Computer Engineering Commons^™

Integrating Ai Into Uavs, Huong Quach

Cybersecurity Undergraduate Research Showcase

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …

An Algorithm For Finding Data Dependencies In An Event Graph, Erik J. Jensen

Modeling, Simulation and Visualization Student Capstone Conference

This work presents an algorithm for finding data dependencies in a discrete-event simulation system, from the event graph of the system. The algorithm can be used within a parallel discrete-event simulation. Also presented is an experimental system and event graph, which is used for testing the algorithm. Results indicate that the algorithm can provide information about which vertices in the experimental event graph can affect other vertices, and the minimum amount of time in which this interference can occur.

U-Net Based Multiclass Semantic Segmentation For Natural Disaster Based Satellite Imagery, Nishat Ara Nipa

Modeling, Simulation and Visualization Student Capstone Conference

Satellite image analysis of natural disasters is critical for effective emergency response, relief planning, and disaster prevention. Semantic segmentation is believed to be on of the best techniques to capture pixelwise information in computer vision. In this work we will be using a U-Net architecture to do a three class semantic segmentation for the Xview2 dataset to capture the level of damage caused by different natural disaster which is beyond the visual scope of human eyes.

A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong

Computer Science Faculty Publications

Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. However, most previous methods treat word embeddings as isolated entities and fail to jointly consider both the intra-space and inter-space topological relations between words. This limitation makes it challenging to align words from embedding spaces with distinct topological structures, especially when the assumption of isomorphism may not hold. To this end, we propose a novel approach called the Structure-Aware Generative Adversarial Network (SA-GAN) model to explicitly capture multiple topological structure information to achieve accurate …

Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu

Electrical & Computer Engineering Faculty Publications

Energy detection (ED) represents a low complexity approach used by secondary users (SU) to sense spectrum occupancy by primary users (PU) in cognitive radio (CR) systems. In this paper, we present a new algorithm that senses the spectrum occupancy by performing ED in K consecutive sensing time slots starting from the current slot and continuing by alternating before and after the current slot. We consider a PU traffic model specified in terms of an average duty cycle value, and derive analytical expressions for the false alarm probability (FAP) and correct detection probability (CDP) for any value of K . Our …

Wg2An: Synthetic Wound Image Generation Using Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Ozgur Guler

Engineering Technology Faculty Publications

In part due to its ability to mimic any data distribution, Generative Adversarial Network (GAN) algorithms have been successfully applied to many applications, such as data augmentation, text-to-image translation, image-to-image translation, and image inpainting. Learning from data without crafting loss functions for each application provides broader applicability of the GAN algorithm. Medical image synthesis is also another field that the GAN algorithm has great potential to assist clinician training. This paper proposes a synthetic wound image generation model based on GAN architecture to increase the quality of clinical training. The proposed model is trained on chronic wound datasets with various …

Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park

VMASC Publications

The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, …

Cyber Security- A New Secured Password Generation Algorithm With Graphical Authentication And Alphanumeric Passwords Along With Encryption, Akash Rao

Electrical & Computer Engineering Theses & Dissertations

Graphical passwords are always considered as an alternative of alphanumeric passwords for their better memorability and usability [1]. Alphanumeric passwords provide an adequate amount of satisfaction, but they do not offer better memorability compared to graphical passwords [1].

On the other hand, graphical passwords are considered less secured and provide better memorability [1]. Therefore many researchers have researched on graphical passwords to overcome the vulnerability. One of the most significant weaknesses of the graphical passwords is "Shoulder Surfing Attack," which means, sneaking into a victim's computer to learn the whole password or part of password or some confidential information. Such …

Applying Machine Learning To Advance Cyber Security: Network Based Intrusion Detection Systems, Hassan Hadi Latheeth Al-Maksousy

Computer Science Theses & Dissertations

Many new devices, such as phones and tablets as well as traditional computer systems, rely on wireless connections to the Internet and are susceptible to attacks. Two important types of attacks are the use of malware and exploiting Internet protocol vulnerabilities in devices and network systems. These attacks form a threat on many levels and therefore any approach to dealing with these nefarious attacks will take several methods to counter. In this research, we utilize machine learning to detect and classify malware, visualize, detect and classify worms, as well as detect deauthentication attacks, a form of Denial of Service (DoS). …

Scalable And Fully Distributed Localization In Large-Scale Sensor Networks, Miao Jin, Su Xia, Hongyi Wu, Xianfeng David Gu

Electrical & Computer Engineering Faculty Publications

This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and a non-uniform nodal distribution. In contrast to current state-of-the-art connectivity-based localization methods, the proposed algorithm is highly scalable with linear computation and communication costs with respect to the size of the network; and fully distributed where each node only needs the information of its neighbors without cumbersome partitioning and merging process. The algorithm is theoretically guaranteed and numerically stable. Moreover, the algorithm can be readily extended to the localization of networks with a one-hop transmission range distance measurement, and the propagation of …

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 …

A Probabilistic Analysis Of Misparking In Reservation Based Parking Garages, Vikas G. Ashok

Computer Science Theses & Dissertations

Parking in major cities is an expensive and annoying affair, the reason ascribed to the limited availability of parking space. Modern parking garages provide parking reservation facility, thereby ensuring availability to prospective customers. Misparking in such reservation based parking garages creates confusion and aggravates driver frustration. The general conception about misparking is that it tends to completely cripple the normal functioning of the system leading to chaos and confusion. A single mispark tends to have a ripple effect and therefore spawns a chain of misparks. The chain terminates when the last mispark occurs at the parking slot reserved by the …

Efficient Corona Training Protocols For Sensor Networks, Alan A. Bertossi, Stephan Olariu, Cristina M. Pinotti

