Computer Engineering Commons^™

Comparison Of Object Detection And Patch-Based Classification Deep Learning Models On Mid- To Late-Season Weed Detection In Uav Imagery, Arun Narenthiran Veeranampalayam Sivakumar, Jiating Li, Stephen Scott, Eric T. Psota, Amit J. Jhala, Joe D. Luck, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean …

Optimization Of Home Mortgage Mover Predictive Model Applying Geo-Spatial Analysis And Machine Learning Techniques, Natalia Riscovaia

Dissertations

In the last decade digital innovations and online banking services have significantly changed customers banking preferences and behaviour. Banking industry is going through the changes and developments in the provision of banking services that are affecting the structure and the organization of the bank network. However, private home loan, referred as Home Mortgage hereinafter, continue to remain among the products, that customers prefer to have personal interaction about with professional advisors prior making the decision to apply for the loan with financial institution.

Exploring The Relationship Between Teamwork Skills And Team Members' Centrality, Francisco Cima, Pilar Pazos, Ana Maria Canto

Engineering Management & Systems Engineering Faculty Publications

The present paper describes an exploratory study of small teams working on a four-month project as part of a graduate engineering program. The research had two primary goals. The first was to utilize the log files from shared repositories used for team collaboration to describe the network structure of the teams. The second was to determine whether the network centrality of any individual team member is associated with their teamwork skills and attitudes towards the collaboration platform. The relationship between teamwork skills, attitudes towards the collaboration technology, and the centrality index was explored using Pearson correlations. A total of 35 …

การวินิจฉัยโรคพาร์กินสันโดยใช้การเรียนรู้ของเครื่อง, หัสพล ธัมมิกรัตน์

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์นี้นำเสนอวิธีการวินิจฉัยโรคพาร์กินสันด้วยการใช้การเรียนรู้ของเครื่องสำหรับการตรวจพบโรคพาร์กินสันในระยะเริ่มต้น โดยใช้โครงข่ายประสาทเทียมแบบวนซ้ำชนิดพิเศษ Long Short-Term Memory กับข้อมูลโรคพาร์กินสันที่ได้รับจากผู้เชี่ยวชาญของโรงพยาบาลจุฬาลงกรณ์ โดยข้อมูลที่ใช้ประกอบไปด้วยข้อมูลจากเซ็นเซอร์และคีย์บอร์ดจากการเก็บข้อมูลจากผู้ร่วมทดสอบซึ่งมีทั้งกลุ่มควบคุมและผู้ป่วยจำนวนหนึ่งผ่านตัวควบคุมที่เก็บข้อมูลคีย์บอร์ดและเซ็นเซอร์ ซึ่งข้อมูลเซ็นเซอร์มีค่าตัวแปรความเร่งและมุม ข้อมูลคีย์บอร์ดคือการกดคีย์บอร์ดเป็นตัวอักษรพร้อมทั้งเวลาการกดคีย์บอร์ด การวิจัยนี้ทำเพื่อช่วยการวินิจฉัยแยกแยะระหว่างอาการสั่นหรือมีปัญหาทางการควบคุมการเครื่องไหวของผู้ป่วยโรคอื่นและผู้ป่วยโรคพาร์กินสัน การวิจัยนี้ได้ใช้การเรียนรู้ของเครื่องเพื่อคัดกรองผู้ป่วยเบื้องต้นแทนการใช้แพทย์ผู้เชี่ยวชาญทางโรคพาร์กินสันสำหรับแพทย์แผนกผู้ป่วยนอกในวินิจฉัยการคัดกรองผู้ป่วยที่มีอาการใกล้เคียงอย่างการเคลื่อนไหว และความผิดปกติของระบบประสาทและสมอง ผลการวินิจฉัยพบว่าการเรียนรู้เครื่องสามารถตรวจพบการวินิจฉัยโรคพาร์กินสัน ได้ร้อยละความถูกต้องที่ 88.78 เปอร์เซ็นต์

Reducing Smart Contract Runtime Errors On The Ethereum Blockchain, Siwapol Jumnongsaksub

Chulalongkorn University Theses and Dissertations (Chula ETD)

