Computer Engineering Commons^™

Combining Virtual Reality And Machine Learning For Enhancing The Resiliency Of Transportation Infrastructure In Extreme Events, Supratik Mukhopadhyay, Yimin Zhu, Ravindra Gudishala

Publications

Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, extreme conditions, etc. As a result, the model’s predictions are made at an aggregate level and for a …

Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen

Research Collection School Of Computing and Information Systems

Reachability computation is a fundamental graph functionality with a wide range of applications. In spite of this, little work has as yet been done on efficient reachability queries over temporal graphs, which are used extensively to model time-varying networks, such as communication networks, social networks, and transportation schedule networks. Moreover, we are faced with increasingly large real-world temporal networks that may be distributed across multiple data centers. This state of affairs motivates the paper's study of efficient reachability queries on distributed temporal graphs. We propose an efficient index, called Temporal Vertex Labeling (TVL), which is a labeling scheme for distributed …

Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball

Master's Theses

Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum …

Analyzing Evolution Of Rare Events Through Social Media Data, Xiaoyu Lu

Dissertations

Recently, some researchers have attempted to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of those studies can accurately deduce a time point when social media activities are most highly affected by a rare event because producing an accurate temporal pattern of social media during the evolution of a rare event is very difficult. This work expands the current studies along three directions. Firstly, we focus on the intensity of information volume and propose an innovative clustering …

Optimal Sampling Paths For Autonomous Vehicles In Uncertain Ocean Flows, Andrew J. De Stefan

Dissertations

Despite an extensive history of oceanic observation, researchers have only begun to build a complete picture of oceanic currents. Sparsity of instrumentation has created the need to maximize the information extracted from every source of data in building this picture. Within the last few decades, autonomous vehicles, or AVs, have been employed as tools to aid in this research initiative. Unmanned and self-propelled, AVs are capable of spending weeks, if not months, exploring and monitoring the oceans. However, the quality of data acquired by these vehicles is highly dependent on the paths along which they collect their observational data. The …

An Optimal Design Of A Moving Target Defense For Attack Detection In Control Systems, Paul Griffioen, Sean Weerakkody, Bruno Sinopoli

Faculty Work Comprehensive List

In this paper, we consider the problem of designing system parameters to improve detection of attacks in control systems. Specifically, we study control systems which are vulnerable to integrity attacks on sensors and actuators. We aim to defend against strong model aware adversaries that can read and modify all sensors and actuators. Previous work has proposed a moving target defense for detecting integrity attacks on control systems. Here, an authenticating subsystem with time-varying dynamics coupled to the original plant is introduced. Due to this coupling, an attack on the original system will affect the authenticating subsystem and in turn be …

Implementing A Lightweight Schmidt-Samoa Cryptosystem (Ssc) For Sensory Communications, Qasem Abu Al-Haija, Ibrahim Marouf, Mohammad M. Asad, Kamal Al Nasr

Computer Science Faculty Research

One of the remarkable issues that face wireless sensor networks (WSNs) nowadays is security. WSNs should provide a way to transfer data securely particularly when employed for mission-critical purposes. In this paper, we propose an enhanced architecture and implementation for 128-bit Schmidt-Samoa cryptosystem (SSC) to secure the data communication for wireless sensor networks (WSN) against external attacks. The proposed SSC cryptosystem has been efficiently implemented and verified using FPGA modules by exploiting the maximum allowable parallelism of the SSC internal operations. To verify the proposed SSC implementation, we have synthesized our VHDL coding using Quartus II CAD tool targeting the …

Online Eeg Seizure Detection And Localization, Amirsalar Mansouri, Sanjay P. Singh, Khalid Sayood

Department of Electrical and Computer Engineering: Faculty Publications

Epilepsy is one of the three most prevalent neurological disorders. A significant proportion of patients suffering from epilepsy can be effectively treated if their seizures are detected in a timely manner. However, detection of most seizures requires the attention of trained neurologists-- a scarce resource. Therefore, there is a need for an automatic seizure detection capability. A tunable non-patient-specific, non-seizure-specific method is proposed to detect the presence and locality of a seizure using electroencephalography (EEG) signals. This multifaceted computational approach is based on a network model of the brain and a distance metric based on the spectral profiles of EEG …

Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning, Ansi Zhang, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong, Jianjun Hu

Faculty Publications

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over …

Learnfca: A Fuzzy Fca And Probability Based Approach For Learning And Classification, Suraj Ketan Samal

School of Computing: Dissertations, Theses, and Student Research

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.

