Systems and Communications Commons^™

Design And Performance Analysis Of A Chemically-Etched Flexible Nfc Tag Antenna, Muhammad Enayetur Rahman, Marwan Abdelatti, Manbir Singh Sodhi, Kunal Mankodiya

Electrical & Computer Engineering Faculty Publications

Near Field Communication (NFC) is a perfect example of ubiquitous computing that is secured, short-ranged, low-powered contactless communication. High demand is predicted for NFC, especially with wearables, and the variety of applications may require that this technology be fabricated onto different materials. In this research, we first designed flexible NFC antennas based on mathematical models. A simulation of the NFC coil inductance was performed and verified using the predictive models. Later, two flexible NFC antennas, including 160x80 mm2 (rectangular) and 80x80 mm2 (square) were fabricated through a chemical etching process and verified at 13.56 MHz frequency. Two experiments 1) Read …

Autonomous Wildfire Detection System, Matthew Pyle, Nick Garrett

Williams Honors College, Honors Research Projects

According to the National Fire Protection Association Journal, federal wildfire suppression costs in the United States have risen from an annual average of about $425 million from 1985 to 1999 up to $1.6 billion from 2000 to 2019. On average, more than 200,000 acres in the United States are burned per year due to wildfires, with more than 700,000 acres burned in 2020 alone. With the risk of wildfire ever rising, there is a need for better early detection of remote wildfires, as existing methods often include long delays like satellites or rely on human lookout towers. The objective is …

Smart Kettlebell, Cole Russell, Patrick Davis, Ryan Deboer, Isaac Stecker

Williams Honors College, Honors Research Projects

The purpose of design team 08’s senior design project is to create and implement a workout device that will be able to calculate the caloric expenditure, repetitions, and length of a workout. Once the duration of a workout is completed, the user metrics will be communicated from the kettlebell system to a user application via Bluetooth. The caloric expenditure will be used using the data that is provided from the accelerometer. The duration of the workout will be controlled using a push button that is connected to the kettlebell system that will allow for the start and stop of a …

Smart Uv-C Disinfectant Module, Nicole Baldy, Luke Rogers, Haitham Saleh

Williams Honors College, Honors Research Projects

The Smart UV Disinfectant device shall sanitize objects which are 18”x14”x8” or smaller and less than 20 lbs. using UV-C light. This device should contain many safety measures to prevent human and animal exposure to the UV-C light and have no public touchpoints to operate the interface. In order to achieve the first objective, this device shall contain a "sanitizing chamber" which completely encloses the object to be sanitized to prevent outside exposure with detection of any lifeforms inside of the chamber; for the second objective, it will contain a wireless interface to an Android application which can be used …

Lapnitor: A Web Service That Protects Your Laptop From Theft., Michael Ameteku

Williams Honors College, Honors Research Projects

Laptop theft is an issue worldwide. According to an article from 2018, Security Boulevard stated that a laptop is stolen every 53 seconds. Using a laptop's camera, we can monitor the surroundings of the laptop and reduce a laptop's probability of being stolen. According to the University of Pittsburgh, a laptop has a 1-in- 10 chance of being stolen and nearly half of these thefts occur in offices or classrooms. These thefts mostly occur when a laptop owner leaves their device unattended for a certain period of time to maybe go visit the restroom or attend to a call when …

Adapting Deep Learning For Underwater Acoustic Communication Channel Modeling, Li Wei

Dissertations, Master's Theses and Master's Reports

The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work.

Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and …

Energy-Efficient Dynamic Directional Modulation With Electrically Small Antennas, Adam Narbudowicz, Abel Zandamela, Nicola Marchetti, Max Ammann

Articles

This letter proposes a compact and energy-efficient directional modulation scheme for small and power constrained devices (e.g., Internet of Things wireless sensors). The scheme uses a uniform circular array of monopole antennas, with a single radiofrequency chain and antenna active at a time. The antennas can be located in close proximity, offering significant size reduction. Furthermore, the schemewas demonstrated to operate successfully with an array of 0.6 λ diameter. The system does not generate additional artificial noise, limiting interference to other systems. Finally, the antenna switching sequence can be randomly generated and without fine-tuned synchronization with the transmitter.

