Computer Engineering Commons^™

A Novel Real-Time Non-Invasive Hemoglobin Level Detection Using Video Images From Smartphone Camera, Golam Mushih Tanimul Ahsan, Md. O. Gani, Md Kamrul Hasan, Sheikh Iqbal Ahamed, William Chu, Mohammad Adibuzzaman, Joshua Field

Electrical and Computer Engineering Faculty Research and Publications

Hemoglobin level detection is necessary for evaluating health condition in the human. In the laboratory setting, it is detected by shining light through a small volume of blood and using a colorimetric electronic particle counting algorithm. This invasive process requires time, blood specimens, laboratory equipment, and facilities. There are also many studies on non-invasive hemoglobin level detection. Existing solutions are expensive and require buying additional devices. In this paper, we present a smartphone-based non-invasive hemoglobin detection method. It uses the video images collected from the fingertip of a person. We hypothesized that there is a significant relation between the fingertip …

Making Software, Making Regions: Labor Market Dualization, Segmentation, And Feminization In Austin, Portland And Seattle, Dillon Mahmoudi

Dissertations and Theses

Through mixed-methods research, this dissertation details the regionally variegated and place-specific software production processes in three second-tier US software regions. I focus on the relationship between different industrial, firm, and worker production configurations and broad-based economic development, prosperity, and inequality. I develop four main empirical findings.

First, I argue for a periodization of software production that tracks with changes in software laboring activity, software technologies, and wage-employment relationships. Through a GIS-based method, I use the IPUMS-USA to extensively measure the amount and type of software labor in industries across the US between 1970 and 2015. I map the uneven geography …

Micro-Contacts Testing Using A Micro-Force Sensor Compatible With Biological Systems, Ronald A. Coutu Jr., Dushyant Tomer

Electrical and Computer Engineering Faculty Research and Publications

This paper presents the performance and reliability testing of microelectromechanical systems (MEMS) switches by using a micro-force sensor which was originally designed/used to conduct mechanical testing of biological cells. MEMS switches are key components for radio frequency (RF) applications due to their extremely low power consumption and small geometries over conventional technologies. However, unstable electrical contact resistance severely degrades the performance and reliability of such micro-switches. Therefore, our focus is to improve the performance and reliability of “cold” switched micro-contacts by using novel contact materials and engineered micro-contact surfaces. The contact metallurgies considered in this work are “similar” thin film …

Selfie-Takers Prefer Left Cheeks: Converging Evidence From The (Extended) Selfiecity Database, Lev Manovich, Vera Ferrari, Nicola Bruno

Publications and Research

According to previous reports, selfie takers in widely different cultural contexts prefer poses showing the left cheek more than the right cheek. This posing bias may be interpreted as evidence for a right-hemispheric specialization for the expression of facial emotions. However, earlier studies analyzed selfie poses as categorized by human raters, which raises methodological issues in relation to the distinction between frontal and three-quarter poses. Here, we provide converging evidence by analyzing the (extended) selfiecity database which includes automatic assessments of head rotation and of emotional expression. We confirm a culture- and sex-independent left-cheek bias and report stronger expression of …

The Use Of Persistent Explorer Artificial Ants To Solve The Car Sequencing Problem, Kieran O'Sullivan

Dissertations

Ant Colony Optimisation is a widely researched meta-heuristic which uses the behaviour and pheromone laying activities of foraging ants to find paths through graphs. Since the early 1990’s this approach has been applied to problems such as the Travelling Salesman Problem, Quadratic Assignment Problem and Car Sequencing Problem to name a few. The ACO is not without its problems it tends to find good local optima and not good global optima. To solve this problem modifications have been made to the original ACO such as the Max Min ant system. Other solutions involve combining it with Evolutionary Algorithms to improve …

Probabilistic Graphical Models Follow Directly From Maximum Entropy, Anh H. Ly, Francisco Zapata, Olac Fuentes, Vladik Kreinovich

Departmental Technical Reports (CS)

Probabilistic graphical models are a very efficient machine learning technique. However, their only known justification is based on heuristic ideas, ideas that do not explain why exactly these models are empirically successful. It is therefore desirable to come up with a theoretical explanation for these models' empirical efficiency. At present, the only such explanation is that these models naturally emerge if we maximize the relative entropy; however, why the relative entropy should be maximized is not clear. In this paper, we show that these models can also be obtained from a more natural -- and well-justified -- idea of maximizing …

Impulse, Matt Schmidt, Lora Berg, Christine Delfanian, Dave Graves, Heidi Kronaizl, Emily Weber, Micayla Standish

