Engineering Commons^™

Formal Modeling And Analysis Of A Family Of Surgical Robots, Niloofar Mansoor

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

Safety-critical applications often use dependability cases to validate that specified properties are invariant, or to demonstrate a counterexample showing how that property might be violated. However, most dependability cases are written with a single product in mind. At the same time, software product lines (families of related software products) have been studied with the goal of modeling variability and commonality and building family-based techniques for both modeling and analysis. This thesis presents a novel approach for building an end to end dependability case for a software product line, where a property is formally modeled, a counterexample is found and then …

Image Classification Using Fuzzy Fca, Niruktha Roy Gotoor

Department of Computer Science and Engineering: 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. It has been used in various domains such as data mining, machine learning, semantic web, Sciences, for the purpose of data analysis and Ontology over the last few decades. Various extensions of FCA are being researched to expand it's scope over more departments. In this thesis,we review the theory of Formal Concept Analysis (FCA) and its extension Fuzzy FCA. Many studies to use FCA in data mining and text learning have been pursued. We extend these studies to include …

Tuning Networks For Prosocial Behavior: From Senseless Swarms To Smart Mobs [Commentary], Sun Sun Lim, Roland Bouffanais

Research Collection College of Integrative Studies

Social media have been seen to accelerate the spread of negative content such as disinformation and hate speech, often unleashing a reckless herd mentality within networks, further aggravated by malicious entities using bots for amplification. So far, the response to this emerging global crisis has centered around social media platform companies making reactive moves that appear to have greater symbolic value than practical utility. Proposes a solution to favor prosocial behavior via social networks.

Ldakm-Eiot: Lightweight Device Authentication And Key Management Mechanism For Edge-Based Iot Deployment, Mohammad Wazid, Ashok Kumar Das, Sachin Shetty, Joel J. P. C. Rodrigues, Youngho Park

VMASC Publications

In recent years, edge computing has emerged as a new concept in the computing paradigm that empowers several future technologies, such as 5G, vehicle-to-vehicle communications, and the Internet of Things (IoT), by providing cloud computing facilities, as well as services to the end users. However, open communication among the entities in an edge based IoT environment makes it vulnerable to various potential attacks that are executed by an adversary. Device authentication is one of the prominent techniques in security that permits an IoT device to authenticate mutually with a cloud server with the help of an edge node. If authentication …

Where We Are With Enterprise Architecture, Leila Halawi, Richard Mccarthy, James Farah

Publications

Enterprise architecture has been continuously developing since the mid-1980s. Although there is now 35 years of research and use, there is still a lack consistent definitions and standards. This is apparent in the proliferation of so many different enterprise architecture frameworks. Despite the significant body of research, there is a need for standardization of terminology based upon a meta-analysis of the literature. Enterprise architecture programs require commitment throughout an organization to be effective and must be perceived to add value. This research offers an initial basis for researchers who need to expand and continue this research topic with an actual …

Seer: An Explainable Deep Learning Midi-Based Hybrid Song Recommender System, Khalil Damak, Olfa Nasraoui

Faculty Scholarship

State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainability and solving the cold start problem. In this work, we propose a hybrid deep learning …

Digitalization In Practice: The Fifth Discipline Advantage, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

Purpose The purpose of this paper is to provide advice to organizations on how to become successful in the digital age. The paper revisits Peter Senge's (1990) notion of the learning organization and discusses the relevance of systems thinking and the other four disciplines, namely, personal mastery, mental models, shared vision and team learning in the context of the current digitalization megatrend. Design/methodology/approach This paper is based on content analysis of essays from international organizations, strategy experts and management scholars, and insights gained from the author's consulting experience. A comparative case study from the health and social sector is also …

Robot Simulation Analysis, Jacob Miller, Jeremy Evert

Student Research

• Simulate virtual robot for test and analysis

• Analyze SLAM solutions using ROS

• Assemble a functional Turtlebot

• Emphasize projects related to current research trajectories for NASA, and general robotics applications

Reputation-Aware Trajectory-Based Data Mining In The Internet Of Things (Iot), Samia Tasnim

