Computer Engineering Commons^™

Ahead: Automatic Holistic Energy-Aware Design Methodology For Mlp Neural Network Hardware Generation In Proactive Bmi Edge Devices, Nan-Sheng Huang, Yi-Chung Chen, Jørgen Christian Larsen, Poramate Manoonpong

Electrical and Computer Engineering Faculty Research

The prediction of a high-level cognitive function based on a proactive brain–machine interface (BMI) control edge device is an emerging technology for improving the quality of life for disabled people. However, maintaining the stability of multiunit neural recordings is made difficult by the nonstationary nature of neurons and can affect the overall performance of proactive BMI control. Thus, it requires regular recalibration to retrain a neural network decoder for proactive control. However, retraining may lead to changes in the network parameters, such as the network topology. In terms of the hardware implementation of the neural decoder for real-time and low-power …

A Capacitive Sensing Gym Mat For Exercise Classification & Tracking, Adam Goertz

Computer Science and Computer Engineering Undergraduate Honors Theses

Effective monitoring of adherence to at-home exercise programs as prescribed by physiotherapy protocols is essential to promoting effective rehabilitation and therapeutic interventions. Currently physical therapists and other health professionals have no reliable means of tracking patients' progress in or adherence to a prescribed regimen. This project aims to develop a low-cost, privacy-conserving means of monitoring at-home exercise activity using a gym mat equipped with an array of capacitive sensors. The ability of the mat to classify different types of exercises was evaluated using several machine learning models trained on an existing dataset of physiotherapy exercises.

Lexicon Based Approaches To Sentiment Analysis Of Spanish Tweets: A Comparative Study, Jean Roca

Computer Science and Computer Engineering Undergraduate Honors Theses

Sentiment analysis is a natural language processing technique that aims to classify text based on the emotions expressed in them. It is a research area that has been around for almost 20 years and has seen a lot of development. The works presented in this paper attempts to target a less-developed area in sentiment analysis known as multilingual sentiment analysis. More specifically, multilingual sentiment analysis of micro-texts. Using the existing WordNet lexicon and a domain-specific lexicon for a corpus of Spanish tweets, we analyze the effectiveness of these techniques.

Using Robotics And Engineering Design Inquiries To Optimize Learning For Middle Level Teachers: A Case Study, Iman Chafik Chahine, Norman Robinson Iii, Kimbeni Mansion

Publications & Research

This exploratory case study reports findings on 20 middle-level science and mathematics teachers’ perceptions of the effectiveness of a one-year project in which teachers engaged in using robotics and engineering design inquiries in their classrooms. Principled by Bandura’s Social Learning Theory (SLT) and using mixed methods approaches, the study measured teachers' efficacy through the Mathematics Teaching Efficacy Belief Instrument (MTEBI) and observation logs before and after the program. The results of this study showed statistically significant differences between PRE MTEBI and POST MTEBI scores. Furthermore, five themes emerged that illuminated potential affordances and constraints that teachers perceive as opportunities and …

Prioritized Anomaly Catalog Generation Using Model-Based Reasoning, Jake Hedlund

Computer Science and Engineering Master's Theses

Anomaly management—the detection, diagnosis, and resolution of anomalies in a system—is traditionally performed using experiential techniques which are quickly computed, but poorly structured. Newer model-based approaches are more systematic and higher performing but are computationally expensive, which is a particular challenge for execution in an operational environment. This paper builds on a novel system to pre-compute model-based anomaly symptoms to enable quick retrieval and diagnosis in operational settings. New additions to this system include a simplified model interface, anomaly likelihoods associated with each component, and easier interpretation of results. The implemented system has been used successfully to detect and diagnose …

Locating Relay Nodes To Maximize Wireless Sensor Network Lifetime: A Numerical Study, Maria Rene Arandia Jimenez

Industrial Engineering Undergraduate Honors Theses

A wireless sensor network (WSN) is a group of sensors deployed over an area, which monitor changes in the environment, collects them as data and forwards it between sensors through wireless links. Data is routed, either in a single-hop or multi-hop manner, with the goal of getting this collected data to the sink nodes, which have higher computational capabilities and connects the network with a user interface. Studies have determined that multi-hop WSNs that integrate relay nodes, which function is to only receive and forward data, can maximize lifetime network. A linear programming model, created by Chang and Tassiulas in …

Using Online Discussions To Connect Theory And Practice In Core Engineering Undergraduate Courses, Lisa B. Bosman, Somesh Roy, Walter M. Mcdonald, Cristinel Ababei