With smart contracts, a wide range of applications can be implemented on blockchains. Ethereum stores smart contract byte code with the smart contract ad-dress so, the Ethereum Virtual Machine (EVM) can read and execute transactions correctly. All executed transactions (both successful and failed transactions) are stored on the platform permanently. Failed transactions are thrown by the EVM due to runtime errors and result in monetary waste. The waste from these transactions add up to around 2 million Ethers or $634.2 million. In this thesis, we propose Evitar, a warning algorithm for reducing Ethereum smart contract runtime errors, which has two …

Red Blood Cell Segmentation And Classification From Microscopic Images Using Machine Learning, Korranat Naruenatthanaset

Chulalongkorn University Theses and Dissertations (Chula ETD)

Red blood cell morphology analysis plays an essential role in diagnosing many diseases caused by RBC disorders. This manual inspection is a long process and requires practice and experience. Since recent computer vision and image processing in the medical imaging area can provide efficient tools, it can help hematologists to automatically analyze images from a microscope in a reduced time and cost. This research presents a new method to segment and classify RBCs from blood smear images. The process started from data collection, which a new application was created for precisely labeling. The normalization was done to reduce the color …

Digital Platform Development For Performance Monitoring System In Oil And Gas Exploration And Production, Tanthai Poopaiboon

Chulalongkorn University Theses and Dissertations (Chula ETD)

The paper provides a case study to enhance the Performance Management System for Oil and Gas Exploration and Production industry. Although the system was designed for the Oil and Gas Exploration and Production industry, the paper could be applied effectively for other industries because the modern organisation mainly utilised the Key Performance Indicator (KPI) to reflect its performance. So, the paper could be applied to most organisations with minor modifications. The Advanced Performance Management System was developed systematically powered by digital transformation according to research methodology framework, including research, analysis, project development, and result measurement. The research stage is studying …

Adaptive Effort Classifiers: A System Design For Partitioned Edge/Cloud Inference, Divya Sankar

Dissertations and Theses

The massive growth in availability of real-world data from connected devices and the overwhelming success of Deep Neural Networks (DNNs) in many ArtificialIntelligence (AI) tasks have enabled AI-based applications and services to become commonplace across the spectrum of computing devices from edge/Internet-of-Things (IoT) devices to data centers and the cloud. However, DNNs incur high computational cost (compute operations, memory footprint and bandwidth),which far outstrip the capabilities of modern computing platforms. Therefore improving the computational efficiency of DNNs wide-spread commercial deployment and success.In this thesis, we address the computational efficiency challenge in the context ofAI inference applications executing on edge/cloud systems, …

V-Slam And Sensor Fusion For Ground Robots, Ejup Hoxha

Dissertations and Theses

In underground, underwater and indoor environments, a robot has to rely solely on its on-board sensors to sense and understand its surroundings. This is the main reason why SLAM gained the popularity it has today. In recent years, we have seen excellent improvement on accuracy of localization using cameras and combinations of different sensors, especially camera-IMU (VIO) fusion. Incorporating more sensors leads to improvement of accuracy,but also robustness of SLAM. However, while testing SLAM in our ground robots, we have seen a decrease in performance quality when using the same algorithms on flying vehicles.We have an additional sensor for ground …

Optimizing Router Performance, Bradley Newton, Radon Rosborough, Miles President, Hakan Alpan

CMC Senior Theses

To support its development of networking hardware and software, Juniper Networks conducts research into enhancements to the protocols used on the Internet, in coordination with standards bodies such as the Internet Engineering Task Force. We helped Juniper Networks with two specific research objectives. The first was to design and implement an improved algorithm by which Internet hosts can establish the appropriate packet size to maximize bandwidth while avoiding packet fragmentation. We produced a working implementation of the improved algorithm in the Linux kernel. The second objective was to measure the effect of different Internet Protocol extension headers (specifically, Routing Header …

