain and the computation complexity of FDTD because the new model only involves the one-cell boundary layer. Numerical examples are provided to benchmark …

Identifying Key Topics Bearing Negative Sentiment On Twitter: Insights Concerning The 2015-2016 Zika Epidemic, Ravali Mamidi, Michele Miller, Tanvi Banerjee, William Romine, Amit Sheth

Publications

Background To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies’ efforts to disseminate relevant information to the public about Zika-related issues.

Objective The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed.

Methods Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised …