Computer Engineering Commons^™

Optimal Mission Planning Of Autonomous Mobile Agents For Applications In Microgrids, Sensor Networks, And Military Reconnaissance, Casey D. Majhor

Dissertations, Master's Theses and Master's Reports

As technology advances, the use of collaborative autonomous mobile systems for various applications will become evermore prevalent. One interesting application of these multi-agent systems is for autonomous mobile microgrids. These systems will play an increasingly important role in applications such as military special operations for mobile ad-hoc power infrastructures and for intelligence, surveillance, and reconnaissance missions. In performing these operations with these autonomous energy assets, there is a crucial need to optimize their functionality according to their specific application and mission. Challenges arise in determining mission characteristics such as how each resource should operate, when, where, and for how long. …

Selective Personalization And Group Profiles For Improved Web Search Personalization, Samira Karimi Mansoub, Gönenç Ercan, İlyas Çi̇çekli̇

Turkish Journal of Electrical Engineering and Computer Sciences

Personalization is a common technique used in Web search engines to improve the effectiveness of retrieval. While personalizing some queries yields significant improvements in user experience by providing a ranking in line with the user preferences, it fails to improve or even degrades the effectiveness for less ambiguous queries. A potential personalization metric could improve search engines by selectively applying personalization. One such measure, click entropy uses the query history and the clicked documents for the query, which might be sparse for some queries. In this article, the topic entropy measure is improved by integrating the user distribution into the …

An Alternative Method Of Biomedical Signal Transmission Through The Gsm Voice Channel, Seyedmohsen Dehghan, Mehmet Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, a new solution for online and accurate biomedical data transmission is presented. For this purpose, a global system for mobile (GSM) communication voice channel is, for the first time, used as a communication link between the patient and healthcare provider. Biomedical signals are converted into speech-like signals before being transferred over a GSM voice channel. On the receiver side, speech-like symbols are stored in a symbols bank, and constructed using random stochastic signals. On the receiver end, the index of the symbol with the most similarity to the received signal is selected as the identified sample. This …

Wavelength Sensitivity Of Indium Tin Oxide On Surface Plasmon Resonance Angles, Antonio Ruiz, Carlos Villa Angulo, Ivan Olaf Hernandez-Fuentes

Turkish Journal of Electrical Engineering and Computer Sciences

Surface plasmon resonance (SPR) is a charge-density oscillation that occurs when a beam of p-polarized monochromatic light impinges with a greater angle than the critical angle in a dielectric-metal interface. Because of the high losses related to metals, the generated surface plasmon waves propagate with high attenuation in the visible and near-infrared spectral regions in most of the dielectric-metal interfaces. An alternative to reduce such losses is to use a transparent indium tin oxide (ITO) film. In this paper, we compared theoretical calculations and experimental measurements of the SPR angle $\theta_{SPR}$ on the interfaces of a borosilicate prism (Bp) and …

Data-Driven Nonlinear Control Designs For Constrained Systems, Roland Harvey

Electronic Theses and Dissertations, 2020-2023

Systems with nonlinear dynamics are theoretically constrained to the realm of nonlinear analysis and design, while explicit constraints are expressed as equalities or inequalities of state, input, and output vectors of differential equations. Few control designs exist for systems with such explicit constraints, and no generalized solution has been provided. This dissertation presents general techniques to design stabilizing controls for a specific class of nonlinear systems with constraints on input and output, and verifies that such designs are straightforward to implement in selected applications. Additionally, a closed-form technique for an open-loop problem with unsolvable dynamic equations is developed. Typical optimal …

Improving Usability Of Genetic Algorithms Through Self Adaptation On Static And Dynamic Environments, Reamonn Norat

Electronic Theses and Dissertations, 2020-2023

We propose a self-adaptive genetic algorithm, called SAGA, for the purposes of improving the usability of genetic algorithms on both static and dynamic problems. Self-adaption can improve usability by automating some of the parameter tuning for the algorithm, a difficult and time-consuming process on canonical genetic algorithms. Reducing or simplifying the need for parameter tuning will help towards making genetic algorithms a more attractive tool for those who are not experts in the field of evolutionary algorithms, allowing more people to take advantage of the problem solving capabilities of a genetic algorithm on real-world problems. We test SAGA and analyze …

