Systems and Communications Commons^™

Optimizing Malaria Control: Granular And Cost-Effective Mosquito Habitat Index In Endemic Areas Through Satellite Imagery, Nur Ainun Daulay, Salwa Rizqina Putri, Arie Wahyu Wijayanto, Ika Yuni Wulansari

Knowledge Engineering and Data Science

Malaria, classified as a tropical disease under the Sustainable Development Goals (SDGs) indicator 3.3, remains a significant global health challenge. In this study, by taking advantage of multiple spectral composite indexes of multisource satellite imagery to capture various geospatial features relevant to the suitability of marsh mosquito habitat, we introduced the Mosquito Habitat Suitability Index (MHSI) to assess potential Anopheles mosquito breeding sites in terms of the vegetation density, water bodies, environment temperature, and humidity in any particular areas. The MHSI integrates the publicly accessible granular level of the normalized difference vegetation index, water index, land surface temperature, and moisture …

Docker Optimization Of An Automotive Sector Virtual Server Infrastructure, Leonel Hernandez, Carlos Eduardo Uc Rios

Knowledge Engineering and Data Science

Server virtualization is a powerful strategy for optimizing network infrastructure. It allows multiple virtual servers to run on a single physical server, maximizing resource utilization and improving efficiency. Deploying server virtualization using Docker technology offers a lightweight and flexible approach to optimizing network infrastructure. Docker contains package applications and their dependencies, enabling consistent and efficient deployment across various environments. Specifically, optimizing virtual server infrastructure using Docker Technology in the automotive sector focuses on improving the efficiency and management of the company's virtual server resources. By implementing Docker technology, a container platform that allows the packaging and running of applications in …

Timbre Style Transfer For Musical Instruments Acoustic Guitar And Piano Using The Generator-Discriminator Model, Widean Nagari, Joan Santoso, Esther Irawati Setiawan

Knowledge Engineering and Data Science

Music style transfer is a technique for creating new music by combining the input song's content and the target song's style to have a sound that humans can enjoy. This research is related to timbre style transfer, a branch of music style transfer that focuses on using the generator-discriminator model. This exciting method has been used in various studies in the music style transfer domain to train a machine learning model to change the sound of instruments in a song with the sound of instruments from other songs. This work focuses on finding the best layer configuration in the generator- …

Optimization Of Human Interactions In The College Campus Model Via Simio Integration, Benjamin E. Chaback

Doctoral Dissertations and Master's Theses

College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, …

Uav-X Communication: Empirical Characterization And Performance Optimization, Muhammad Hashir Syed

Electrical Engineering Theses and Dissertations

In emerging wireless networks, the scalability of deploying drones presents an opportunity to design extensive aerial networks. These networks could effectively monitor large agricultural fields from the air and soil for food production with efficient resource utilization. On the one hand, unmanned aerial vehicles (UAVs) have gained interest in agricultural aerial inspection due to their ubiquity and observation scale. On the other hand, agricultural internet-of-thing devices, including buried soil sensors, have gained interest in improving natural resource efficiency in crop production. In this work, we investigate the natural interaction of these two phenomena, where UAVs can be leveraged as flying …

Optimising The Fashion E-Commerce Journey: A Data-Driven Approach To Customer Retention, Hasna Luthfiana Fadhila, Vynska Amalia Permadi, Sylvert Prian Tahalea

Knowledge Engineering and Data Science

A fashion e-commerce company offers a wide range of products from domestic and international brands that are popular with young people. However, there has been an increase in non-organically acquired customers, many of whom do not return to make repeat purchases. This has led to a higher customer churn rate, with a significant proportion of non-organically sourced customers failing to become repeat purchasers. Consequently, a churn analysis and prediction model were developed to address this issue. This paper employs the Recency, Frequency, and Monetary (RFM) framework for churn analysis and prediction. The framework is underpinned by three key dimensions: last …

Random Forest Algorithm To Measure The Air Pollution Standard Index, Ariyono Setiawan, Untung Lestari Wibowo, Ahmad Mubarok, Khoirunnisa Larasati