FIU Electronic Theses and Dissertations

Internet of Things (IoT) is a critically important technology for the acquisition of spatiotemporally dense data in diverse applications, ranging from environmental monitoring to surveillance systems. Such data helps us improve our transportation systems, monitor our air quality and the spread of diseases, respond to natural disasters, and a bevy of other applications. However, IoT sensor data is error-prone due to a number of reasons: sensors may be deployed in hazardous environments, may deplete their energy resources, have mechanical faults, or maybe become the targets of malicious attacks by adversaries. While previous research has attempted to improve the quality of …

Trajectory Privacy Preservation And Lightweight Blockchain Techniques For Mobility-Centric Iot, Abdur Bin Shahid

FIU Electronic Theses and Dissertations

Various research efforts have been undertaken to solve the problem of trajectory privacy preservation in the Internet of Things (IoT) of resource-constrained mobile devices. Most attempts at resolving the problem have focused on the centralized model of IoT, which either impose high delay or fail against a privacy-invading attack with long-term trajectory observation. These proposed solutions also fail to guarantee location privacy for trajectories with both geo-tagged and non-geo-tagged data, since they are designed for geo-tagged trajectories only. While a few blockchain-based techniques have been suggested for preserving trajectory privacy in decentralized model of IoT, they require large storage capacity …

Malware Analysis For Evaluating The Integrity Of Mission Critical Devices, Robert Heras

FIU Electronic Theses and Dissertations

The rapid evolution of technology in our society has brought great advantages, but at the same time it has increased cybersecurity threats. At the forefront of these threats is the proliferation of malware from traditional computing platforms to the rapidly expanding Internet-of-things. Our research focuses on the development of a malware detection system that strives for early detection as a means of mitigating the effects of the malware's execution.

The proposed scheme consists of a dual-stage detector providing malware detection for compromised devices in order to mitigate the devices malicious behavior. Furthermore, the framework analyzes task structure features as well …

Security Of The Internet Of Things: Vulnerabilities, Attacks And Countermeasures, Ismail Butun, Houbing Song, Patrik Osterberg

Publications

Wireless Sensor Networks (WSNs) constitute one of the most promising third-millennium technologies and have wide range of applications in our surrounding environment. The reason behind the vast adoption of WSNs in various applications is that they have tremendously appealing features, e.g., low production cost, low installation cost, unattended network operation, autonomous and longtime operation. WSNs have started to merge with the Internet of Things (IoT) through the introduction of Internet access capability in sensor nodes and sensing ability in Internet-connected devices. Thereby, the IoT is providing access to huge amount of data, collected by the WSNs, over the Internet. Hence, …

Water Pipeline Leakage Detection Based On Machine Learning And Wireless Sensor Networks, Yang Liu, Xuehui Ma, Yong Tie, Yinghui Zhang, Jing Gao

Department of Electrical and Computer Engineering: Faculty Publications

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and …

Machine Current Sensor Fdi Strategy In Pmsms, Haibo Li, Yi Qian, Sohrab Asgarpoor, Hamid Sharif

Department of Electrical and Computer Engineering: Faculty Publications

This work proposes a machine current sensor fault detection and isolation (FDI) strategy in permanent magnet synchronous machines (PMSMs) resilient to multiple faults. The fault detection is performed by comparing the measured and estimated DC link currents. The fault isolation is achieved according to machine phase signal estimation and the corresponding residual examination. Single sensor fault, multiple sensor faults and non-sensor fault are covered by the proposed FDI method. The proposed sensor FDI method is not influenced by machine imbalance, feasible for FDI of both single and multiple machine current sensor faults, and capable of distinguishing between machine current sensor …

Algorithms And Circuits For Analog-Digital Hybrid Multibeam Arrays, Paboda Viduneth A. Beruwawela Pathiranage

FIU Electronic Theses and Dissertations

Fifth generation (5G) and beyond wireless communication systems will rely heavily on larger antenna arrays combined with beamforming to mitigate the high free-space path-loss that prevails in millimeter-wave (mmW) and above frequencies. Sharp beams that can support wide bandwidths are desired both at the transmitter and the receiver to leverage the glut of bandwidth available at these frequency bands. Further, multiple simultaneous sharp beams are imperative for such systems to exploit mmW/sub-THz wireless channels using multiple reflected paths simultaneously. Therefore, multibeam antenna arrays that can support wider bandwidths are a key enabler for 5G and beyond systems.