Electrical and Computer Engineering Faculty Research and Publications

Providing engineering undergraduate students opportunities to connect real-world applications with theory is key to preparing them for the workforce; however, this task often requires a balancing act between meeting course objectives in content-heavy core engineering undergraduate courses and providing experiences that connect real-world applications with theory. This study seeks to address this problem through the integration of online discussion prompts to promote a connection to real-world practical applications. The study included undergraduate students enrolled in three engineering core courses. The hypothesis was that participation in online discussions (using prompts) would lead to 1) an increase in student empowerment towards self-regulated …

Stay-At-Home Motor Rehabilitation: Optimizing Spatiotemporal Learning On Low-Cost Capacitive Sensor Arrays, Reid Sutherland

Graduate Theses and Dissertations

Repeated, consistent, and precise gesture performance is a key part of recovery for stroke and other motor-impaired patients. Close professional supervision to these exercises is also essential to ensure proper neuromotor repair, which consumes a large amount of medical resources. Gesture recognition systems are emerging as stay-at-home solutions to this problem, but the best solutions are expensive, and the inexpensive solutions are not universal enough to tackle patient-to-patient variability. While many methods have been studied and implemented, the gesture recognition system designer does not have a strategy to effectively predict the right method to fit the needs of a patient. …

Reinforcement Learning In Self Organizing Cellular Networks, Roohollah Amiri

Boise State University Theses and Dissertations

Self-organization is a key feature as cellular networks densify and become more heterogeneous, through the additional small cells such as pico and femtocells. Self- organizing networks (SONs) can perform self-configuration, self-optimization, and self-healing. These operations can cover basic tasks such as the configuration of a newly installed base station, resource management, and fault management in the network. In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration, and maintenance of the network. In fact, SONs aim to bring two main factors in play: intelligence and autonomous adaptability. …

A Framework For Vector-Weighted Deep Neural Networks, Carter Chiu

UNLV Theses, Dissertations, Professional Papers, and Capstones

The vast majority of advances in deep neural network research operate on the basis of a real-valued weight space. Recent work in alternative spaces have challenged and complemented this idea; for instance, the use of complex- or binary-valued weights have yielded promising and fascinating results. We propose a framework for a novel weight space consisting of vector values which we christen VectorNet. We first develop the theoretical foundations of our proposed approach, including formalizing the requisite theory for forward and backpropagating values in a vector-weighted layer. We also introduce the concept of expansion and aggregation functions for conversion between real …

Predictive Modeling Of Iphone 7 Charge Rates Using Least Squares Curve Fitting, Grace Cahill

Honors Theses

In a time where individuals depend on their cell phones, the need for a long lasting and quick charging battery life is imperative. As information regarding how long a battery can remained charged is highly advertised, there is no information regarding how long it would take for a dead phone battery to completely charge. This study determined the amount of time it will take an iPhone 7 to charge from 0% to 100% using the standard charging cable under four different charging conditions. The charge percentage was recorded every two minutes until it was fully charged with this process being …

Augmented Reality In A Dynamic Drone Based Environment, Christopher Davis

Honors Theses

Augmented Reality (AR) and Unmanned Ariel Vehicles (UAVs) are fast advancing technologies, and this research seeks to combine them to offer an effective, user friendly approach for monitoring infrastructure. Drones provide a means to easily access otherwise difficult to reach locations and visualize useful information with Augmented Reality. A UAV employs a wide-angle view and, when paired with AR, this will enable the user to better complete their task by effortlessly providing the critical information they need in the most intuitive way possible. This research is particularly applicable for civil applications such as construction and monitoring of difficult to access …

Investigation Of The Effects Of A Situated Learning Digital Game On Mathematics Education At The Primary School Level, Mariana Rocha

Doctoral

Previous research suggests games can improve learning outcomesand students’ motivation. However, there still exists insufficient clarity on the design principles and pedagogical approach that should underpinmathematics educational games. This thesis is aimed at evaluating the effects of an educationalgame on the learningperformance and levels of anxiety promoted by mathematics activities of primary school students. The game was designed based on theprinciples of situated learning, following acombination of an in-depth literature review, a collection of teachers’ perceptions about educational games, and features ofclassroom games. Empirical evaluation of the game was performed through a 5-weeks experiment carried out in three Irish schools, …

Secure Network-On-Chip Against Black Hole And Tampering Attacks, Luka Daoud

Boise State University Theses and Dissertations

The Network-on-Chip (NoC) has become the communication heart of Multiprocessors-System-on-Chip (MPSoC). Therefore, it has been subject to a plethora of security threats to degrade the system performance or steal sensitive information. Due to the globalization of the modern semiconductor industry, many different parties take part in the hardware design of the system. As a result, the NoC could be infected with a malicious circuit, known as a Hardware Trojan (HT), to leave a back door for security breach purposes. HTs are smartly designed to be too small to be uncovered by offline circuit-level testing, so the system requires an online …