Energy-Efficient Signal Conversion And In-Memory Computing Using Emerging Spin-Based Devices, Soheil Salehi Mobarakeh

Electronic Theses and Dissertations, 2020-2023

New approaches are sought to maximize the signal sensing and reconstruction performance of Internet-of-Things (IoT) devices while reducing their dynamic and leakage energy consumption. Recently, Compressive Sensing (CS) has been proposed as a technique aimed at reducing the number of samples taken per frame to decrease energy, storage, and data transmission overheads. CS can be used to sample spectrally-sparse wide-band signals close to the information rate rather than the Nyquist rate, which can alleviate the high cost of hardware performing sampling in low-duty IoT applications. In my dissertation, I am focusing mainly on the adaptive signal acquisition and conversion circuits …

Pervasive Spectrum Sharing For Improved Wireless Experience, Mostafizur Rahman

Electronic Theses and Dissertations, 2020-2023

Spectrum sharing among cellular users has been a promising approach to attain better efficiency in the use of the limited spectral bands. The existing dynamic spectrum access techniques include sharing of the licensed spectrum bands by allowing other 'secondary' users to use the bands if the licensee 'primary' user is idle. This primary-secondary spectrum sharing is limited in terms of design space, and may not be sufficient to meet the ever-increasing demand of connectivity and high signal quality to improve the end-users' wireless experience. The next step to increase spectrum efficiency is to design markets where sharing takes place pervasively …

Scalable Communication Frameworks For Multi-Agency Data Sharing, Shafaq Chaudhry

Electronic Theses and Dissertations, 2020-2023

With the rise in frequency and magnitude of natural disasters, there is a need to break down monolithic organizational barriers and engage with community volunteers. This calls for ease of systems interoperability to facilitate communication, data-sharing and scalability of real-time response, essential for crisis communications. We propose two scalable frameworks that enable multi-agency interoperability and real-time data-sharing. The first framework harnesses the power of social media, artificial intelligence, and community volunteers to form an extended rescue-and-response network that alleviates call center burden and augments the finite capacity of dispatch units. Through an "online 9-1-1" service, affected people can request help …

Multi-Element Multi-Datastream Visible Light Communication Networks, Sifat Ibne Mushfique

Electronic Theses and Dissertations, 2020-2023

Because of the exponentially increasing demand of wireless data, the Radio Frequency (RF) spectrum crunch is rising rapidly. The amount of available RF spectrum is being shrunk at a very heavy rate, and spectral management is becoming more difficult. Visible Light Communication (VLC) is a recent promising technology complementary to RF spectrum which operates at the visible light spectrum band (400 THz to 780 THz) and it has 10,000 times bigger bandwidth than radio waves (3 kHz to 300 GHz). Due to this tremendous potential, VLC has captured a lot of interest recently as there is already an extensive deployment …

Jc Drain And Sewer Website, Jarod Pichler, Nathan Houman

Capstone Showcase

1. A website for a small plumbing business in Scranton, Pennsylvania. The website includes a Home, About Us, Services, Contact Us, and Testimonials page. The home page introduces the company to the website viewer. The About Us page provides information about the company and owner, to the website viewer. The Services page provides the website viewer with all of the services that the company can provide. The Contact Us page allows the website viewer to send the company an email. Finally, the Testimonials page will allow the website viewer to leave a comment about the company’s services. The website also includes …

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Ultrafast Optical Response And Ablation Mechanisms Of Molybdenum Disulfide Under Intense Femtosecond Laser Irradiation, Changji Pan, Lan Jiang, Jingya Sun, Qingsong Wang, Feifei Wang, Ka Wang, Yongfeng Lu, Yeliang Wang, Liangti Qu, Tianhong Cui

Department of Electrical and Computer Engineering: Faculty Publications

Numerous valuable studies on electron dynamics have focussed on the extraordinary properties of molybdenum disulfide (MoS2); however, most of them were confined to the level below the damage threshold. Here the electron dynamics of MoS2 under intense ultrafast laser irradiation was investigated by experiments and simulations. Two kinds of ablation mechanisms were revealed, which led to two distinct types of electron dynamics and final ablation morphology. At a higher fluence, the emergence of superheated liquid induced a dramatic change in the transient reflectivity and micro-honeycomb structures. At a lower fluence, the material was just removed by sublimation, and the ablation …

Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Electrical and Computer Engineering Faculty Publications

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select …

Metadata Management For Clinical Data Integration, Ningzhou Zeng

Theses and Dissertations--Computer Science

Clinical data have been continuously collected and growing with the wide adoption of electronic health records (EHR). Clinical data have provided the foundation to facilitate state-of-art researches such as artificial intelligence in medicine. At the same time, it has become a challenge to integrate, access, and explore study-level patient data from large volumes of data from heterogeneous databases. Effective, fine-grained, cross-cohort data exploration, and semantically enabled approaches and systems are needed. To build semantically enabled systems, we need to leverage existing terminology systems and ontologies. Numerous ontologies have been developed recently and they play an important role in semantically enabled …

Algorithms For Achieving Fault-Tolerance And Ensuring Security In Cloud Computing Systems, Md. Tariqul Islam

Theses and Dissertations--Computer Science

Security and fault tolerance are the two major areas in cloud computing systems that need careful attention for its widespread deployment. Unlike supercomputers, cloud clusters are mostly built on low cost, unreliable, commodity hardware. Therefore, large-scale cloud systems often suffer from performance degradation, service outages, and sometimes node and application failures. On the other hand, the multi-tenant shared architecture, dynamism, heterogeneity, and openness of cloud computing make it susceptible to various security threats and vulnerabilities. In this dissertation, we analyze these problems and propose algorithms for achieving fault tolerance and ensuring security in cloud computing systems.

First, we perform a …

Greater Cybersecurity Threat Predictions With A Primer In Machine Learning, Samir Souidi, Stanley Mierzwa

Center for Cybersecurity

Why is it that Big Data services such as Netflix can predict, with reasonable accuracy, the movies and programs that I may be interested in when I connect to their service? When I access Amazon.com Online Shopping, I get a good glimpse of items I purchased in the past, and at what timeframe, and also a prediction if it is time to repurchase it? Machine Learning (ML) and Artificial Intelligence (AI) probably hold the key to the reasons these predictions are performed so well. So, why can’t we predict, at least at a small-scale level, when and what type of …

Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone

Dissertations

The CIRSY system (or Chick Instance Recognition System) is am image processing system developed as part of this research to detect images of chicks in highly-populated images that uses the leading algorithm in instance segmentation tasks, called the Mask R-CNN. It extends on the Faster R-CNN framework used in object detection tasks, and this extension adds a branch to predict the mask of an object along with the bounding box prediction. Mask R-CNN has proven to be effective ininstance segmentation and object de-tection tasks after outperforming all existing models on evaluation of the Microsoft Common Objects in Context (MS COCO) …

Machine Learning Assisted Gait Analysis For The Determination Of Handedness In Able-Bodied People, Hugh Gallagher

Dissertations

This study has investigated the potential application of machine learning for video analysis, with a view to creating a system which can determine a person’s hand laterality (handedness) from the way that they walk (their gait). To this end, the convolutional neural network model VGG16 underwent transfer learning in order to classify videos under two ‘activities’: “walking left-handed” and “walking right-handed”. This saw varying degrees of success across five transfer learning trained models: Everything – the entire dataset; FiftyFifty – the dataset with enough right-handed samples removed to produce a set with parity between activities; Female – only the female …

An Examination Of The Smote And Other Smote-Based Techniques That Use Synthetic Data To Oversample The Minority Class In The Context Of Credit-Card Fraud Classification, Eduardo Parkinson De Castro

Dissertations

This research project seeks to investigate some of the different sampling techniques that generate and use synthetic data to oversample the minority class as a means of handling the imbalanced distribution between non-fraudulent (majority class) and fraudulent (minority class) classes in a credit-card fraud dataset. The purpose of the research project is to assess the effectiveness of these techniques in the context of fraud detection which is a highly imbalanced and cost-sensitive dataset. Machine learning tasks that require learning from datasets that are highly unbalanced have difficulty learning since many of the traditional learning algorithms are not designed to cope …

Transformer Neural Networks For Automated Story Generation, Kemal Araz

Dissertations

Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given …