In general, N- …

Bibliometric Survey Of Privacy Of Social Media Network Data Publishing, Rupali Gangarde Ass. Prof., Amit Sharma Dr., Ambika Pawar Dr.

Library Philosophy and Practice (e-journal)

We are witness to see exponential growth of the social media network since the year 2002. Leading social media networking sites used by people are Twitter, Snapchats, Facebook, Google, and Instagram, etc. The latest global digital report (Chaffey and Ellis-Chadwick 2019) states that there exist more than 800 million current online social media users, and the number is still exploding day by day. Users share their day to day activities such as their photos and locations etc. on social media platforms. This information gets consumed by third party users, like marketing companies, researchers, and government firms. Depending upon the purpose, …

The Art Of Selecting Phd Students: Combination Of Bibliometric And Ahp Approach, Preeti Mulay Dr., Rahul Raghvendra Joshi Prof., Sophia Gaikwad Dr.

Library Philosophy and Practice (e-journal)

For the PhD guide or the advisor selecting the accurate PhD scholar is the most elephantine task. It actually requires an art for the perfect selection; as the length, breadth, depth and volume of PhD work is spread across the years and this relationship between the scholar and the guide should start and flourish positively for the immense experience throughout the PhD process. Hence it was essential to understand bibliometric details including how many researchers have already published their contributions in the form of papers and patents, in the Scopus database. In addition to the bibliometric details, in this study, …

Aspect And Opinion Aware Abstractive Review Summarization With Reinforced Hard Typed Decoder, Yufei Tian, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we study abstractive review summarization. Observing that review summaries often consist of aspect words, opinion words and context words, we propose a two-stage reinforcement learning approach, which first predicts the output word type from the three types, and then leverages the predicted word type to generate the final word distribution. Experimental results on two Amazon product review datasets demonstrate that our method can consistently outperform several strong baseline approaches based on ROUGE scores.

Personality Prediction Through Curriculam Vitae Analysis Involving Password Encryption And Prediction Analysis, Gagandeep Kaur, Shruti Maheshwari

Library Philosophy and Practice (e-journal)

A recruitment process requires an eligibility check, an aptitude evaluation and a psychometric analysis of prospective candidates. The work puts forward an application where the system allows employers to post new job offerings and registered candidates can apply. The application estimates applicant’s emotional aptitude through a psychometric analysis based on a test whereas the professional standard is verified via a technical aptitude test. OCEAN Model is used to assess emotional quotient and predict the personality traits. Machine learning techniques such as Logistic Regression are used for modelling the personality predictor. The details of the candidates are kept secure by using …

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 …

Automated Dynamic Detection Of Self-Hiding Behaviors, Luke Baird

Student Works

Certain Android applications, such as but not limited to malware, conceal their presence from the user, exhibiting a self-hiding behavior. Consequently, these apps put the user’s security and privacy at risk by performing tasks without the user’s awareness. Static analysis has been used to analyze apps for self-hiding behavior, but this approach is prone to false positives and suffers from code obfuscation. This research proposes a set of three tools utilizing a dynamic analysis method of detecting self-hiding behavior of an app in the home, installed, and running application lists on an Android emulator. Our approach proves both highly accurate …

A Co-Optimal Coverage Path Planning Method For Aerial Scanning Of Complex Structures, Zhexiong Shang, Justin Bradley, Zhigang Shen

Department of Construction Engineering and Management: Faculty Publications

The utilization of unmanned aerial vehicles (UAVs) in survey and inspection of civil infrastructure has been growing rapidly. However, computationally efficient solvers that find optimal flight paths while ensuring high-quality data acquisition of the complete 3D structure remains a difficult problem. Existing solvers typically prioritize efficient flight paths, or coverage, or reducing computational complexity of the algorithm – but these objectives are not co-optimized holistically. In this work we introduce a co-optimal coverage path planning (CCPP) method that simultaneously co-optimizes the UAV path, the quality of the captured images, and reducing computational complexity of the solver all while adhering to …