Knowledge Engineering and Data Science

This study uses the Random Forest algorithm to measure and predict the Air Pollution Standard Index (APSI) at Blimbing Banyuwangi Airport. Air pollution data, including concentrations of O3, CO, NO2, SO2, PM2.5, and PM10, were collected from air monitoring stations at the airport from April 15-30, 2024. APSI measurement followed established formulas by relevant authorities. Data analysis utilized statistical approaches and computational algorithms. The findings reveal that air quality at the airport is generally "Moderate," with occasional "Good" days. The Random Forest algorithm effectively predicts APSI based on existing pollution data. These results provide insights for improving air pollution management …

A Novel Approach To Defect Detection In Arabica Coffee Beans Using Deep Learning: Investigating Data Augmentation And Model Optimization, Yusriel Ardian, Novta Danyel Irawan, Sutoko Sutoko, I Nyoman Gede Arya Astawa

Knowledge Engineering and Data Science

Arabica coffee beans have valuable market worth because of their taste and quality, and there are defects like wholly and partially black beans that can lower the standards of a product, especially in the premium coffee sector. However, the manual processes used to detect the defects take an inordinate amount of time and are inefficient. This study aims to bridge the knowledge gap on the automated detection and recognition of the defects present in the Arabica coffee beans by creating and optimizing a CNN model based on a modified VGG16 architecture. The model applies data augmentation, rotation, cropping, and Bayesian …

Plc Course Development, Leonard Hernandez, Jacqueline Grace Radding

Electrical Engineering

The goal of this project is to design and construct the first lab experiment for the newly developed EE435 laboratory course. The experiment entails PLC programming to automate LED lighting in a simulated building. Starting with why PLCs are used, how to use the interface of EcoStruxure Control Expert V15.0 and practice using logic to control the dimming of a Light Emitting Diode (LED) to sustain a certain number of lumens consistent in a zone. Through the lab manual developed in this project, students will gain experience of creating logic designs using Function Block Diagram (FBD) and learn the layout …

Fair Fault-Tolerant Approach For Access Point Failures In Networked Control System Greenhouses, Mohammed Ali Yaslam Ba Humaish

Theses and Dissertations

Greenhouse Networked Control Systems (NCS) are popular applications in modern agriculture due to their ability to monitor and control various environmental factors that can affect crop growth and quality. However, designing and operating a greenhouse in the context of NCS could be challenging due to the need for highly available and cost-efficient systems. This thesis presents a design methodology for greenhouse NCS that addresses these challenges, offering a framework to optimize crop productivity, minimize costs, and improve system availability and reliability. It contributes several innovations to the field of greenhouse NCS design. For example, it recommends using the 2.4GHz frequency …

Tree Localization In A Plantation Using Ultra Wideband Signals, Akshat Verma

The Journal of Purdue Undergraduate Research

No abstract provided.

Optimizing Energy-Efficient Grid Performance: Integrating Electric Vehicles, Dstatcom, And Renewable Sources Using The Hippopotamus Optimization Algorithm, Mohamed Abdelaziz, A.A. Ali, R.A. Swief, Rasha Elazab

Renewable Electrical Energy Engineering

The rapid increase in renewable energy integration and electric vehicle (EV) adoption creates significant challenges for the stability and efficiency of power distribution networks. This study addresses the need for optimized placement and sizing of Electric Vehicle Charging Stations (EVCSs), photovoltaic (PV) systems, and Distribution Static Compensators (DSTATCOMs) to enhance grid performance. The motivation for this work arises from the fluctuating nature of renewable energy generation and the unpredictable demands of EV charging, which strain existing infrastructure. To address these challenges, we propose a novel optimization framework that introduces the Renewable Distributed Generation Hosting Factor (RDG-HF) and Electric Vehicle Hosting …