This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide …

Machine Learning To Predict The Likelihood Of A Personal Computer To Be Infected With Malware, Maryam Shahini, Ramin Farhanian, Marcus Ellis

SMU Data Science Review

In this paper, we present a new model to predict the prob- ability that a personal computer will become infected with malware. The dataset is selected from a Kaggle competition supported by Mi- crosoft. The data includes computer configuration, owner information, installed software, and configuration information. In our research, sev- eral classification models are utilized to assign a probability of a machine being infected with malware. The LightGBM classifier is the optimum machine learning model by performing faster with higher efficiency and lower memory usage in this research. The LightGBM algorithm obtained a cross-validation ROC-AUC score of 74%. Leading factors …

Aws Ec2 Instance Spot Price Forecasting Using Lstm Networks, Jeffrey Lancon, Yejur Kunwar, David Stroud, Monnie Mcgee, Robert Slater

SMU Data Science Review

Cloud computing is a network of remote computing resources hosted on the Internet that allow users to utilize cloud resources on demand. As such, it represents a paradigm shift in the way businesses and industries think about digital infrastructure. With the shift from IT resources being a capital expenditure to a managed service, companies must rethink how they approach utilizing and optimizing these resources in order to maximize productivity and minimize costs. With proper resource management, cloud resources can be instrumental in reducing computing expenses.

Cloud resources are perishable commodities; therefore, cloud service providers have developed strategies to maximize utilization …

Non-Parametric Combination Analysis Of Multiple Data Types Enables Detection Of Novel Regulatory Mechanisms In T Cells Of Multiple Sclerosis Patients., Sunjay Jude Fernandes, Hiromasa Morikawa, Ewoud Ewing, Sabrina Ruhrmann, Rubin Narayan Joshi, Vincenzo Lagani, Nestoras Karathanasis, Mohsen Khademi, Nuria Planell, Angelika Schmidt, Ioannis Tsamardinos, Tomas Olsson, Fredrik Piehl, Ingrid Kockum, Maja Jagodic, Jesper Tegnér, David Gomez-Cabrero

Computational Medicine Center Faculty Papers

Multiple Sclerosis (MS) is an autoimmune disease of the central nervous system with prominent neurodegenerative components. The triggering and progression of MS is associated with transcriptional and epigenetic alterations in several tissues, including peripheral blood. The combined influence of transcriptional and epigenetic changes associated with MS has not been assessed in the same individuals. Here we generated paired transcriptomic (RNA-seq) and DNA methylation (Illumina 450 K array) profiles of CD4+ and CD8+ T cells (CD4, CD8), using clinically accessible blood from healthy donors and MS patients in the initial relapsing-remitting and subsequent secondary-progressive stage. By integrating the output of a …

Recent Trends, Current Research In Cyberpsychology: A Literature Review, Amarjit Kumar Singh, Pawan Kumar Singh

Library Philosophy and Practice (e-journal)

Cyberpsychology refers to the study of the mind and behavior in the context of interactions with technology. It is an emerging branch, which has focused on the psychological aspects connected to the increasing presence and usages of technology in modern lives. This paper traces recent advancement and trends of Cyberpsychology is an emerging domain of knowledge and goes on the give a literature review of the same. An analysis of the recent research and literature covering 300 most relevant research papers from the period of 2012 to 15, August 2019 was conducted to determine and shape the research pattern based …

Decoupling Information And Connectivity Via Information-Centric Transport, Hila Ben Abraham

McKelvey School of Engineering Graduate Student Theses & Dissertations

The power of Information-Centric Networking architectures (ICNs) lies in their abstraction for communication --- the request for named data. This abstraction was popularized by the HyperText Transfer Protocol (HTTP) as an application-layer abstraction, and was extended by ICNs to also serve as their network-layer abstraction. In recent years, network mechanisms for ICNs, such as scalable name-based forwarding, named-data routing and in-network caching, have been widely explored and researched. However, to the best of our knowledge, the impact of this network abstraction on ICN applications has not been explored or well understood. The motivation of this dissertation is to address this …

Polarization Division Multiplexing For Optical Data Communications, Darko Ivanovich