Leveraging Machine Learning For Detecting Iot-Based Interference In Operational Wifi Networks, Josh Pulse

Honors Program Theses

IoT (Internet of Things) devices have become increasingly popular in recent years while WiFi continues to serve as primary network provider indoors. With the advancements in technology, the networks of IoT devices continue to weave closely with indoor WiFi network deployments. Both kinds of these networks primarily operate in 2.4 GHz ISM Band (though latest WiFi standards can operate in 5 GHz and 60 GHz bands, too). With the multitude of tiny IoT sensors being deployed indoors alongside operational WiFi networks, severe interference scenarios cannot be ruled out. As a result of this interference, performance of WiFi networks is bound …

Security Concerns On Machine Learning Solutions For 6g Networks In Mmwave Beam Prediction, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Devrim Unal

Engineering Technology Faculty Publications

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial …

Speaker Encoding For Zero-Shot Speech Synthesis, Tristin W. Cory

Graduate Theses/Dissertations

Spoken communication, for many, is an essential part of everyday life. Some individuals can lose or not be born with the ability to speak. To function on a day-to-day basis, these individuals find other ways of communication. Adaptive speech synthesis is one of those ways. It recreates a user’s previous voice or creates a voice that blends with their regional dialect. Current adaptive speech synthesis techniques that achieve human-like speech require thirty minutes, to a few hours of high-quality audio recordings of a target speaker. This amount of recorded audio is not commonly possessed by people in need of a …

Multimodal Adversarial Learning, Uche Osahor

Graduate Theses, Dissertations, and Problem Reports (ETD)

Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models …

A Primer On Software Defined Radios, Dimitrie C. Popescu, Rolland Vida

Electrical & Computer Engineering Faculty Publications

The commercial success of cellular phone systems during the late 1980s and early 1990 years heralded the wireless revolution that became apparent at the turn of the 21st century and has led the modern society to a highly interconnected world where ubiquitous connectivity and mobility are enabled by powerful wireless terminals. Software defined radio (SDR) technology has played a major role in accelerating the pace at which wireless capabilities have advanced, in particular over the past 15 years, and SDRs are now at the core of modern wireless communication systems. In this paper we give an overview of SDRs that …

Effect Of Freestream Noise On Hypersonic Crossflow-Induced Boundary-Layer Transition, Andrew Bustard, Thomas J. Juliano, Harrison B. Yates, Mark Noftz, Joseph Jewell

Publications

Between the windward and leeward rays, boundary-layer transition on cones at angle of attack in hypersonic flow is dominated by the crossflow instability [1, 2], wherein stationary and/or traveling crossflow vortices develop and can breakdown into turbulence [3]. Stationary crossflow modes are caused by surface roughness or any other steady forcing and produce disturbances that are fixed in place on the surface [3–6]. Stationary vortices create a generalized inflection point in the velocity profile that is inviscidly unstable [7]. This distorted mean flow develops secondary instabilities, whose growth and breakdown is one path to turbulence [8–10]. Traveling crossflow modes are …

Stress Classification Using Deep Learning With 1d Convolutional Neural Networks, Abdulrazak Yahya Saleh, Lau Khai Xian

Knowledge Engineering and Data Science

Stress has been a major problem impacting people in various ways, and it gets serious every day. Identifying whether someone is suffering from stress is crucial before it becomes a severe illness. Artificial Intelligence (AI) interprets external data, learns from such data, and uses the learning to achieve specific goals and tasks. Deep Learning (DL) has created an impact in the field of Artificial Intelligence as it can perform tasks with high accuracy. Therefore, the primary purpose of this paper is to evaluate the performance of 1D Convolutional Neural Networks (1D CNNs) for stress classification. A Psychophysiological stress (PS) dataset …

Machine Learning Based Critical Resource Allocation In Mixed-Traffic Cellular Networks, Mohamed Nomeir

Theses and Dissertations

The proliferation of cellular networks over the past two decades has encouraged the expansion of their use in many modern applications. These applications involve the use of data traffic of different quality of service (QoS) requirements. Some of these requirements are quite stringent such as in the case of critical Internet of Things (IoT) health care, military and homeland security applications. This situation resulted in imposing a variety of resource allocation requirements on the cellular network operation in a simultaneous manner.