Control Strategies For Multi-Controller Multi-Objective Systems, Raaed Al-Azzawi

Electronic Theses and Dissertations, 2020-2023

This dissertation's focus is control systems controlled by multiple controllers, each having its own objective function. The control of such systems is important in many practical applications such as economic systems, the smart grid, military systems, robotic systems, and others. To reap the benefits of feedback, we consider and discuss the advantages of implementing both the Nash and the Leader-Follower Stackelberg controls in a closed-loop form. However, closed-loop controls require continuous measurements of the system's state vector, which may be expensive or even impossible in many cases. As an alternative, we consider a sampled closed-loop implementation. Such an implementation requires …

Target Acquisition Performance Improvement With Boost And Restoration Filtering Using Deep-Electron-Well Infrared Detectors, Robert Short

Electronic Theses and Dissertations, 2020-2023

Recent advances in infrared focal plane fabrication have allowed for the production of sensors with small detector size (small pitch) and long integration time (deep electron wells) in large-format arrays. Individually, these are all welcome developments, but we raise the question of whether it is possible to utilize all of these technologies in concert to optimize performance. If so, a key part of such a system will be digital boost filtering, to recover the performance loss due to diffraction blur. We describe a system design concept called PWP (Pitch-Well-Processing) that uses each of these features along with Wiener filtering to …

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 …

A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan

University of the Pacific Theses and Dissertations

The execution of the scientific applications on the Cloud comes with great flexibility, scalability, cost-effectiveness, and substantial computing power. Market-leading Cloud service providers such as Amazon Web service (AWS), Azure, Google Cloud Platform (GCP) offer various general purposes, memory-intensive, and compute-intensive Cloud instances for the execution of scientific applications. The scientific community, especially small research institutions and undergraduate universities, face many hurdles while conducting high-performance computing research in the absence of large dedicated clusters. The Cloud provides a lucrative alternative to dedicated clusters, however a wide range of Cloud computing choices makes the instance selection for the end-users. This thesis …

Automated And Standardized Tools For Realistic, Generic Musculoskeletal Model Development, Trevor Rees Moon

Graduate Theses, Dissertations, and Problem Reports (ETD)

Human movement is an instinctive yet challenging task that involves complex interactions between the neuromusculoskeletal system and its interaction with the surrounding environment. One key obstacle in the understanding of human locomotion is the availability and validity of experimental data or computational models. Corresponding measurements describing the relationships of the nervous and musculoskeletal systems and their dynamics are highly variable. Likewise, computational models and musculoskeletal models in particular are vitally dependent on these measurements to define model behavior and mechanics. These measurements are often sparse and disparate due to unsystematic data collection containing variable methodologies and reporting conventions. To date, …

Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin

Browse all Theses and Dissertations

Features and attributes that describe an event (disasters, social movements, etc.) are heterogeneous in nature. For virtually all events that impact humans, technology enables us to capture a large amount and variety of data from many sources, including humans (i.e., social media) and sensors/internet of things (IoTs). The corresponding modalities of data include text, imagery, voice and video, along with structured data such as gazetteers (i.e., location-based data) and government and statistical data. However, even though there is often an abundance of information produced, this information is fragmented across the various modalities and sources. The DisasterRecord system aims to provide …

Risk Assessment Of Architecture Technical Debt, Mrwan Omar Kh. Ben Idris

Graduate Theses, Dissertations, and Problem Reports (ETD)

Technical Debt (TD) is a metaphor that refers to short-term solutions in software development that may affect the software development life cycle cost. Researchers have found many TD types. These TD types include but are not limited to code debt (CD), design debt (DD), and architecture technical debt (ATD). Several methods have been used to detect technical debt, such as bad smells, software metrics, and code comments. Although TD has received many researchers’ attention, ATD has received less attention compared with CD and DD. We found a lack of tools to deal with ATD in contrast to CD and DD. …

Palmprint Gender Classification Using Deep Learning Methods, Minou Khayami

Graduate Theses, Dissertations, and Problem Reports (ETD)

Gender identification is an important technique that can improve the performance of authentication systems by reducing searching space and speeding up the matching process. Several biometric traits have been used to ascertain human gender. Among them, the human palmprint possesses several discriminating features such as principal-lines, wrinkles, ridges, and minutiae features and that offer cues for gender identification. The goal of this work is to develop novel deep-learning techniques to determine gender from palmprint images. PolyU and CASIA palmprint databases with 90,000 and 5502 images respectively were used for training and testing purposes in this research. After ROI extraction and …