McKelvey School of Engineering Graduate Student Theses & Dissertations

Multiple parallel channels are ubiquitous in optical communications, with spatial division multiplexing (separate physical paths) and wavelength division multiplexing (separate optical wavelengths) being the most common forms. In this research work, we investigate the viability of polarization division multiplexing, the separation of distinct parallel optical communication channels through the polarization properties of light. We investigate polarization division multiplexing based optical communication systems in five distinct parts. In the first part of the work, we define a simulation model of two or more linearly polarized optical signals (at different polarization angles) that are transmitted through a common medium (e.g., air), filtered …

A Tutorial On Detecting Security Attacks On Cyber-Physical Systems, Paul Griffioen, Sean Weerakkody, Bruno Sinopoli, Omur Ozel, Yilin Mo

Faculty Work Comprehensive List

Cyber-physical systems (CPSs) have become targets for malicious adversaries, creating new challenges for attaining reliable CPS performance. Achieving CPS security requires tools which extend beyond what is offered in state of the art software and cyber security. In this tutorial, we consider a science of CPS security which combines tools from both cyber security and system theory to defend against adversarial behavior. We discuss realistic and intelligent attack models from an adversarial perspective and then present mechanisms for defenders to achieve resilience by recognizing and responding to such malicious behavior. The focus of this tutorial is particularly on the recognition …

Knn Optimization For Multi-Dimensional Data, Arialdis Japa

Master of Science in Computer Science Theses

The K-Nearest Neighbors (KNN) algorithm is a simple but powerful technique used in the field of data analytics. It uses a distance metric to identify existing samples in a dataset which are similar to a new sample. The new sample can then be classified via a class majority voting of its most similar samples, i.e. nearest neighbors. The KNN algorithm can be applied in many fields, such as recommender systems where it can be used to group related products or predict user preferences. In most cases, the performance of the KNN algorithm tends to suffer as the size of the …

Optimizing Impression Counts For Outdoor Advertising, Yipeng Zhang, Yuchen Li, Zhifeng Bao, Songsong Mo, Ping Zhang

Research Collection School Of Computing and Information Systems

In this paper we propose and study the problem of optimizing theinfluence of outdoor advertising (ad) when impression counts aretaken into consideration. Given a database U of billboards, each ofwhich has a location and a non-uniform cost, a trajectory databaseT and a budget B, it aims to find a set of billboards that has themaximum influence under the budget. In line with the advertisingconsumer behavior studies, we adopt the logistic function to takeinto account the impression counts of an ad (placed at differentbillboards) to a user trajectory when defining the influence measurement. However, this poses two challenges: (1) our problemis …

Coresets For Minimum Enclosing Balls Over Sliding Windows, Yanhao Wang, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Coresets are important tools to generate concise summaries of massive datasets for approximate analysis. A coreset is a small subset of points extracted from the original point set such that certain geometric properties are preserved with provable guarantees. This paper investigates the problem of maintaining a coreset to preserve the minimum enclosing ball (MEB) for a sliding window of points that are continuously updated in a data stream. Although the problem has been extensively studied in batch and append-only streaming settings, no efficient sliding-window solution is available yet. In this work, we first introduce an algorithm, called AOMEB, to build …

Subsurface Mimo: A Beamforming Design In Internet Of Underground Things For Digital Agriculture Applications, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required for the degree of …

Bleeding Out: The Case For Strengthening Healthcare Client Portal Data Privacy Regulations, Matthew D. Mccord

Minnesota Journal of Law, Science & Technology

No abstract provided.

Centralizing Energy Consumption Data In State Energy Data Centers, Zach Sibley

Minnesota Journal of Law, Science & Technology

No abstract provided.

Guest Editorial Special Issue On Toward Securing Internet Of Connected Vehicles (Iov) From Virtual Vehicle Hijacking, Yue Cao, Houbing Song, Omprakash Kaiwartya, Sinem Coleri Ergen, Jaime Lloret, Naveed Ahmad

Publications

Today’s vehicles are no longer stand-alone transportation means, due to the advancements on vehicle-tovehicle (V2V) and vehicle-to-infrastructure (V2I) communications enabled to access the Internet via recent technologies in mobile communications, including WiFi, Bluetooth, 4G, and even 5G networks. The Internet of vehicles was aimed toward sustainable developments in transportation by enhancing safety and efficiency. The sensor-enabled intelligent automation of vehicles’ mechanical operations enhances safety in on-road traveling, and cooperative traffic information sharing in vehicular networks improves traveling efficiency.