Novel Phase Shifters Using Reconfigurable Filters, Georgiy Brussenskiy

Graduate Thesis and Dissertation 2023-2024

Phase shifters play a crucial role in radar, satellite communications, and 5G networks. Recently, the idea of using filters as phase shifters have attracted much interest due to providing many benefits such as smaller area, lower noise figure, easier fabrication method as compared with other technologies, reduced cost, and the ability to work as multi-functioning device. This work focuses on the implementation of bandstop-based and bandpass-based filtering phase shifters. For the bandstop-based approach, some of the resonating structures that were examined are stubs, LC tanks, L-shaped/U-shaped half wavelength resonators and many others. Periodic stub loading filter design method was compared …

All-Optical Signal Processing With Fiber-Based Parametric Wavelength Converters, Cheng Guo

Electrical Engineering Dissertations - Archive

The optical signal degradation by optical amplifier noise set the fundamental limit of link reach in the fiber-optics networks. The industrial solution is to use the optical-electrical-optical (OEO) regenerator to clean up the noise at the expense of high-speed electronics and extra cost of laser and photodetectors. All-optical signal processing, enabled by nonlinear optics and optical fiber, intrinsically provides 2-order of magnitude higher processing bandwidth and seamless interface to fiber communication channels. However, there is no robust phase-preserving regenerator that has been experimentally demonstrated without sophisticated polarization tuning and without instable interferometric structure. In this project, we explore the applications …

Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can …

Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel …

Power And Waiting Time Efficiency At Automatic Toll Gates With The Contactless Card Payment Implementation, Ujang Wiharja, Sri Hartanto

ASEAN Journal on Science and Technology for Development

One method of Electronic Toll Collection (ETC) in Automatic Toll Gate (ATG) currently uses contactless transactions using Radio Frequency IDentification (RFID) technology. Tracking and monitoring objects (the car) with RFID is carried out in real-time and is required to keep up with the speed of an object (the car). The On-Board Unit (OBU) transponder installed on the car's windshield and the Road Side Unit (RSU) installed on the ATG are the main components of the Dedicated Short-Range Communication (DSRC) system, which allows the car and ATG to communicate with each other and carry out transactions, including online toll payments, without …

Non-Invasive Monitoring Device For Early Detection Of Breast Cancer Related Lymphedema, Amy Prendergast

Honors Theses and Capstones

Breast Cancer Related Lymphedema (BCRL) is a common co-morbidity in cancer survivors following neoadjuvant therapies such as chemotherapy, radiation, and/or surgery. It is brought about by the disruption in the lymphatic system (think lymph node biopsy) that leads to a buildup of lymphatic fluid in the arm. Current diagnostic strategies for this condition are merely retroactive, and fairly limited in the parameters that are examined to ensure patient well-being long term. We hypothesize that with an approach that mimics bioimpedance spectroscopy analysis, we will be able to provide a clinical support tool that would better determine early stages of lymphedema …

Energy Harvesting For Residential Microgrid Distributed Sensor Systems, Devin C. Whalen

Master’s Theses

Microgrids are localized, independent power grids that can operate while connected to the larger electrical grid. These systems make intelligent decisions regarding power management and use an array of components to monitor power generation, consumption, and environmental conditions. While this technology can save end users money, the complexity of installation and maintenance has limited the adoption of microgrids in residential spaces. To simplify this technology for end users, the next evolution of microgrid components includes sensors that are wireless and ambiently powered.

Even with a microgrid installed, significant energy is wasted in residential spaces. To address this loss, energy harvesting …

Autonomous Damage And Structure Scanning Drone, Natasha Ninan, Amber Long, Lee Nestor, Emmanuel Jensen

Williams Honors College, Honors Research Projects

Remote damage analysis plays a crucial role in lowering the risk associated with human presence at dangerous sites. This project focuses on developing a system for remote structural examination using photogrammetric techniques and analysis. The system utilizes a drone equipped with cameras for photogrammetry, and LiDARs for navigation. This setup can enable efficient structural model generation with the collection of images from multiple points. A novel structural analysis is performed to detect potential damage or points of failure in the structure.