Impulse (Jerome J. Lohr College of Engineering Publication)

[Page] 2 Better Equipped
[Page] 4 Flashback Summer Scholars
[Page] 6 Biomedical Engineering
Ryan Mahutga is the third mechanical engineering graduate in five years to receive a prestigious National Science Foundation Graduate Research Fellowship.
Nigerian native John Asiruwa is working with associate professor Stephen Gent on a mechanical engineering project to help Sanford Health build better heart stents.
Electrical engineering graduate Bruce Lutz has selected the biomedical engineering program as a target for his future giving.
[Page] 9 Programs Accredited
[Page] 10 Fast Wheels
[Page] 12 Five Unforgettable Weeks
[Page] 14 Second in the Nation
[Page] 16 Athletes and Engineering …

A Novel Privacy Preserving User Identification Approach For Network Traffic, Nathan Clarke, Fudong Li, Steven Furnell

Research outputs 2014 to 2021

The prevalence of the Internet and cloud-based applications, alongside the technological evolution of smartphones, tablets and smartwatches, has resulted in users relying upon network connectivity more than ever before. This results in an increasingly voluminous footprint with respect to the network traffic that is created as a consequence. For network forensic examiners, this traffic represents a vital source of independent evidence in an environment where anti-forensics is increasingly challenging the validity of computer-based forensics. Performing network forensics today largely focuses upon an analysis based upon the Internet Protocol (IP) address – as this is the only characteristic available. More typically, …

Personalized Microtopic Recommendation On Microblogs, Yang Li, Jing Jiang, Ting Liu, Minghui Qiu, Xiaofei Sun

Research Collection School Of Computing and Information Systems

Microblogging services such as Sina Weibo and Twitter allow users to create tags explicitly indicated by the # symbol. In Sina Weibo, these tags are called microtopics, and in Twitter, they are called hashtags. In Sina Weibo, each microtopic has a designate page and can be directly visited or commented on. Recommending these microtopics to users based on their interests can help users efficiently acquire information. However, it is non-trivial to recommend microtopics to users to satisfy their information needs. In this article, we investigate the task of personalized microtopic recommendation, which exhibits two challenges. First, users usually do not …

Feature Extraction And Parallel Visualization For Large-Scale Scientific Data, Lina Yu

School of Computing: Dissertations, Theses, and Student Research

Advanced computing and sensing technologies enable scientists to study natural and physical phenomena with unprecedented precision, resulting in an explosive growth of data. The unprecedented amounts of data generated from large scientific simulations impose a grand challenge in data analytics and visualization due to the fact that data are too massive for transferring, storing, and processing.

This dissertation makes the first contribution to the design of novel transfer functions and application-aware data replacement policy to facilitate feature classification on highly parallel distributed systems. We design novel transfer functions that advance the classification of continuously changed volume data by combining the …

Sugarmate: Non-Intrusive Blood Glucose Monitoring With Smartphones, Weixi Gu, Yuxun Zhou, Zimu Zhou, Xi Liu, Han Zou, Pei Zhang, Costas J. Spanos, Lin Zhang

Research Collection School Of Computing and Information Systems

Inferring abnormal glucose events such as hyperglycemia and hypoglycemia is crucial for the health of both diabetic patients and non-diabetic people. However, regular blood glucose monitoring can be invasive and inconvenient in everyday life. We present SugarMate, a first smartphone-based blood glucose inference system as a temporary alternative to continuous blood glucose monitors (CGM) when they are uncomfortable or inconvenient to wear. In addition to the records of food, drug and insulin intake, it leverages smartphone sensors to measure physical activities and sleep quality automatically. Provided with the imbalanced and often limited measurements, a challenge of SugarMate is the inference …

Natural Language Processing Based Generator Of Testing Instruments, Qianqian Wang

Electronic Theses, Projects, and Dissertations

Natural Language Processing (NLP) is the field of study that focuses on the interactions between human language and computers. By “natural language” we mean a language that is used for everyday communication by humans. Different from programming languages, natural languages are hard to be defined with accurate rules. NLP is developing rapidly and it has been widely used in different industries. Technologies based on NLP are becoming increasingly widespread, for example, Siri or Alexa are intelligent personal assistants using NLP build in an algorithm to communicate with people. “Natural Language Processing Based Generator of Testing Instruments” is a stand-alone program …