A Comparison Of Contextual Bandit Approaches To Human-In-The-Loop Robot Task Completion With Infrequent Feedback, Matt Mcneill, Damian Lyons

Faculty Publications

Artificially intelligent assistive agents are playing an increased role in our work and homes. In contrast with currently predominant conversational agents, whose intelligence derives from dialogue trees and external modules, a fully autonomous domestic or workplace robot must carry out more complex reasoning. Such a robot must make good decisions as soon as possible, learn from experience, respond to feedback, and rely on feedback only as much as necessary. In this research, we narrow the focus of a hypothetical robot assistant to a room tidying task in a simulated domestic environment. Given an item, the robot chooses where to put …

Deep Learning (Partly) Demystified, Vladik Kreinovich, Olga Kosheleva

Departmental Technical Reports (CS)

Successes of deep learning are partly due to appropriate selection of activation function, pooling functions, etc. Most of these choices have been made based on empirical comparison and heuristic ideas. In this paper, we show that many of these choices -- and the surprising success of deep learning in the first place -- can be explained by reasonably simple and natural mathematics.

Computing Without Computing: Dna Version, Vladik Kreinovich, Julio C. Urenda

Departmental Technical Reports (CS)

The traditional DNA computing schemes are based on using or simulating DNA-related activity. This is similar to how quantum computers use quantum activities to perform computations. Interestingly, in quantum computing, there is another phenomenon known as computing without computing, when, somewhat surprisingly, the result of the computation appears without invoking the actual quantum processes. In this chapter, we show that similar phenomenon is possible for DNA computing: in addition to the more traditional way of using or simulating DNA activity, we can also use DNA inactivity to solve complex problems. We also show that while DNA computing without …

Why Deep Learning Is More Efficient Than Support Vector Machines, And How It Is Related To Sparsity Techniques In Signal Processing, Laxman Bokati, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Several decades ago, traditional neural networks were the most efficient machine learning technique. Then it turned out that, in general, a different technique called support vector machines is more efficient. Reasonably recently, a new technique called deep learning has been shown to be the most efficient one. These are empirical observations, but how we explain them -- thus making the corresponding conclusions more reliable? In this paper, we provide a possible theoretical explanation for the above-described empirical comparisons. This explanation enables us to explain yet another empirical fact -- that sparsity techniques turned out to be very efficient in signal …

Ridesourcing Systems: A Framework And Review, Hai Wang, Hai Yang

Research Collection School Of Computing and Information Systems

With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation indus- try. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensi- tive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform …

Flexibility Of Remediation Methods For Winding Open Circuit Faults In A Multiphase Pm Machine Considering Iron Losses Minimization, Fan Wu, Ayman M. El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

The flexibility of post-fault control in multiphase machine systems stems from their multiple degrees of freedom. A post-fault loss-minimization method is proposed and investigated in this paper, in which both the machine copper and iron losses are considered during the derivation of post-fault remediation methods. Therefore, machine efficiency during post-fault operation can be further improved compared to the conventional stator-ohmic-loss-minimization approach. In addition, the combination of three key factors/constraints that can influence the post-fault control strategy of a six-phase permanent magnet (PM) machine has been investigated. By comparing four selected remediation methods based on three constraints, the pros and cons …

2019 (Fall) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department

ENSI Informer Magazine Archive

The ENSI Informer Magazine published in the fall of 2019.

An Open-Source Integration Platform For Multiple Peripheral Modules With Kuka Robots, Mahyar Abdeetedal, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

This paper presents an open-source software interface for the integration of a Kuka robot with peripheral tools and sensors, KUI: Kuka User Interface. KUI is developed based on Kuka Fast Research Interface (FRI) which enables soft real-time control of the robot. Simulink Desktop Real-Time™ or any User Datagram Protocol (UDP) client can send real-time commands to Kuka robot via KUI. In KUI, third-party tools can be added and controlled synchronously with Kuka light-weight robot (LWR). KUI can send the control commands via serial communication to the attached devices. KUI can generate low-level commands using data acquisition (DAQ) boards. This feature …