Route Planning For Long-Term Robotics Missions, Christopher Alexander Arend Tatsch

Graduate Theses, Dissertations, and Problem Reports (ETD)

Many future robotic applications such as the operation in large uncertain environment depend on a more autonomous robot. The robotics long term autonomy presents challenges on how to plan and schedule goal locations across multiple days of mission duration. This is an NP-hard problem that is infeasible to solve for an optimal solution due to the large number of vertices to visit. In some cases the robot hardware constraints also adds the requirement to return to a charging station multiple times in a long term mission. The uncertainties in the robot model and environment require the robot planner to account …

Deep Learning Based Face Detection And Recognition In Mwir And Visible Bands, Suha Reddy Mokalla

Graduate Theses, Dissertations, and Problem Reports (ETD)

In non-favorable conditions for visible imaging like extreme illumination or nighttime, there is a need to collect images in other spectra, specifically infrared. Mid-Wave infrared (3-5 microm) images can be collected without giving away the location of the sensor in varying illumination conditions. There are many algorithms for face detection, face alignment, face recognition etc. proposed in visible band till date, while the research using MWIR images is highly limited. Face detection is an important pre-processing step for face recognition, which in turn is an important biometric modality. This thesis works towards bridging the gap between MWIR and visible spectrum …

Development Of Software-Only Simulation Test Beds (Sost) For Spacecraft And Smallsats, Scott Alan Zemerick

Graduate Theses, Dissertations, and Problem Reports (ETD)

Software-only-Simulation Test Beds (SoST) are beginning to become more popular among aircraft, spacecraft, and smallsat embedded system developers due to the high cost of duplicating hardware test beds.

SoSTs provide a software-only, or virtual test bed, that creates a “digital twin” that contains software models of the ETUs and often includes modeled components such as flight computers, busses (e.g., MIL-STD-1553, SPI, I2C), compact PCI (cPCI) backplane cards, sensors, and actuators. The ultimate goal of a SoST is for it to run the native system software compiled-binary on its native CPU architecture (e.g., PowerPC, LEON3/4, ARM) on a standard X86 personal …

Instructor Activity Recognition Using Smartwatch And Smartphone Sensors, Zayed Uddin Chowdhury

College of Graduate Studies: Theses & Dissertations

During a classroom session, an instructor performs several activities, such as writing on the board, speaking to the students, gestures to explain a concept. A record of the time spent in each of these activities could be valuable information for the instructors to virtually observe their own style of instruction. It can help in identifying activities that engage the students more, thereby enhancing teaching effectiveness and efficiency. In this work, we present a preliminary study on profiling multiple activities of an instructor in the classroom using smartwatch and smartphone sensor data. We use 2 benchmark datasets to test out the …

Remote Communication In Wilderness Search And Rescue: Implications For The Design Of Emergency Distributed-Collaboration Tools For Network-Sparse Environments, Brennan Jones, Anthony Tang, Carman Neustaedter

Research Collection School Of Computing and Information Systems

Wilderness search and rescue (WSAR) requires careful communication between workers in different locations. To understand the contexts from which WSAR workers communicate and the challenges they face, we interviewed WSAR workers and observed a mock-WSAR scenario. Our findings illustrate that WSAR workers face challenges in maintaining a shared mental model. This is primarily done through distributed communication using two-way radios and cell phones for text and photo messaging; yet both implicit and explicit communication suffer. WSAR workers send messages for various reasons and share different types of information with varying levels of urgency. This warrants the use of multiple communication …

Simulation And Analysis Of Wind Turbine Radar Echo Based On 3-D Scattering Point Model, Jiangong Zhang, Bin Hao, Bo Tang, Li Huang, Jiawei Yang

Turkish Journal of Electrical Engineering and Computer Sciences

Wind turbine (WT) arrays in wind farms can cause serious interference on nearby radar stations. This interference could be filtered out if wind turbine radar echo (WTRE) can be obtained accurately. Considering the singleness of in-field experiments, numerical simulation became the majority among such works, but few of them reached necessary accuracy. Therefore, we propose a solution method of WTRE based on three-dimensional (3-D) scattering point model. Firstly, we use the nonuniform rational B-spline to build the 3-D model of WT. Secondly, based on the method of moments (MoM), the Rao-Wilton-Gisson (RWG) basis function is adopted to discretize the integral …

