Computer Engineering Commons^™

Less-Java, More Type Safety: Type Inference And Static Analysis In Less-Java, Charles D. Hines

Senior Honors Projects, 2020-current

Less-Java is an object-oriented programming language whose primary goal is to help new programmers learn programming. Some of the features of Less-Java that might make it better for beginners are static typing, implicit typing, low verbosity, and built-in support for unit testing. The primary focus of this project is on improving type inference (especially with regards to object-oriented programming) and adding static analysis in the Less-Java compiler.

Artificial Stupidity: Data We Need To Make Machines Our Equals, Michaël Trazzi, Roman V. Yampolskiy

Faculty and Staff Scholarship

AI must understand human limitations to provide good service and safe interactions. Standardized data on human limits would be valuable in many domains but is not available. The data science community has to work on collecting and aggregating such data in a common and widely available format, so that any AI researcher can easily look up the applicable limit measurements for their latest project. AI must understand human limitations to provide good service and safe interactions. Standardized data on human limits would be valuable in many domains but is not available. Data science community has to work on collecting and …

Elicitation And Aggregation Of Data In Knowledge Intensive Crowdsourcing, Dohoon Kim

All Computer Science and Engineering Research

With the significant advance of internet and connectivity, crowdsourcing gained more popularity and various crowdsourcing platforms emerged. This project focuses on knowledge-intensive crowdsourcing, in which agents are presented with the tasks that require certain knowledge in domain. Knowledge-intensive crowdsourcing requires agents to have experiences on the specific domain. With the constraint of resources and its trait as sourcing from crowd, platform is likely to draw agents with different levels of expertise and knowledge and asking same task can result in bad performance. Some agents can give better information when they are asked with more general question or more knowledge-specific task …

Zenneck Waves In Decision Agriculture: An Empirical Verification And Application In Em-Based Underground Wireless Power Transfer, Usman Raza, Abdul Salam

Faculty Publications

In this article, the results of experiments for the observation of Zenneck surface waves in sub GHz frequency range using dipole antennas are presented. Experiments are conducted over three different soils for communications distances of up to 1 m. This empirical analysis confirms the existence of Zenneck waves over the soil surface. Through the power delay profile (PDP) analysis, it has been shown that other subsurface components exhibit rapid decay as compared to the Zenneck waves. A potential application of the Zenneck waves for energy transmission in the area of decision agriculture is explored. Accordingly, a novel wireless through-the-soil power …

Campuspartner: An Assistive Technology For Pedestrians With Mobility Impairments, Cynthia R. Zastudil

Senior Honors Projects, 2020-current

Route-planning applications such as Google Maps and Apple Maps are used by millions of people each month. However, these mapping applications are optimized for vehicle navigation, and although they provide pedestrian routing, the route customization options aren’t sufficient for pedestrian users, especially those with mobility impairments. CampusPartner is an assistive mobile application that was designed with the purpose of supporting people with mobility impairments in planning and previewing their walking routes. By viewing routes in advance, users can see an overview and detailed information about them as well as turn-by-turn instructions. CampusPartner integrates existing services, GraphHopper, OpenStreetMap, and Mapbox, to …

A Virtual 4d Ct Scanner, Xiwen Li

All Computer Science and Engineering Research

4D CT scan is widely used in medical imaging. Images are acquired through phases. In this case, we can track the motion of organs such as heart. However, it also introduces motion artifacts. A lot of research focuses on remove these artifacts. It is difficult to acquire artifact data by a real CT scanner. In this project, we implement a virtual CT machine to simulate the real 4D CT scan. we also conduct experi- ments to check its clinical reality with respect to respiratory and heart motion parameters.

Centrality Of Blockchain, Zixuan Li

All Computer Science and Engineering Research

Decentralization is widely recognized as the property and one of most important advantage of blockchain over legacy systems. However, decentralization is often discussed on the consensus layer and recent research shows the trend of centralization on several subsystem of blockchain. In this project, we measured centralization of Bitcoin and Ethereum on source code, development eco-system, and network node levels. We found that the programming language of project is highly centralized, code clone is very common inside Bitcoin and Ethereum community, and developer contribution distribution is highly centralized. We further discuss how could these centralizations lead to security issues in blockchain. …

Solving Disappearance At Gastech With Visual Analytic Techniques, Saulet Yskak

All Computer Science and Engineering Research

We are living in a society, where images and charts speak louder than words. Therefore, information visualization plays a major role in solving complex problems since it provides a visual summary of data that makes it easier to identify trends and patterns.

In this master project, I propose a web – based visual analytics tool that enables to analyze complex email and time based / event series data. The visual analytics framework uses test data from IEEE VAST Challenge 2014: Mini challenge 1 that concentrated on the disappearance of employees of a fictional GAStech company, but the tool allows users …

Understanding Eye Gaze Patterns In Code Comprehension, Jonathan Saddler

School of Computing: Dissertations, Theses, and Student Research

Program comprehension is a sub-field of software engineering that seeks to understand how developers understand programs. Comprehension acts as a starting point for many software engineering tasks such as bug fixing, refactoring, and feature creation. The dissertation presents a series of empirical studies to understand how developers comprehend software in realistic settings. The unique aspect of this work is the use of eye tracking equipment to gather fine-grained detailed information of what developers look at in software artifacts while they perform realistic tasks in an environment familiar to them, namely a context including both the Integrated Development Environment (Eclipse or …

Small Mode Volume Plasmonic Film-Coupled Nanostar Resonators, Negar Charchi, Ying Li, Margaret Huber, Elyahb Allie Kwizera, Xiaohua Huang, Christos Argyropoulos, Thang Hoang

Department of Electrical and Computer Engineering: Faculty Publications

Confining and controlling light in extreme subwavelength scales are tantalizing tasks. In this work, we report a study of individual plasmonic film-coupled nanostar resonators where polarized plasmonic optical modes are trapped in ultrasmall volumes. Individual gold nanostars, separated from a flat gold film by a thin dielectric spacer layer, exhibit a strong light confinement between the sub-10 nm volume of the nanostar's tips and the film. Through dark field scattering measurements of many individual nanostars, a statistical observation of the scattered spectra is obtained and compared with extensive simulation data to reveal the origins of the resonant peaks. We observe …

Using Taint Analysis And Reinforcement Learning (Tarl) To Repair Autonomous Robot Software, Damian Lyons, Saba Zahra

Faculty Publications

It is important to be able to establish formal performance bounds for autonomous systems. However, formal verification techniques require a model of the environment in which the system operates; a challenge for autonomous systems, especially those expected to operate over longer timescales. This paper describes work in progress to automate the monitor and repair of ROS-based autonomous robot software written for an a-priori partially known and possibly incorrect environment model. A taint analysis method is used to automatically extract the data-flow sequence from input topic to publish topic, and instrument that code. A unique reinforcement learning approximation of MDP utility …

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 …

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 …

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 …

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 …

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 …

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. …

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 …

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 …

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 …

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.

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 …