Comparing And Improving Facial Recognition Method, Brandon Luis Sierra

Electronic Theses, Projects, and Dissertations

Facial recognition is the process in which a sample face can be correctly identified by a machine amongst a group of different faces. With the never-ending need for improvement in the fields of security, surveillance, and identification, facial recognition is becoming increasingly important. Considering this importance, it is imperative that the correct faces are recognized and the error rate is as minimal as possible. Despite the wide variety of current methods for facial recognition, there is no clear cut best method. This project reviews and examines three different methods for facial recognition: Eigenfaces, Fisherfaces, and Local Binary Patterns to determine …

Cybercrime: An Investigation Of The Attitudes And Environmental Factors That Make People More Willing To Participate In Online Crime, Dearbhail Kirwan

Dissertations

Cybercrime incidence rates are increasing. In order to identify solutions to this problem, the sources of cybercrime need to be identified. This research attempted to identify a potential set of circumstances that create an environment in which people are more likely to engage in cybercrime. There are three aspects to this; (1) Behaviour on the internet – Are people more likely to engage in illicit activities online than in the physical world? (2) Crime Perceptions – Do people perceive cybercrime as being less serious than non-cybercrime? (3) Resources on the Internet – Are people aware of the types of free …

“How Short Is A Piece Of String?”: An Investigation Into The Impact Of Text Length On Short-Text Classification Accuracy, Austin Mccartney

Dissertations

The recent increase in the widespread use of short messages, for example micro-blogs or SMS communications, has created an opportunity to harvest a vast amount of information through machine-based classification. However, traditional classification methods have failed to produce accuracies comparable to those obtained from similar classification of longer texts. Several approaches have been employed to extend traditional methods to overcome this problem, including the enhancement of the original texts through the construction of associations with external data enrichment sources, ranging from thesauri and semantic nets such as Wordnet, to pre-built online taxonomies such as Wikipedia. Other avenues of investigation have …

Audiosense: Sound-Based Shopper Behavior Analysis System, Amit Sharma, Youngki Lee

Research Collection School Of Computing and Information Systems

This paper presents AudioSense, the system to monitor user-item interactions inside a store hence enabling precisely customized promotions. A shopper's smartwatch emits sound every time the shopper picks up or touches an item inside a store. This sound is then localized, in 2D space, by calculating the angles of arrival captured by multiple microphones deployed on the racks. Lastly, the 2D location is mapped to specific items on the rack based on the rack layout information. In our initial experiments conducted with a single rack with 16 compartments, we could localize the shopper's smartwatch with a median estimation error of …

Can A Strictly Defined Security Configuration For Iot Devices Mitigate The Risk Of Exploitation By Botnet Malware?, David Kennefick

Dissertations

The internet that we know and use every day is the internet of people, a collection of knowledge and data that can be accessed anywhere is the world anytime from many devices. The internet of the future is the Internet of Things. The Internet of Things is a collection of automated technology that is designed to be run autonomously, but on devices designed for humans to use. In 2016 the Mirai malware has shown there are underlying vulnerabilities in devices connected to the internet of things. Mirai is specifically designed to recognise and exploit IoT devices and it has been …

An Analysis Of Predicting Job Titles Using Job Descriptions, John Lynch

Dissertations

A job title is an all-encompassing very short form description that conveys all of the pertinent information relating to a job. The job title typically encapsulates - and should encapsulate - the domain, role and level of responsibility of any given job. Significant value is attached to job titles both internally within organisational structures and to individual job holders. Organisations map out all employees in an organogram on the basis of job titles. This has a bearing on issues like salary, level and scale of responsibility, employee selection and so on. Employees draw value from their own job titles as …

Can Deep Learning Techniques Improve The Risk Adjusted Returns From Enhanced Indexing Investment Strategies, Anthony Grace

Dissertations

Deep learning techniques have been widely applied in the field of stock market prediction particularly with respect to the implementation of active trading strategies. However, the area of portfolio management and passive portfolio management in particular has been much less well served by research to date. This research project conducts an investigation into the science underlying the implementation of portfolio management strategies in practice focusing on enhanced indexing strategies. Enhanced indexing is a passive management approach which introduces an element of active management with the aim of achieving a level of active return through small adjustments to the portfolio weights. …

An Unsupervised Multilingual Approach For Online Social Media Topic Identification, Siaw Ling Lo, Raymond Chiong, David Cornforth

Research Collection School Of Computing and Information Systems

Social media data can be valuable in many ways. However, the vast amount of content shared and the linguistic variants of languages used on social media are making it very challenging for high-value topics to be identified. In this paper, we present an unsupervised multilingual approach for identifying highly relevant terms and topics from the mass of social media data. This approach combines term ranking, localised language analysis, unsupervised topic clustering and multilingual sentiment analysis to extract prominent topics through analysis of Twitter’s tweets from a period of time. It is observed that each of the ranking methods tested has …