Designing Cloud Computing Architecture For Bank Industry The Case Of Dashen Bank, Melaku Yenew

African Conference on Information Systems and Technology

Technology makes life easy. People contact banks in their day to day life activity. And also the banks are committed to serve their customers with the help of currently advanced technology. The aim of a bank is to give consistent and satisfactory banking services for the customers. The use of advanced technology in banking requires sophisticated knowledge of the technology and expertise and a large number of employees are required for implementation and management of that system.

Cloud computing makes easy the management of IT infrastructure and the bank sector systems. Cloud service providers provide three basic types of services: …

Model-Driven Engineering For Big Data, Belachew Regane

African Conference on Information Systems and Technology

Accessing heterogeneous and huge amount of data through different sources heavily impacts users of data nowadays worldwide. Thus, Big Data has now become a hot emerging paradigm in computing environments. Issues in scalability, interoperability, platform independency, adaptability and reusability in big data systems are considered the main current challenges. This raises the need for appropriate software engineering approaches to develop effective and efficient Big Data system models, i.e. an approach which reduce investment cost and development time. Today, software engineering has emerged advanced methodologies to solve problems from different perspectives, while still further research is needed to overcome new challenges …

A Study Of The Electron Regeneration Efficiency Of Solar Cells Fabricated Using Cmc/Pva-, Alginate-, And Xanthan-Based Electrolytes, Nur Farha Shaafi, Saifful Kamaluddin Muzakir, Bouchta Sahraoui

Makara Journal of Technology

A photovoltaic (PV) mechanism consists of three important steps, i.e., (i) electron excitation upon absorption of photon with energy higher than the bandgap of fluorophore, (ii) excited-state electron injection from the fluorophore to the pho-toelectrode, and (iii) electron regeneration from the electrolyte to the fluorophore. An efficient electron regeneration could be achieved upon fulfillment of the requirements of energy alignment, i.e., lowest unoccupied molecular orbital of fluorophore (LUMOfluorophore) > redox potential of electrolyte > highest occupied molecular orbital of fluorophore (HOMOfluorophore). This study investigated the electron regeneration efficiency of excitonic solar cells fabricated using three polymer-based electrolytes, i.e., (i) 60% carboxymethyl cellulose …

Use Of The “Dnachecker” Algorithm For Improving Bioinformatics Research, Nausheen Bhat, Ezra Bernadus Wijaya, Arli Aditya Parikesit

Makara Journal of Technology

Basic Local Alignment Sequencing Tool (BLAST) is a bioinformatics tool used for analyzing nucleotide sequences with regards to their similarity. BLAST can be found online on biological databases such as the National Center for Biotechnology Information (NCBI) and other such repositories. The mechanism of BLAST allows the target sequence to be compared with other sequences to find regions of local similarity, and thus, a comparability quotient that determines the resemblance between the sequences is created. Due to the open-platform nature of the online databanks, several sequences can be accepted with little to no interjections regarding the quality of sequence submitted. …

Study Of Ecological Design Of Residential Complexes Using Spss To Elevate Resident Satisfaction, Soheil Malekpour Kolbadinejad, V. Malekpour Ravasjan, A. Darvish

Makara Journal of Technology

This article reports the general residential satisfaction, affecting ecological factors, and their importance in area in Tehran. The collected field data was analyzed using SPSS. Cronbach's alpha variable was used to determine the reliability of the questionnaire and the Friedman test was used to assess priorities that influence residential satisfaction. The results show that the quality of residential environments and general satisfaction of their residents may be enhanced by identifying these criteria and making appropriate plans for their improvement.

Adsorption Of Heavy Metal Ions By Oil Palm Decanter Cake Activated Carbon, Mohd. Ezreeza Mohamed Yusoff, Juferi Idris, Nahrul Hayawin Zainal, Mohamad Faizal Ibrahim, Suraini Abd-Aziz

Makara Journal of Technology

Adsorption processes are widely used for the removal of heavy metals from waste streams. Oil palm decanter cake (OPDC) is used as a bioadsorbent because of its compositional properties for activated carbon production; moreover, it is a readily available raw material produced in palm oil mills and is thus abundant and cheap. In this study, the OPDC was carbonized at 700 °C and activated using steam at 700 °C to produce oil palm decanter cake activated carbon (AC-OPDC). Batch adsorption experiments were carried out to compare the adsorption capacities of the raw OPDC and the AC-OPDC for heavy metals removal. …