In this thesis, we consider the challenging problem of mixed-traffic resource allocation, or scheduling, in cellular networks. We focus …

Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya

Electrical and Computer Engineering Faculty Publications

In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debriscovered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the …

A Comprehensive Analysis Of Reward Function For Adaptive Traffic Signal Control, Abu Rafe Md Jamil, Naushin Nower

Knowledge Engineering and Data Science

Adaptive traffic control systems (ATCS) can play an essential role in reducing traffic congestion in urban areas. The main challenge for ATSC is to determine the proper signal timing. Recently, Deep Reinforcement Learning (DRL) has been used to determine proper signal timing. However, the success of the DRL algorithm depends on the appropriate reward function design. There exist various reward functions for ATSC in the existing research. This research presents a comprehensive analysis of the widely used reward function. The pros and cons of various reward algorithms were discussed, and experimental analysis shows that the multi-objective reward function enhances the …

Similarity Identification Of Large-Scale Biomedical Documents Using Cosine Similarity And Parallel Computing, Merlinda Wibowo, Christoph Quix, Nur Syahela Hussien, Herman Yuliansyah, Faisal Dharma Adhinata

Knowledge Engineering and Data Science

Document similarity computation is an important research topic in information retrieval, and it is a crucial issue for automatic document categorization. The similarity value is between 0 and 1, then the closest value to 1 is represented both documents is considered more relevant, vice versa. However, the large scale of textual information has created the problem of finding the relevance level between documents. Therefore, the relevance between mesh heading text in the PubMed documents is higher than the relevance of the abstract text in the PubMed documents. Furthermore, parallel computing is implemented to speed up the large-scale documents similarity identification …

Recognition Of Handwritten Javanese Script Using Backpropagation With Zoning Feature Extraction, Anik Nur Handayani, Heru Wahyu Herwanto, Katya Lindi Chandrika, Kohei Arai

Knowledge Engineering and Data Science

Backpropagation is part of supervised learning, in which the training process requires a target. The resulting error is transmitted back to the units below in its training process. Backpropagation can solve complicated problems because it consumes less memory than other algorithms. In addition, it also can produce solutions with a low error rate while executing less time. In image pattern recognition, backpropagation can be utilized for cultural preservation in many places worldwide, including Indonesia. It is used to recognize picture patterns in Javanese script writings. This study concluded that feature extraction approaches, zoning, and backpropagation could be utilized to distinguish …

Parallel Approach Of Adaptive Image Thresholding Algorithm On Gpu, Adhi Prahara, Andri Pranolo, Nuril Anwar, Yingchi Mao

Knowledge Engineering and Data Science

Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational times to deal with high-resolution images. The approach involves parallel reduction and parallel prefix sum (scan) techniques …

A Comparative Study Of Machine Learning-Based Approach For Network Traffic Classification, Kien Trang, An Hoang Nguyen

Knowledge Engineering and Data Science

Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to …

Cnn Based Face Recognition System For Patients With Down And William Syndrome, Endang Setyati, Suharyono Az, Subroto Prasetya Hudiono, Fachrul Kurniawan

Knowledge Engineering and Data Science

Down syndrome, also known as trisomy genetic condition, is a genetic disorder that affects many people. Williams syndrome is a hereditary disorder that can affect anyone at birth. It marks medical and cognitive issues, such as cardiovascular illness, developmental delays, and learning impairments. This is accompanied by exceptional verbal abilities, a gregarious attitude, and a passion for music. Down syndrome and William Syndrome are both genetic illnesses. However, it can be distinguished from the arrangement of chromosome 21. Down syndrome and William syndrome can also be identified by recognizing faces, or facial characteristics, such as observing particular facial features. Therefore, …

Melanoma Classification Based On Simulated Annealing Optimization Neural Network, Edi Jaya Kusuma, Ika Pantiawati, Sri Handayani

Knowledge Engineering and Data Science

Technology development in image processing and artificial intelligence leads to the high demand for smart systems, especially in the health sector. Cancer is one of the diseases with the highest mortality cases worldwide. Melanoma is one of the cancers commonly caused by high exposure to UV light. The earliest the melanoma is identified, the higher the patient's chance of recovering. Therefore, this study proposes melanoma detection based on BPNN optimized by a simulated annealing algorithm. This research utilizes PH2 dermoscopic image data containing 200 color digital images in BMP format. The data is processed using color feature extraction techniques to …

Network Management, Optimization And Security With Machine Learning Applications In Wireless Networks, Mariam Nabil