Key components include a teleoperated drone, a software pipeline for photogrammetric analysis, collision protection mechanisms, and a user interface. …

Application Of Fusion Based Deep Learning Models To Improve Millimeter Wave Beamforming, Abishek Subramanian

Dissertations, Master's Theses and Master's Reports

This study addresses the challenge of selecting millimeter Wave (mmWave) beamforming pairs for vehicle-to-infrastructure (V2I) communication, to mitigate latency in highly dynamic vehicular environments. We investigate the use of out-of-band sensor data as side information to model mmWave ray tracing paths and predicting a subset of top-K optimal beamforming pairs for efficient and low-latency searches. Unimodal-Fusion Deep Learning (F-DL) networks was applied to enhance mmWave beamforming process. We started by first investigating the centralized architecture, and then explored a novel distributed architecture through federated learning to minimize resource and latency overheads. The distributed architecture incorporates two biased client selection strategies: …

Cross-Layer Performance Evaluation Of C-V2x, Dhruba Sunuwar

College of Graduate Studies: Theses & Dissertations

The evolution of connected vehicles from a distant futuristic concept to an integral part of daily life is indisputable. Vehicle-to-everything communication (V2X) serves as the cornerstone of this transformation, facilitating seamless interaction among vehicles, infrastructure, pedestrians, and networks. However, evaluating V2X system performance proves intricate due to the dynamic nature of vehicles influenced by mobility factors. To address this complexity, we have developed a specialized system-level simulator expressly for evaluating V2X communication performance. Notably, the simulator encompasses (i) intelligent transportation system (ITS) scenarios integrated into a geographical framework and (ii) the capability to assess cross-layer performance spanning physical (PHY) and …

Information Access For Infrastructurally-Challenged Environments And Beyond Through Mutually Aware Spectrum Sharing Technologies, Karyn Doke

Electronic Theses & Dissertations (2024 - present)

The Radio Frequency (RF) spectrum is scarce and to make it available for new mobile wireless services, regulators are forced to re-allocate spectrum from existing services or develop mechanisms to share spectrum with new entries. Television White Space (TVWS) and Citizen Broadband Radio Service (CBRS) are two examples of recently commercialized spectrum sharing technologies. TVWS enables sharing among fixed wireless broadband technologies (secondary users) and terrestrial TV broadcast services (primary users). CBRS enables spectrum sharing among 5G/LTE (secondary users) and naval radar (primary users). With both technologies, a central database determines when it is safe for secondary users to operate …

Enhancing 5g Fixed Wireless Access In Rural Settings Via Machine Learning-Driven Resource Optimization, Maryam Amini

Graduate Theses, Dissertations, and Problem Reports (ETD)

Providing broadband access to rural communities continues to be an important societal problem whose solution would help to break down the digital divide. While 5G wireless networks may be used for rural broadband, a key challenge is the placement of base stations, which is exacerbated by the use of high frequencies in the millimeter-wave band. Such technology requires an unobstructed line of sight, demanding meticulous planning of the number, height, and location of base stations for optimal coverage. Conventional methods, such as ray-tracing to simulate signal propagation across varied terrain, are computational costly and not feasible for vast coverage areas. …

Comparative Analysis Of Deep Learning-Based Anomaly Detection Models For Gps Spoofing Detection, Hasan Mirzakhaninafchi

Electronic Theses and Dissertations

As autonomous vehicles (AVs) become integral to modern transportation, their susceptibility to cyber-attacks, particularly GPS spoofing, presents a serious security threat. This study addresses these challenges by applying a suite of deep learning models to enhance the detection of anomalous GPS signals. Focusing on autoencoder-based architectures, the proposed models such as long short-term memory-based variational autoencoder (LSTM-VAE), LSTM-based autoencoder (LSTM-AE), multilayer perceptron-based variational autoencoder (MLP-VAE), MLP-based Autoencoder (MLPAE), Stacked LSTM-based variational autoencoder (Stacked-LSTM-VAE), stacked LSTM-based autoencoder (Stacked-LSTM-AE), memory-augmented-LSTM-VAE (Mem-LSTM-VAE), and time-series-anomaly-detection-generative-adversarial-networks (TadGAN) were trained exclusively on authentic GPS data. This unsupervised learning approach which used for the above-mentioned models enables …

Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In this paper, we implement a comprehensive three-class system to categorize social media discussions about Islam and Muslims, enhancing the typical binary approach. These classes are: I) General Discourse About Islam and Muslims, II) Criticism of Islamic Teachings and Figures, and III) Comments Against Muslims. These categories are designed to balance the nuances of free speech while protecting diverse groups like Muslims, ex-Muslims, LGBTQ+ communities, and atheists. By utilizing machine learning and employing transformer-based models, we analyze the distribution and characteristics of these classes in social media content. Our findings reveal distinct patterns of user engagement with topics related to …

Generalized Functions In The Study Of Signals And Systems, Erik I. Verriest, Gunther Dirr, W. Steven Gray

Electrical & Computer Engineering Faculty Publications

We collect three instances where the theory of generalized functions may still make contributions to the study of signals and systems. In the first, a purely algebraic approach is presented for LTI-ODE's, in terms of two operators, D and T, respectively the differentiation operator and the multiplication-by-the-independent-variable operator. This formalism adds simplicity, a duality theory, and nicely generalizes to other classes of operator equations and their solutions. In the second part we extend the classical bilateral Laplace transform to include Bohl functions with support in ℝ by invoking Sato's hyperfunctions. Finally, in the third case we use the Colombeau algebra …

Explore Security And Machine Learning Applications In Next Generation Wireless Networks, Haolin Tang

Theses and Dissertations

Next-generation (NextG) or Beyond-Fifth-Generation (B5G) wireless networks have become a prominent focus in academic and industry circles. This is driven by the increasing demand for cutting-edge applications such as mobile health, self-driving cars, the metaverse, digital twins, virtual reality, and more. These diverse applications typically require high communication network performance, including spectrum utilization, data speed, and latency. New technologies are emerging to meet the communication requirements of various applications. Intelligent Reflecting Surface (IRS) and Artificial Intelligence (AI) are two representatives that have been demonstrated as promising and powerful technologies in NextG communications. While new technologies significantly enhance communication performance, they …

Enhancing Research Productivity: Seamless Integration Of Personal Devices And Hpc Resources With The Cybershuttle Notebook Gateway, Yasith Jayawardana, Dimuthu Wannipurage, Eroma Abeysinghe, Suresh Marru

Computer Science Faculty Publications

Scientists often utilize personal laptops and workstations for initial research stages and turn to high-performance computing (HPC) supercomputers for compute-intensive tasks. However, seamless transitions between these environments are vital for enhancing productivity and accelerating research progress. Our paper presents the Cybershuttle Notebook Gateway, an open-source framework crafted to streamline this transition, optimize resource utilization, and reduce time-to-science for researchers. Leveraging JupyterLab, the framework extends kernel mechanics for seamless provisioning and connection to remote HPC cluster kernels. We delve into its architecture, which separates user authentication, kernel provisioning, and remote file system access. Additionally, we highlight practical capabilities like analyzing network …

5g New Radio Access And Core Network Slicing For Next-Generation Network Services And Management, Abdullah Ridwan Hossain

Dissertations

In recent years, fifth-generation New Radio (5G NR) has attracted much attention owing to its potential in enhancing mobile access networks and enabling better support for heterogeneous services and applications. Network slicing has garnered substantial focus as it promises to offer a higher degree of isolation between subscribers with diverse quality-of-service requirements. Integrating 5G NR technologies, specifically the mmWave waveform and numerology schemes, with network slicing can unlock unparalleled performance so crucial to meeting the demands of high throughput and sub-millisecond latency constraints.

While conceding that optimizing next-generation access network performance is extremely important, it needs to be acknowledged that …