A Hybrid Model Based On The Convolutional Neural Network Model And Artificial Bee Colony Or Particle Swarm Optimization-Based Iterative Thresholding For The Detection Of Bruised Apples, Mahmut Heki̇m, Onur Cömert, Kemal Adem

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stem-calyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken …

Applying Deep Learning Models To Structural Mri For Stage Prediction Of Alzheimer's Disease, Altuğ Yi̇ği̇t, Zerri̇n Işik

Turkish Journal of Electrical Engineering and Computer Sciences

Alzheimer's disease is a brain disease that causes impaired cognitive abilities in memory, concentration, planning, and speaking. Alzheimer's disease is defined as the most common cause of dementia and changes different parts of the brain. Neuroimaging, cerebrospinal fluid, and some protein abnormalities are commonly used as clinical diagnostic biomarkers. In this study, neuroimaging biomarkers were applied for the diagnosis of Alzheimer's disease and dementia as a noninvasive method. Structural magnetic resonance (MR) brain images were used as input of the predictive model. T1 weighted volumetric MR images were reduced to two-dimensional space by several preprocessing methods for three different projections. …

Time Series Forecasting On Multivariate Solar Radiation Data Using Deep Learning (Lstm), Murat Ci̇han Sorkun, Özlem Durmaz İncel, Christophe Paoli

Turkish Journal of Electrical Engineering and Computer Sciences

Energy management is an emerging problem nowadays and utilization of renewable energy sources is an efficient solution. Solar radiation is an important source for electricity generation. For effective utilization, it is important to know precisely the amount from different sources and at different horizons: minutes, hours, and days. Depending on the horizon, two main classes of methods can be used to forecast the solar radiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium- to long-term horizons. Although statistical time series forecasting methods are utilized in the literature, there are a limited …

Nonlocal Means Estimation Of Intrinsic Mode Functions For Speech Enhancement, Sagar Reddy Vumanthala, Bikshalu K

Turkish Journal of Electrical Engineering and Computer Sciences

The main aim of this paper is to introduce a new approach to enhance speech signals by exploring the advantages of nonlocal means (NLM) estimation and empirical mode decomposition. NLM, a patch-based denoising method, is extensively used for two-dimensional signals like images. However, its use for one-dimensional signals has been attracting more attention recently. The NLM-based approach is quite useful for removing low-frequency noises based on nonlocal similarities present among samples of the signal. However, there is an issue of under averaging in the high-frequency regions. The temporal and spectral characteristics of the speech signal are changing markedly over time. …

A Symmetric-Based Framework For Securing Cloud Data At Rest, Mohammed Mohammed, Fadhil Abed

Turkish Journal of Electrical Engineering and Computer Sciences

Cloud computing is the umbrella term for delivering services via the Internet. It enables enterprises and individuals to access services such as virtual machines, storage, or applications on demand. It allows them to achieve more by paying less, and it removes the barrier of installing physical infrastructure. However, due to its openness and availability over the Internet, the issue of ensuring security and privacy arises. This requires careful consideration from enterprises and individuals before the adoption of cloud computing. In order to overcome security issues, cloud service providers are required to use strong security measures to secure their storage and …

A Compact Wideband Series Linear Dielectric Resonator Array Antenna, Yazeed Qasayneh, Abdullulah Almuhaisen, Talha Yazdani

Turkish Journal of Electrical Engineering and Computer Sciences

This communication presents a miniaturised series linear wideband array of notched rectangular dielectric resonator antennas that operate in the band IEEE 802.11a. Three dielectric resonators (DRs) were excited through the aperture slots coupled with a microstrip feed. To improve the array gain, the aperture slots were placed based on the attributes related to the standing-wave ratio on a short-ended microstrip feeder to obtain optimal joint power for the DRs, while the bandwidth was improved using the notched rectangular DRs. An equivalent impedance model of the proposed array was postulated to provide physical insight into the array resonance behaviour. The impedance …