Theses and Dissertations

Wireless communication networks are emerging fast with a lot of challenges and ambitions. Requirements that are expected to be delivered by modern wireless networks are complex, multi-dimensional, and sometimes contradicting. In this thesis, we investigate several types of emerging wireless networks and tackle some challenges of these various networks. We focus on three main challenges. Those are Resource Optimization, Network Management, and Cyber Security. We present multiple views of these three aspects and propose solutions to probable scenarios. The first challenge (Resource Optimization) is studied in Wireless Powered Communication Networks (WPCNs). WPCNs are considered a very promising approach towards sustainable, …

Analysis Of Deep Learning Methods For Wired Ethernet Physical Layer Security Of Operational Technology, Lucas Torlay

All Theses

The cybersecurity of power systems is jeopardized by the threat of spoofing and man-in-the-middle style attacks due to a lack of physical layer device authentication techniques for operational technology (OT) communication networks. OT networks cannot support the active probing cybersecurity methods that are popular in information technology (IT) networks. Furthermore, both active and passive scanning techniques are susceptible to medium access control (MAC) address spoofing when operating at Layer 2 of the Open Systems Interconnection (OSI) model. This thesis aims to analyze the role of deep learning in passively authenticating Ethernet devices by their communication signals. This method operates at …

Automated Flight Controller Adaptive Compensation For Actuator Failures In Transport Type Aircraft, Daniel James Fresella

Graduate Theses and Dissertations

Aircraft operate over a wide range of conditions including atmospheric, weight, and center ofgravity changes. This presents a substantial challenge to automatic control system designers. When these operating conditions are merged with a partial or full control surface failure, automatic flight control is near impossible with conventional controllers. Additionally, when pilots experience control failure emergencies during flight, workload and fatigue increase drastically. The continued research of automatic flight control systems that can seamlessly adapt to unmodelled failures will enable a new generation of robust aircraft control. In this paper a 9th order 6 degree of freedom aircraft model is used …

Machine Learning For Unmanned Aerial System (Uas) Networking, Jian Wang

Doctoral Dissertations and Master's Theses

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex …

Indoor Positioning Using Synchronized Ultrasonic Ofdma Signals, Julian Bartolone

Master's Theses

This paper proposes a method of short-range indoor localization using differential phase measurements of synchronized two-tone ultrasonic signals in an Orthogonal Frequency Multiple Access (OFDMA) scheme. This indoor positioning system (IPS) operates at an ultrasonic frequency of approximately 40kHz and synchronizes using an infrared signal. The OFDMA scheme allows for a receiver to process the signals from multiple transmitters continuously without the signals interfering with each other. The phases of the signals are measured using Goertzel Filters, allowing for low-complexity frequency content analysis. A MATLAB simulation using the proposed localization method is performed using four transmitter nodes in the corners …

Plasmonic Field-Effect Transistors (Terafets) For 6g Communications, Michael Shur, Gregory Aizin, Taiichi Otsuji, Victor Ryzhii

Publications and Research

Ever increasing demands of data traffic makes the transition to 6G communications in the 300 GHz band inevitable. Short-channel field-effect transistors (FETs) have demonstrated excellent potential for detection and generation of terahertz (THz) and sub-THz radiation. Such transistors (often referred to as TeraFETs) include short-channel silicon complementary metal oxide (CMOS). The ballistic and quasi-ballistic electron transport in the TeraFET channels determine the TeraFET response at the sub-THz and THz frequencies. TeraFET arrays could form plasmonic crystals with nanoscale unit cells smaller or comparable to the electron mean free path but with the overall dimensions comparable with the radiation wavelength. Such …

The Factors Influencing The Acceptance Of Web-Based E-Learning System Among Academic Staffs Of Saudi Arabia, Ikhlas Zamzami

Future Computing and Informatics Journal

It is possible to learn more quickly and effectively with e-learning software development because it provides learners with convenient and flexible learning environments. This allows them to progress further in their careers. Reports on web-based e-learning systems for in-service education have frequently neglected to include the viewpoint of the instructor. In order to conduct quantitative research, a sample of 50 academic staff members was selected. The purpose of this study was to investigate various factors that influence the intention to use web-based e-learning, with the theoretical foundation being provided by university lecturers. According to the findings of the study, the …