An Fpga-Based Hardware Accelerator For The Digital Image Correlation Engine, Keaten Stokke

Graduate Theses and Dissertations

The work presented in this thesis was aimed at the development of a hardware accelerator for the Digital Image Correlation engine (DICe) and compare two methods of data access, USB and Ethernet. The original DICe software package was created by Sandia National Laboratories and is written in C++. The software runs on any typical workstation PC and performs image correlation on available frame data produced by a camera. When DICe is introduced to a high volume of frames, the correlation time is on the order of days. The time to process and analyze data with DICe becomes a concern when …

Analog Versus Digital Guitar Pedals, Shaping Guitar Tones And Sparking Debates, Cameron Karren

Capstone Projects and Master's Theses

This paper goes into the history of guitar effects, what exactly they are, how they have evolved, and what they are like today. It also presents the results of an experiment that compares perceptions of differences between analog and digital guitar pedals.

Decoding Ldpc Codes With Probabilistic Local Maximum Likelihood Bit Flipping, Rejoy Roy Mathews

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Communication channels are inherently noisy making error correction coding a major topic of research for modern communication systems. Error correction coding is the addition of redundancy to information transmitted over communication channels to enable detection and recovery of erroneous information. Low-density parity-check (LDPC) codes are a class of error correcting codes that have been effective in maintaining reliability of information transmitted over communication channels. Multiple algorithms have been developed to benefit from the LDPC coding scheme to improve recovery of erroneous information. This work develops a matrix construction that stores the information error probability statistics for a communication channel. This …

Multiplex Memory Network For Collaborative Filtering, Xunqiang Jiang, Binbin Hu, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Recommender systems play an important role in helping users discover items of interest from a large resource collection in various online services. Although current deep neural network-based collaborative filtering methods have achieved state-of-the-art performance in recommender systems, they still face a few major weaknesses. Most importantly, such deep methods usually focus on the direct interaction between users and items only, without explicitly modeling high-order co-occurrence contexts. Furthermore, they treat the observed data uniformly, without fine-grained differentiation of importance or relevance in the user-item interactions and high-order co-occurrence contexts. Inspired by recent progress in memory networks, we propose a novel multiplex …

Geolocation Based On Signal Level Measurement And Time Advance Inside The Network, Zaenab Shakir

Theses and Dissertations

The increasing demands for location-based services in recent years led to provide accurate location information inside the network. The applications for location-based service are assisting emergency request, fraud defense, social media, and marketing. All these demands make the position accuracy highly required. Various techniques and methods have been developed to estimate the position of mobile device inside the network such as RSS, TOA, AOA, and TDOA. In this work, a new method to estimate the accuracy of locating active LTE cellular subscribers. The proposed method is a network-based technique and relies on the Reference Signal Received Power (RSRP) measurements and …

On The Characterization Of Natural Language Structure And Literary Stylometry - A Network Science Approach, Younis Anas Younis Al Rozz

Theses and Dissertations

Natural language processing (NLP) techniques have been through many advancements in recent years, linguistics and scientist utilized these techniques to solve many challenges related to written language and literary. Problems such as finding the genetic relationships among languages, attributing author of a text and categorizing text by genre have been treated throughout the years using conventional statistical methods, for instance, bag of words (BoW), N-gram, the frequency of words and the lexical distance between words. By considering written language as a complex system, network science tools and techniques can be used to address those problems. A unified methodology is proposed …

The Dynamic Control Platform: Reinventing The Wheel, One Leg At A Time, Angel Javier Solis

UNLV Theses, Dissertations, Professional Papers, and Capstones

Upright bipedal walking is a complex balance of forces and actions that is almost taken for granted. How this system is modeled, how it affects a prosthesis, and how it can be implemented in the real world are topics that the proposed Dynamic Control Platform aims to address.

The Dynamic Control Platform (DCP) is a bipedal robot designed to test bio-inspired control algorithms with the aim to smooth out the walking experience for prosthetic legs. The main control paradigm that the DCP centers on the principle of orthogonal constraint, which aims to enforce a perpendicular relationship between the center of …

Computational Techniques In Medical Image Analysis Application For White Blood Cells Classification., Omar Dekhil

Electronic Theses and Dissertations

White blood cells play important rule in the human body immunity and any change in their count may cause serious diseases. In this study, a system is introduced for white blood cells localization and classification. The dataset used in this study is formed by two components, the first is the annotation dataset that will be used in the localization (364 images), and the second is labeled classes that will be used in the classification (12,444 images). For the localization, two approaches will be discussed, a classical approach and a deep learning based approach. For the classification, 5 different deep learning …

Deep Learning And Polar Transformation To Achieve A Novel Adaptive Automatic Modulation Classification Framework, Pejman Ghasemzadeh