Computer Science Faculty Publications

Phenomenal advances in nano-technology and packaging have made it possible to develop miniaturized low-power devices that integrate sensing, special-purpose computing, and wireless communications capabilities. It is expected that these small devices, referred to as sensors, will be mass-produced and deployed, making their production cost negligible. Due to their small form factor and modest non-renewable energy budget, individual sensors are not expected to be GPS-enabled. Moreover, in most applications, exact geographic location is not necessary, and all that the individual sensors need is a coarse-grain location awareness. The task of acquiring such a coarse-grain location awareness is referred to as training. …

Channel Management In Heterogeneous Cellular Networks, Mohammad Hadi Arbabi

Computer Science Theses & Dissertations

Motivated by the need to increase system capacity in the face of tight FCC regulations, modem cellular systems are under constant pressure to increase the sharing of the frequency spectrum among the users of the network.

Key to increasing system capacity is an efficient channel management strategy that provides higher capacity for the system while, at the same time, providing the users with Quality of Service guarantees. Not surprisingly, dynamic channel management has become a high profile topic in wireless communications. Consider a highly populated urban area, where mobile traffic loads are increased due to highway backups or sporting events. …

A Tabu Search Algorithm To Minimize The Makespan For The Unrelated Parallel Machines Scheduling Problem With Setup Times, Magdy Helal, Ghaith Rabadi, Ameer Al-Salem

Engineering Management & Systems Engineering Faculty Publications

In this paper we propose a tabu search implementation to solve the unrelated parallel machines scheduling problem with sequence- and machine- dependent setup times to minimize the schedules makespan. The problem is NP-hard and finding an optimal solution efficiently is unlikely. Therefore, heuristic techniques are more appropriate to find near-optimal solutions. The proposed tabu search algorithm uses two phases of perturbation schemes: the intra-machine perturbation, which optimizes the sequence of jobs on the machines, and the inter-machine perturbation, which balances the assignment of the jobs to the machines. We compare the proposed algorithm to an existing one that addressed the …

Robust And Efficient Localization Techniques For Cellular And Wireless Sensor Networks, Haseebulla M. Khan

Computer Science Theses & Dissertations

Localization in wireless networks refers to a collection of tasks that, collectively, determines the location of a mobile user, striving to hide the effects of mobility from the user and/or application. Localization has become an important issue and has drawn considerable attention, as many applications including E-911, cargo tracking, locating patients, location-sensitive billing, etc., require knowledge of the location of user/objects. It was realized, quite a while back, that extending emergency 911-like services (E-911) to continually growing mobile population is one of the extremely important localization applications. The bulk of the proposed solutions to emergency location management in wireless environments …

Introduction: Data Communication And Topology Algorithms For Sensor Networks, Stephan Olariu, David Simplot-Ryl, Ivan Stojmenovic

Computer Science Faculty Publications

(First paragraph) We are very proud and honored to have been entrusted to be Guest Editors for this special issue. Papers were sought to comprehensively cover the algorithmic issues in the “hot” area of sensor networking. The concentration was on network layer problems, which can be divided into two groups: data communication problems and topology control problems. We wish to briefly introduce the five papers appearing in this special issue. They cover specific problems such as time division for reduced collision, fault tolerant clustering, self-stabilizing graph optimization algorithms, key pre-distribution for secure communication, and distributed storage based on spanning trees …

Two Approaches To Critical Path Scheduling For A Heterogeneous Environment, Guangxia Liu

Computer Science Theses & Dissertations

Advances in computing and networking technologies are making large scale distributed heterogeneous computing a reality. Multi-Disciplinary Optimization (MDO) is a class of applications that is being addressed under this paradigm. It consists of multiple heterogeneous modules interacting with each other to solve an overall design problem. An efficient implementation of such an application requires scheduling heterogeneous modules (with different computing and disk 1/0 requirements) on a heterogeneous set of resources (with different CPU, memory, disk IO specifications).

Given a set of tasks and a set of resources, an optimal schedule of the tasks on the resources is very hard to …

Architectural Optimization Of Digital Libraries, Aileen O. Biser

Computer Science Theses & Dissertations

This work investigates performance and scaling issues relevant to large scale distributed digital libraries. Presently, performance and scaling studies focus on specific implementations of production or prototype digital libraries. Although useful information is gained to aid these designers and other researchers with insights to performance and scaling issues, the broader issues relevant to very large scale distributed libraries are not addressed. Specifically, no current studies look at the extreme or worst case possibilities in digital library implementations. A survey of digital library research issues is presented. Scaling and performance issues are mentioned frequently in the digital library literature but are …

Single Row Routing: Theoretical And Experimental Performance Evaluation, And New Heuristic Development, David A. Hysom

Computer Science Theses & Dissertations

The Single Row Routing Problem (SRRP) is an abstraction arising from real-world multilayer routing concerns. While NP-Complete, development of efficient SRRP routing heuristics are of vital concern to VLSI design. Previously, researchers have introduced various heuristics for SRRP; however, a comprehensive examination of SRRP behavior has been lacking.

We are particularly concerned with the street-congestion minimization constraint, which is agreed to be the constraint of greatest interest to industry. Several theorems stating lower bounds on street congestion are known. We show that these bounds are not tight in general, and argue they may be in error by at least 50% …

Pipelining Data Compression Algorithms, R. L. Bailey, R. Mukkamala

Computer Science Faculty Publications

Many different data compression techniques currently exist. Each has its own advantages and disadvantages. Combining (pipelining) multiple data compression techniques could achieve better compression rates than is possible with either technique individually. This paper proposes a pipelining technique and investigates the characteristics of two example pipelining algorithms. Their performance is compared with other well-known compression techniques.