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Automatic modulation classification (AMC) is an approach that can be leveraged to identify an observed signal's most likely employed modulation scheme without any a priori knowledge of the intercepted signal. Of the three primary approaches proposed in literature, which are likelihood-based, distribution test-based, and feature-based (FB), the latter is considered to be the most promising approach for real-world implementations due to its favorable computational complexity and classification accuracy. FB AMC is comprised of two stages: feature extraction and labeling. In this thesis, we enhance the FB approach in both stages. In the feature extraction stage, we propose a new architecture …

Revisiting Elliptic Curve Cryptography With Applications To Post-Quantum Sidh Ciphers, Wesam Nabil Eid

Theses and Dissertations

Elliptic Curve Cryptography (ECC) has positioned itself as one of the most promising candidates for various applications since its introduction by Miller and Kolbitz in 1985 [53, 44]. The core operation for ECC is the scalar multiplication [k]P where many efforts have addressed its computation speed. Here we introduce an efficient approach for calculating elliptic curve operations by a novel regrouping of terms and creating new projective representation operators and increasing parallelism. These operators and the corresponding projective coordinate representations are shown to lead to adjusted versions of scalar multiplication algorithms that are evaluated. These techniques enable more opportunities for …

Improving Wake-Up-Word And General Speech Recognition Systems, Gamal Mohamed Bohouta

Theses and Dissertations

Automatic Speech Recognition (ASR), a technology that allows a machine to recognize the utterances spoken into a microphone by a person and then converts it to text, is commonly used for different types of applications, such as command and control systems, personal assistant systems, medical systems, disabilities systems, dictation systems, telephony systems, and embedded applications. Due to its extensive use, interest in ASR technology has surged among inventors and researchers alike. They have worked diligently to improve the performance of the ASR systems by developing several techniques or approaches in different aspects,such as enhancing features, training an acoustic model, enhancing …

Model Optimization For Edge Devices, Adolf Anthony D’Costa

Theses and Dissertations

Edge devices are undergoing groundbreaking computing transformation, which lets us tap into artificial intelligence, quantum computing, 5th generation network capability, fog networking, and computing complex algorithms. Edge systems have substantial advantages over the conventional system in terms of scalability, optimized resources, reliability, and security. The proliferation of such resource-constrained devices in recent years has resulted in the generation of a large quantity of data; these data-producing devices are attractive targets for applications of machine learning. Machine learning models, especially deep learning neural networks, produced models that have high accuracy and prediction capability, but it comes at the cost of computation …

Coordinated Autonomy Of Unmanned Aerial Vehicles (Uavs), Paribartan Dhakal

Honors Theses

Unmanned Aerial Vehicles (UAVs) are being extensively used in diverse sectors of the society for various tasks ranging from videography to an extremely sensitive situation such as first responders helping during a disaster. It has been seen that if a fleet of UAVs is deployed, they can perform a task quicker and more efficiently than a single UAV. With an increase in the number of UAVs, a problem arises of handling them with proper control structures. It has been studied that the Behavior Trees (BT) can be a better control architecture to handle the autonomous vehicles, as BTs are more …

Enhancing Cellular Communications For Uavs Via Intelligent Reflective Surface, Dong Ma, Ming Ding, Mahbub Hassan

Research Collection School Of Computing and Information Systems

Intelligent reflective surfaces (IRSs) capable of reconfiguring their electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we analyze the potential of IRS in enhancing cellular communications for UAVs, which currently suffers from poor signal strength due to the down-tilt of base station antennas optimized to serve ground users. We consider deployment of IRS on building walls, which can be remotely configured by cellular base stations to coherently direct the reflected radio waves towards specific UAVs in order to increase their received signal strengths. Using the recently …

Brief Survey And Testbed Development For Blockchain Based Internet Of Things, Aishwant Ghimire

Honors Theses

Blockchain and the Internet of Things are uprising in today’s technology world. Internet of Things or IoT are the devices with unique identifiers that share data or information over the internet whereas, Blockchain is a peer to peer network with a distributed ledger that contains a list of blocks that are linked together by cryptography. Fascinated and motivated by blockchain and Internet of Things (IoT), this thesis provides a review on blockchain based internet of things and also introduces a working testbed that integrates the two together. It also uses IoT device to invoke transactions into the blockchain. The reasons …

Emotional Awareness During Bug Fixes: A Pilot Study, Jada O. Loro, Abigail L. Schneff, Sarah J. Oran, Bonita Sharif

School of Computing: Dissertations, Theses, and Student Research

This study examines the effects of a programmer's emotional awareness on progress while fixing bugs. The goal of the study is to capitalize on emotional awareness to ultimately increase progress made during software development. This process could result in improved software maintenance.